sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:1821475 loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:1705.00652 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1821475
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Estimating User Location in Social Media with Stacked Denoising
    Auto-encoders
  sentences:
  - 'Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach'
  - Conventional sphygmomanometers are being replaced by automated devices; can they
    be used to accurately calculate ABPI?Thirty-six volunteers (72 legs) attending
    a vascular clinic had their ankle, brachial blood pressure and ABPIs calculated
    using each of these 3 methods. (1) Conventional aneuroid BP cuff with hand held
    doppler. (2) OMRON HEM 705CP portable automated BP monitor. (3) The hand held
    doppler to determine systolic BP measured by the OMRON.Conventional doppler readings
    for brachial and ankle pressures were generally higher than those obtained digitally
    by less than 3 mmHg but this was not statistically significant. This did not translate
    into a significant difference in ABPIs obtained using all 3 techniques; the correlation
    coefficient of conventional ABPI with automated ABPI (method 2) was 0.746, this
    was improved to 0.899 using method 3. The OMRON failed to detect a signal in 16
    of the 72 legs, 11 of these legs had ABPIs<0.66.
  - Deep neural networks based user interface detection for mobile applications using
    symbol marker
- source_sentence: 'Central mesenteric lymph node BER-Ep4+ cells in colorectal cancer:
    challenge to sentinel node concept?'
  sentences:
  - The Lovely Bones (film) the film had many positive messages about life." The Lovely
    Bones (film) The Lovely Bones is a 2009 supernatural drama film directed by Peter
    Jackson, and starring Mark Wahlberg, Rachel Weisz, Susan Sarandon, Stanley Tucci,
    Michael Imperioli, and Saoirse Ronan. The screenplay by Fran Walsh, Philippa Boyens,
    and Jackson was based on Alice Sebold’s award-winning and bestselling 2002 novel
    of the same name. It follows a girl who is murdered and watches over her family
    from the in-between, and is torn between seeking vengeance on her killer and allowing
    her family to heal. An international co-production between the United States,
  - Postoperative intracranial hematoma (POIH) is a frequent sequela secondary to
    cranial surgery. The role of routine early postoperative computed tomography (CT)
    scanning in the detection of POIH remains controversial. The study was aimed at
    analyzing the effect of routine early CT scanning after craniotomy for the early
    detection of POIH.Routine early postoperative CT scanning was performed at our
    institute, and a retrospective study was conducted to analyze the data. POIH was
    defined as an intracranial hematoma requiring surgical management.A total of 1,148
    patients undergoing craniotomy were included in this study; 28 of these patients
    developed POIH. The majority of POIH cases (15/28, 54 %) were detected during
    the first 6 h following craniotomy. A routine CT scan was performed on all included
    patients but two; however, CT scans detected only 16 POIH cases. During the first
    6 h, the rate at which CT scans detected POIH was 1.9 % (15/786); subsequently,
    the rate decreased to only 0.3 % (1/360; p < 0.05, compared with the rate during
    the first 6 h). Among patients without clinical manifestations, the rate at which
    the routine post-craniotomy CT scan detected POIH was only 0.7 % (5/721) (p < 0.05,
    compared with the incidence of POIH). Finally, among high-risk POIH patients,
    the POIH-positive rate of routine CT scanning was elevated.
  - The role of sentinel lymph nodes in colorectal cancer remains unclear.Cryosections
    from central para-aortic mesenterial lymph nodes were stained using mAb BER-Ep4.
    Overall survival and distant recurrence were calculated using Kaplan-Meier plots.All
    patients (n = 48) were free of distant metastases and curatively resected (R0).
    23 pN0, 13 pN1 and 12 pN2 stages were found. 21/48 patients (44%) showed BER-Ep4+
    cells in their central lymph nodes (7/23 pN0, 8/13 pN1, 6/12 pN2). In 6/23 pN0
    patients, BER-Ep4+ cells were also found in locoregional nodes (p = 0.03, Fisher's
    exact test). pN status predicted overall survival (p = 0.006, Kaplan-Meier curve,
    log-rank test). An impact was exerted by central mesenteric BER-Ep4+ cells on
    overall survival (p = 0.009 in pN0 patients, p = 0.07 for all pN) and distant
    recurrence-free survival (p = 0.001 in pN0 patients, p = 0.007 for all pN). Multivariate
    analysis showed an independent prognostic effect on overall survival in pN0 patients
    (p = 0.022).
- source_sentence: when did the samsung galaxy s8 come out
  sentences:
  - Samsung Galaxy S8 support for Daydream. The Galaxy S8 was one of the first Android
    phones to support ARCore, Google's augmented reality engine. In February 2018,
    the official Android 8.0 Oreo update began rolling out to the Samsung Galaxy S8,
    Samsung Galaxy S8+, and Samsung Galaxy S8 Active. Besides the phone's protective
    case reportedly cracking and peeling away in under 2 months of use, Dan Seifert
    of "The Verge" praised the design of the Galaxy S8, describing it as a "stunning
    device to look at and hold" that was "refined and polished to a literal shine",
    and adding that it "truly doesn't look
  - British Raj British Raj The British Raj (; from "rāj", literally, "rule" in Hindustani)
    was the rule by the British Crown in the Indian subcontinent between 1858 and
    1947. The rule is also called Crown rule in India, or direct rule in India. The
    region under British control was commonly called British India or simply India
    in contemporaneous usage, and included areas directly administered by the United
    Kingdom, which were collectively called British India, and those ruled by indigenous
    rulers, but under British tutelage or paramountcy, and called the princely states.
    The whole was also informally called the Indian Empire. As India,
  - Samsung Galaxy S8 Samsung Galaxy S8 The Samsung Galaxy S8, Samsung Galaxy S8+
    (shortened to S8 and S8+, respectively) and Samsung Galaxy S8 Active are Android
    smartphones (with the S8+ being the phablet smartphone) produced by Samsung Electronics
    as the eighth generation of the Samsung Galaxy S series. The S8 and S8+ were unveiled
    on 29 March 2017 and directly succeeded the Samsung Galaxy S7 and S7 edge, with
    a North American release on 21 April 2017 and international rollout throughout
    April and May. The S8 Active was announced on 8 August 2017 and is exclusive to
    certain U.S. cellular carriers. The S8
- source_sentence: Can Carrier-Mediated Delivery System Promote the Development of
    Antisense Imaging?
  sentences:
  - 8-track tape month of the vinyl release. The eight-track format became by far
    the most popular and offered the largest music library of all the tape systems.
    Eight-track players were fitted as standard equipment in most Rolls-Royce and
    Bentley cars of the period for sale in Great Britain and worldwide. Optional 8-track
    players were available in many cars and trucks through the early 1980s. Ampex,
    based in Elk Grove Village, Illinois, set up a European operation (Ampex Stereo
    Tapes) in London, England, in 1970 under general manager Gerry Hall, with manufacturing
    in Nivelles, Belgium, to promote 8-track product (as well as musicassettes)
  - Heterotopic heart transplantation (HHTx) is a therapeutic option in heart failure
    patients with fixed elevated pulmonary hypertension. However, survival is poorer
    in HHTx recipients, and with improving results in continuous flow ventricular
    assist devices (VADs), many patients can be bridged to allow normalization of
    pulmonary artery pressures, making them orthotopic heart transplant (OHTx) candidates.
    Thus, the aim of this study was to analyse the survival of our HHTx cohort and
    compare them with our VAD bridge patients.A retrospective review of 342 heart
    transplant patients (315 OHTx and 27 HHTx) performed at our institution over 15
    years was compared with 124 bridge-to-transplant VAD patients over the same time
    period, of whom 69 received an OHTx. Pulmonary artery pressures before and after
    VAD implant were analysed. Survival was analysed using both univariate and multivariate
    analyses.HHTx recipients were significantly older, and the donor allografts were
    older, smaller and had longer ischaemic times than the OHTx cohort. Comparison
    of the VAD types implanted (pulsatile vs continuous) showed significantly longer
    time supported on the continuous devices with significantly fewer deaths than
    the pulsatile devices. The continuous devices were successful in reducing pulmonary
    artery pressures pretransplant. The HHTx cohort had a significantly poorer survival
    than the OHTx cohort (P=0.002). Survival on a continuous device and then OHTx
    was significantly better than either HHTx or pulsatile device support.
  - We aimed to explore the feasibility of transfection methods for antisense imaging.Antisense
    oligonucleotides (ASON) targeted to the mRNA of hTERT gene were synthesized and
    labeled with Technetium-99m and fluorescein isothiocyanate (FITC), respectively.
    Then, ASON was combined with transfection reagent Lipofectamine 2000 and Xfect(TM),
    named Lipo-ASON and Xfect-ASON, respectively. After transfection, the labeled
    ASON was characterized in hNPCs-G3 and hRPE cells. Reverse transcription polymerase
    chain reaction (RT-PCR) and Western blotting were performed to assay the hTERT
    mRNA and protein levels after hNPCs-G3 cells were incubated with Lipo-ASON, Xfect-ASON,
    and naked ASON. In addition, Lipo-ASON, Xfect-ASON, and naked ASON were injected
    into tumor-bearing mice, and the biodistribution in vivo was performed.The presence
    of two transfection reagents significantly increased intracellular uptake of radiolabeled
    ASON in both cell lines compared with naked ASON (p < 0.05). However, there was
    no significant difference in cellular uptake rates of Lipo-ASON and Xfect-ASON
    between hNPCs-G3 and hRPE cells. In comparison with naked ASON, the fluorescence
    intensity was strongly enhanced after binding to transfection reagents. Furthermore,
    the levels of hTERT mRNA and protein were significantly reduced in cells treated
    with Lipo-ASON and Xfect-ASON (p < 0.05), but naked ASON had no significant effect
    on hTERT expression level. The biodistribution study indicated that tumor radioactivity
    uptake of radiolabeled ASON for naked ASON, Lipo-ASON, and Xfect-ASON group was
    low and shown no significant difference in vivo.
- source_sentence: Does early second-trimester sonography predict adverse perinatal
    outcomes in monochorionic diamniotic twin pregnancies?
  sentences:
  - Calcium and vitamin D are essential nutrients for bone metabolism Vitamin D can
    either be obtained from dietary sources or cutaneous synthesis. The study was
    conducted in subtropic weather; therefore, some might believe that the levels
    of solar radiation would be sufficient in this area.To evaluate calcium and vitamin
    D supplementation in postmenopausal women with osteoporosis living in a sunny
    country.A 3-month controlled clinical trial with 64 postmenopausal women with
    osteoporosis, mean age 62 + or - 8 years. They were randomly assigned to either
    the supplement group, who received 1,200 mg of calcium carbonate and 400 IU (10
    microg) of vitamin D(3,) or the control group. Dietary intake assessment was performed,
    bone mineral density and body composition were measured, and biochemical markers
    of bone metabolism were analyzed.Considering all participants at baseline, serum
    vitamin D was under 75 nmol/l in 91.4% of the participants. The concentration
    of serum 25(OH)D increased significantly (p = 0.023) after 3 months of supplementation
    from 46.67 + or - 13.97 to 59.47 + or - 17.50 nmol/l. However, the dose given
    was limited in effect, and 86.2% of the supplement group did not reach optimal
    levels of 25(OH)D. Parathyroid hormone was elevated in 22.4% of the study group.
    After the intervention period, mean parathyroid hormone tended to decrease in
    the supplement group (p = 0.063).
  - 'To determine whether intertwin discordant abdominal circumference, femur length,
    head circumference, and estimated fetal weight sonographic measurements in early
    second-trimester monochorionic diamniotic twins predict adverse obstetric and
    neonatal outcomes.We conducted a multicenter retrospective cohort study involving
    9 regional perinatal centers in the United States. We examined the records of
    all monochorionic diamniotic twin pregnancies with two live fetuses at the 16-
    to 18-week sonographic examination who had serial follow-up sonography until delivery.
    The intertwin discordance in abdominal circumference, femur length, head circumference,
    and estimated fetal weight was calculated as the difference between the two fetuses,
    expressed as a percentage of the larger using the 16- to 18-week sonographic measurements.
    An adverse composite obstetric outcome was defined as the occurrence of 1 or more
    of the following in either fetus: intrauterine growth restriction, twin-twin transfusion
    syndrome, intrauterine fetal death, abnormal growth discordance (≥20% difference),
    and very preterm birth at or before 28 weeks. An adverse composite neonatal outcome
    was defined as the occurrence of 1 or more of the following: respiratory distress
    syndrome, any stage of intraventricular hemorrhage, 5-minute Apgar score less
    than 7, necrotizing enterocolitis, culture-proven early-onset sepsis, and neonatal
    death. Receiver operating characteristic and logistic regression-with-generalized
    estimating equation analyses were constructed.Among the 177 monochorionic diamniotic
    twin pregnancies analyzed, intertwin abdominal circumference and estimated fetal
    weight discordances were only predictive of adverse composite obstetric outcomes
    (areas under the curve, 79% and 80%, respectively). Receiver operating characteristic
    curves showed that intertwin discordances in abdominal circumference, femur length,
    head circumference, and estimated fetal weight were not acceptable predictors
    of twin-twin transfusion syndrome or adverse neonatal outcomes.'
  - We aimed to investigate our results of carotid endarterectomy operations in symptomatic
    patients operated by using an intraluminal shunt and without use of an intraluminal
    shunt in patients with contralateral carotid artery stenosis.We reviewed the results
    of 144 carotid endarterectomy operations in patients with contralateral carotid
    artery stenosis from January 2007 to December 2012. These patients were allocated
    in 2 groups. Group 1 (n = 70) consisted of the patients operated by using an intraluminal
    shunt and Group 2 (n = 74) consisted of the patients operated without use of an
    intraluminal shunt. Postoperative neurologic complications were recorded.Temporary
    neurologic impairment developed in 3 (4.3%) patients postoperatively in group
    1 and in 2 (2.7%) patients postoperatively in group 2. This difference was not
    statistically significant between groups (p = 0.675). None of the patients returned
    to operation theatre due to excessive bleeding postoperatively. The stroke/death
    rate was 0.7% in the study group.
model-index:
- name: all-MiniLM-L6-v2 trained on MEDI-MTEB triplets
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: medi mteb dev
      type: medi-mteb-dev
    metrics:
    - type: cosine_accuracy
      value: 0.9152662981006076
      name: Cosine Accuracy
---

# all-MiniLM-L6-v2 trained on MEDI-MTEB triplets

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the NQ, pubmed, specter_train_triples, S2ORC_citations_abstracts, fever, gooaq_pairs, codesearchnet, wikihow, WikiAnswers, eli5_question_answer, amazon-qa, medmcqa, zeroshot, TriviaQA_pairs, PAQ_pairs, stackexchange_duplicate_questions_title-body_title-body, trex, flickr30k_captions, hotpotqa, task671_ambigqa_text_generation, task061_ropes_answer_generation, task285_imdb_answer_generation, task905_hate_speech_offensive_classification, task566_circa_classification, task184_snli_entailment_to_neutral_text_modification, task280_stereoset_classification_stereotype_type, task1599_smcalflow_classification, task1384_deal_or_no_dialog_classification, task591_sciq_answer_generation, task823_peixian-rtgender_sentiment_analysis, task023_cosmosqa_question_generation, task900_freebase_qa_category_classification, task924_event2mind_word_generation, task152_tomqa_find_location_easy_noise, task1368_healthfact_sentence_generation, task1661_super_glue_classification, task1187_politifact_classification, task1728_web_nlg_data_to_text, task112_asset_simple_sentence_identification, task1340_msr_text_compression_compression, task072_abductivenli_answer_generation, task1504_hatexplain_answer_generation, task684_online_privacy_policy_text_information_type_generation, task1290_xsum_summarization, task075_squad1.1_answer_generation, task1587_scifact_classification, task384_socialiqa_question_classification, task1555_scitail_answer_generation, task1532_daily_dialog_emotion_classification, task239_tweetqa_answer_generation, task596_mocha_question_generation, task1411_dart_subject_identification, task1359_numer_sense_answer_generation, task329_gap_classification, task220_rocstories_title_classification, task316_crows-pairs_classification_stereotype, task495_semeval_headline_classification, task1168_brown_coarse_pos_tagging, task348_squad2.0_unanswerable_question_generation, task049_multirc_questions_needed_to_answer, task1534_daily_dialog_question_classification, task322_jigsaw_classification_threat, task295_semeval_2020_task4_commonsense_reasoning, task186_snli_contradiction_to_entailment_text_modification, task034_winogrande_question_modification_object, task160_replace_letter_in_a_sentence, task469_mrqa_answer_generation, task105_story_cloze-rocstories_sentence_generation, task649_race_blank_question_generation, task1536_daily_dialog_happiness_classification, task683_online_privacy_policy_text_purpose_answer_generation, task024_cosmosqa_answer_generation, task584_udeps_eng_fine_pos_tagging, task066_timetravel_binary_consistency_classification, task413_mickey_en_sentence_perturbation_generation, task182_duorc_question_generation, task028_drop_answer_generation, task1601_webquestions_answer_generation, task1295_adversarial_qa_question_answering, task201_mnli_neutral_classification, task038_qasc_combined_fact, task293_storycommonsense_emotion_text_generation, task572_recipe_nlg_text_generation, task517_emo_classify_emotion_of_dialogue, task382_hybridqa_answer_generation, task176_break_decompose_questions, task1291_multi_news_summarization, task155_count_nouns_verbs, task031_winogrande_question_generation_object, task279_stereoset_classification_stereotype, task1336_peixian_equity_evaluation_corpus_gender_classifier, task508_scruples_dilemmas_more_ethical_isidentifiable, task518_emo_different_dialogue_emotions, task077_splash_explanation_to_sql, task923_event2mind_classifier, task470_mrqa_question_generation, task638_multi_woz_classification, task1412_web_questions_question_answering, task847_pubmedqa_question_generation, task678_ollie_actual_relationship_answer_generation, task290_tellmewhy_question_answerability, task575_air_dialogue_classification, task189_snli_neutral_to_contradiction_text_modification, task026_drop_question_generation, task162_count_words_starting_with_letter, task079_conala_concat_strings, task610_conllpp_ner, task046_miscellaneous_question_typing, task197_mnli_domain_answer_generation, task1325_qa_zre_question_generation_on_subject_relation, task430_senteval_subject_count, task672_nummersense, task402_grailqa_paraphrase_generation, task904_hate_speech_offensive_classification, task192_hotpotqa_sentence_generation, task069_abductivenli_classification, task574_air_dialogue_sentence_generation, task187_snli_entailment_to_contradiction_text_modification, task749_glucose_reverse_cause_emotion_detection, task1552_scitail_question_generation, task750_aqua_multiple_choice_answering, task327_jigsaw_classification_toxic, task1502_hatexplain_classification, task328_jigsaw_classification_insult, task304_numeric_fused_head_resolution, task1293_kilt_tasks_hotpotqa_question_answering, task216_rocstories_correct_answer_generation, task1326_qa_zre_question_generation_from_answer, task1338_peixian_equity_evaluation_corpus_sentiment_classifier, task1729_personachat_generate_next, task1202_atomic_classification_xneed, task400_paws_paraphrase_classification, task502_scruples_anecdotes_whoiswrong_verification, task088_identify_typo_verification, task221_rocstories_two_choice_classification, task200_mnli_entailment_classification, task074_squad1.1_question_generation, task581_socialiqa_question_generation, task1186_nne_hrngo_classification, task898_freebase_qa_answer_generation, task1408_dart_similarity_classification, task168_strategyqa_question_decomposition, task1357_xlsum_summary_generation, task390_torque_text_span_selection, task165_mcscript_question_answering_commonsense, task1533_daily_dialog_formal_classification, task002_quoref_answer_generation, task1297_qasc_question_answering, task305_jeopardy_answer_generation_normal, task029_winogrande_full_object, task1327_qa_zre_answer_generation_from_question, task326_jigsaw_classification_obscene, task1542_every_ith_element_from_starting, task570_recipe_nlg_ner_generation, task1409_dart_text_generation, task401_numeric_fused_head_reference, task846_pubmedqa_classification, task1712_poki_classification, task344_hybridqa_answer_generation, task875_emotion_classification, task1214_atomic_classification_xwant, task106_scruples_ethical_judgment, task238_iirc_answer_from_passage_answer_generation, task1391_winogrande_easy_answer_generation, task195_sentiment140_classification, task163_count_words_ending_with_letter, task579_socialiqa_classification, task569_recipe_nlg_text_generation, task1602_webquestion_question_genreation, task747_glucose_cause_emotion_detection, task219_rocstories_title_answer_generation, task178_quartz_question_answering, task103_facts2story_long_text_generation, task301_record_question_generation, task1369_healthfact_sentence_generation, task515_senteval_odd_word_out, task496_semeval_answer_generation, task1658_billsum_summarization, task1204_atomic_classification_hinderedby, task1392_superglue_multirc_answer_verification, task306_jeopardy_answer_generation_double, task1286_openbookqa_question_answering, task159_check_frequency_of_words_in_sentence_pair, task151_tomqa_find_location_easy_clean, task323_jigsaw_classification_sexually_explicit, task037_qasc_generate_related_fact, task027_drop_answer_type_generation, task1596_event2mind_text_generation_2, task141_odd-man-out_classification_category, task194_duorc_answer_generation, task679_hope_edi_english_text_classification, task246_dream_question_generation, task1195_disflqa_disfluent_to_fluent_conversion, task065_timetravel_consistent_sentence_classification, task351_winomt_classification_gender_identifiability_anti, task580_socialiqa_answer_generation, task583_udeps_eng_coarse_pos_tagging, task202_mnli_contradiction_classification, task222_rocstories_two_chioce_slotting_classification, task498_scruples_anecdotes_whoiswrong_classification, task067_abductivenli_answer_generation, task616_cola_classification, task286_olid_offense_judgment, task188_snli_neutral_to_entailment_text_modification, task223_quartz_explanation_generation, task820_protoqa_answer_generation, task196_sentiment140_answer_generation, task1678_mathqa_answer_selection, task349_squad2.0_answerable_unanswerable_question_classification, task154_tomqa_find_location_hard_noise, task333_hateeval_classification_hate_en, task235_iirc_question_from_subtext_answer_generation, task1554_scitail_classification, task210_logic2text_structured_text_generation, task035_winogrande_question_modification_person, task230_iirc_passage_classification, task1356_xlsum_title_generation, task1726_mathqa_correct_answer_generation, task302_record_classification, task380_boolq_yes_no_question, task212_logic2text_classification, task748_glucose_reverse_cause_event_detection, task834_mathdataset_classification, task350_winomt_classification_gender_identifiability_pro, task191_hotpotqa_question_generation, task236_iirc_question_from_passage_answer_generation, task217_rocstories_ordering_answer_generation, task568_circa_question_generation, task614_glucose_cause_event_detection, task361_spolin_yesand_prompt_response_classification, task421_persent_sentence_sentiment_classification, task203_mnli_sentence_generation, task420_persent_document_sentiment_classification, task153_tomqa_find_location_hard_clean, task346_hybridqa_classification, task1211_atomic_classification_hassubevent, task360_spolin_yesand_response_generation, task510_reddit_tifu_title_summarization, task511_reddit_tifu_long_text_summarization, task345_hybridqa_answer_generation, task270_csrg_counterfactual_context_generation, task307_jeopardy_answer_generation_final, task001_quoref_question_generation, task089_swap_words_verification, task1196_atomic_classification_oeffect, task080_piqa_answer_generation, task1598_nyc_long_text_generation, task240_tweetqa_question_generation, task615_moviesqa_answer_generation, task1347_glue_sts-b_similarity_classification, task114_is_the_given_word_longest, task292_storycommonsense_character_text_generation, task115_help_advice_classification, task431_senteval_object_count, task1360_numer_sense_multiple_choice_qa_generation, task177_para-nmt_paraphrasing, task132_dais_text_modification, task269_csrg_counterfactual_story_generation, task233_iirc_link_exists_classification, task161_count_words_containing_letter, task1205_atomic_classification_isafter, task571_recipe_nlg_ner_generation, task1292_yelp_review_full_text_categorization, task428_senteval_inversion, task311_race_question_generation, task429_senteval_tense, task403_creak_commonsense_inference, task929_products_reviews_classification, task582_naturalquestion_answer_generation, task237_iirc_answer_from_subtext_answer_generation, task050_multirc_answerability, task184_break_generate_question, task669_ambigqa_answer_generation, task169_strategyqa_sentence_generation, task500_scruples_anecdotes_title_generation, task241_tweetqa_classification, task1345_glue_qqp_question_paraprashing, task218_rocstories_swap_order_answer_generation, task613_politifact_text_generation, task1167_penn_treebank_coarse_pos_tagging, task1422_mathqa_physics, task247_dream_answer_generation, task199_mnli_classification, task164_mcscript_question_answering_text, task1541_agnews_classification, task516_senteval_conjoints_inversion, task294_storycommonsense_motiv_text_generation, task501_scruples_anecdotes_post_type_verification, task213_rocstories_correct_ending_classification, task821_protoqa_question_generation, task493_review_polarity_classification, task308_jeopardy_answer_generation_all, task1595_event2mind_text_generation_1, task040_qasc_question_generation, task231_iirc_link_classification, task1727_wiqa_what_is_the_effect, task578_curiosity_dialogs_answer_generation, task310_race_classification, task309_race_answer_generation, task379_agnews_topic_classification, task030_winogrande_full_person, task1540_parsed_pdfs_summarization, task039_qasc_find_overlapping_words, task1206_atomic_classification_isbefore, task157_count_vowels_and_consonants, task339_record_answer_generation, task453_swag_answer_generation, task848_pubmedqa_classification, task673_google_wellformed_query_classification, task676_ollie_relationship_answer_generation, task268_casehold_legal_answer_generation, task844_financial_phrasebank_classification, task330_gap_answer_generation, task595_mocha_answer_generation, task1285_kpa_keypoint_matching, task234_iirc_passage_line_answer_generation, task494_review_polarity_answer_generation, task670_ambigqa_question_generation, task289_gigaword_summarization, npr, nli, SimpleWiki, amazon_review_2018, ccnews_title_text, agnews, xsum, msmarco, yahoo_answers_title_answer, squad_pairs, wow, mteb-amazon_counterfactual-avs_triplets, mteb-amazon_massive_intent-avs_triplets, mteb-amazon_massive_scenario-avs_triplets, mteb-amazon_reviews_multi-avs_triplets, mteb-banking77-avs_triplets, mteb-emotion-avs_triplets, mteb-imdb-avs_triplets, mteb-mtop_domain-avs_triplets, mteb-mtop_intent-avs_triplets, mteb-toxic_conversations_50k-avs_triplets, mteb-tweet_sentiment_extraction-avs_triplets and covid-bing-query-gpt4-avs_triplets datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
    - NQ
    - pubmed
    - specter_train_triples
    - S2ORC_citations_abstracts
    - fever
    - gooaq_pairs
    - codesearchnet
    - wikihow
    - WikiAnswers
    - eli5_question_answer
    - amazon-qa
    - medmcqa
    - zeroshot
    - TriviaQA_pairs
    - PAQ_pairs
    - stackexchange_duplicate_questions_title-body_title-body
    - trex
    - flickr30k_captions
    - hotpotqa
    - task671_ambigqa_text_generation
    - task061_ropes_answer_generation
    - task285_imdb_answer_generation
    - task905_hate_speech_offensive_classification
    - task566_circa_classification
    - task184_snli_entailment_to_neutral_text_modification
    - task280_stereoset_classification_stereotype_type
    - task1599_smcalflow_classification
    - task1384_deal_or_no_dialog_classification
    - task591_sciq_answer_generation
    - task823_peixian-rtgender_sentiment_analysis
    - task023_cosmosqa_question_generation
    - task900_freebase_qa_category_classification
    - task924_event2mind_word_generation
    - task152_tomqa_find_location_easy_noise
    - task1368_healthfact_sentence_generation
    - task1661_super_glue_classification
    - task1187_politifact_classification
    - task1728_web_nlg_data_to_text
    - task112_asset_simple_sentence_identification
    - task1340_msr_text_compression_compression
    - task072_abductivenli_answer_generation
    - task1504_hatexplain_answer_generation
    - task684_online_privacy_policy_text_information_type_generation
    - task1290_xsum_summarization
    - task075_squad1.1_answer_generation
    - task1587_scifact_classification
    - task384_socialiqa_question_classification
    - task1555_scitail_answer_generation
    - task1532_daily_dialog_emotion_classification
    - task239_tweetqa_answer_generation
    - task596_mocha_question_generation
    - task1411_dart_subject_identification
    - task1359_numer_sense_answer_generation
    - task329_gap_classification
    - task220_rocstories_title_classification
    - task316_crows-pairs_classification_stereotype
    - task495_semeval_headline_classification
    - task1168_brown_coarse_pos_tagging
    - task348_squad2.0_unanswerable_question_generation
    - task049_multirc_questions_needed_to_answer
    - task1534_daily_dialog_question_classification
    - task322_jigsaw_classification_threat
    - task295_semeval_2020_task4_commonsense_reasoning
    - task186_snli_contradiction_to_entailment_text_modification
    - task034_winogrande_question_modification_object
    - task160_replace_letter_in_a_sentence
    - task469_mrqa_answer_generation
    - task105_story_cloze-rocstories_sentence_generation
    - task649_race_blank_question_generation
    - task1536_daily_dialog_happiness_classification
    - task683_online_privacy_policy_text_purpose_answer_generation
    - task024_cosmosqa_answer_generation
    - task584_udeps_eng_fine_pos_tagging
    - task066_timetravel_binary_consistency_classification
    - task413_mickey_en_sentence_perturbation_generation
    - task182_duorc_question_generation
    - task028_drop_answer_generation
    - task1601_webquestions_answer_generation
    - task1295_adversarial_qa_question_answering
    - task201_mnli_neutral_classification
    - task038_qasc_combined_fact
    - task293_storycommonsense_emotion_text_generation
    - task572_recipe_nlg_text_generation
    - task517_emo_classify_emotion_of_dialogue
    - task382_hybridqa_answer_generation
    - task176_break_decompose_questions
    - task1291_multi_news_summarization
    - task155_count_nouns_verbs
    - task031_winogrande_question_generation_object
    - task279_stereoset_classification_stereotype
    - task1336_peixian_equity_evaluation_corpus_gender_classifier
    - task508_scruples_dilemmas_more_ethical_isidentifiable
    - task518_emo_different_dialogue_emotions
    - task077_splash_explanation_to_sql
    - task923_event2mind_classifier
    - task470_mrqa_question_generation
    - task638_multi_woz_classification
    - task1412_web_questions_question_answering
    - task847_pubmedqa_question_generation
    - task678_ollie_actual_relationship_answer_generation
    - task290_tellmewhy_question_answerability
    - task575_air_dialogue_classification
    - task189_snli_neutral_to_contradiction_text_modification
    - task026_drop_question_generation
    - task162_count_words_starting_with_letter
    - task079_conala_concat_strings
    - task610_conllpp_ner
    - task046_miscellaneous_question_typing
    - task197_mnli_domain_answer_generation
    - task1325_qa_zre_question_generation_on_subject_relation
    - task430_senteval_subject_count
    - task672_nummersense
    - task402_grailqa_paraphrase_generation
    - task904_hate_speech_offensive_classification
    - task192_hotpotqa_sentence_generation
    - task069_abductivenli_classification
    - task574_air_dialogue_sentence_generation
    - task187_snli_entailment_to_contradiction_text_modification
    - task749_glucose_reverse_cause_emotion_detection
    - task1552_scitail_question_generation
    - task750_aqua_multiple_choice_answering
    - task327_jigsaw_classification_toxic
    - task1502_hatexplain_classification
    - task328_jigsaw_classification_insult
    - task304_numeric_fused_head_resolution
    - task1293_kilt_tasks_hotpotqa_question_answering
    - task216_rocstories_correct_answer_generation
    - task1326_qa_zre_question_generation_from_answer
    - task1338_peixian_equity_evaluation_corpus_sentiment_classifier
    - task1729_personachat_generate_next
    - task1202_atomic_classification_xneed
    - task400_paws_paraphrase_classification
    - task502_scruples_anecdotes_whoiswrong_verification
    - task088_identify_typo_verification
    - task221_rocstories_two_choice_classification
    - task200_mnli_entailment_classification
    - task074_squad1.1_question_generation
    - task581_socialiqa_question_generation
    - task1186_nne_hrngo_classification
    - task898_freebase_qa_answer_generation
    - task1408_dart_similarity_classification
    - task168_strategyqa_question_decomposition
    - task1357_xlsum_summary_generation
    - task390_torque_text_span_selection
    - task165_mcscript_question_answering_commonsense
    - task1533_daily_dialog_formal_classification
    - task002_quoref_answer_generation
    - task1297_qasc_question_answering
    - task305_jeopardy_answer_generation_normal
    - task029_winogrande_full_object
    - task1327_qa_zre_answer_generation_from_question
    - task326_jigsaw_classification_obscene
    - task1542_every_ith_element_from_starting
    - task570_recipe_nlg_ner_generation
    - task1409_dart_text_generation
    - task401_numeric_fused_head_reference
    - task846_pubmedqa_classification
    - task1712_poki_classification
    - task344_hybridqa_answer_generation
    - task875_emotion_classification
    - task1214_atomic_classification_xwant
    - task106_scruples_ethical_judgment
    - task238_iirc_answer_from_passage_answer_generation
    - task1391_winogrande_easy_answer_generation
    - task195_sentiment140_classification
    - task163_count_words_ending_with_letter
    - task579_socialiqa_classification
    - task569_recipe_nlg_text_generation
    - task1602_webquestion_question_genreation
    - task747_glucose_cause_emotion_detection
    - task219_rocstories_title_answer_generation
    - task178_quartz_question_answering
    - task103_facts2story_long_text_generation
    - task301_record_question_generation
    - task1369_healthfact_sentence_generation
    - task515_senteval_odd_word_out
    - task496_semeval_answer_generation
    - task1658_billsum_summarization
    - task1204_atomic_classification_hinderedby
    - task1392_superglue_multirc_answer_verification
    - task306_jeopardy_answer_generation_double
    - task1286_openbookqa_question_answering
    - task159_check_frequency_of_words_in_sentence_pair
    - task151_tomqa_find_location_easy_clean
    - task323_jigsaw_classification_sexually_explicit
    - task037_qasc_generate_related_fact
    - task027_drop_answer_type_generation
    - task1596_event2mind_text_generation_2
    - task141_odd-man-out_classification_category
    - task194_duorc_answer_generation
    - task679_hope_edi_english_text_classification
    - task246_dream_question_generation
    - task1195_disflqa_disfluent_to_fluent_conversion
    - task065_timetravel_consistent_sentence_classification
    - task351_winomt_classification_gender_identifiability_anti
    - task580_socialiqa_answer_generation
    - task583_udeps_eng_coarse_pos_tagging
    - task202_mnli_contradiction_classification
    - task222_rocstories_two_chioce_slotting_classification
    - task498_scruples_anecdotes_whoiswrong_classification
    - task067_abductivenli_answer_generation
    - task616_cola_classification
    - task286_olid_offense_judgment
    - task188_snli_neutral_to_entailment_text_modification
    - task223_quartz_explanation_generation
    - task820_protoqa_answer_generation
    - task196_sentiment140_answer_generation
    - task1678_mathqa_answer_selection
    - task349_squad2.0_answerable_unanswerable_question_classification
    - task154_tomqa_find_location_hard_noise
    - task333_hateeval_classification_hate_en
    - task235_iirc_question_from_subtext_answer_generation
    - task1554_scitail_classification
    - task210_logic2text_structured_text_generation
    - task035_winogrande_question_modification_person
    - task230_iirc_passage_classification
    - task1356_xlsum_title_generation
    - task1726_mathqa_correct_answer_generation
    - task302_record_classification
    - task380_boolq_yes_no_question
    - task212_logic2text_classification
    - task748_glucose_reverse_cause_event_detection
    - task834_mathdataset_classification
    - task350_winomt_classification_gender_identifiability_pro
    - task191_hotpotqa_question_generation
    - task236_iirc_question_from_passage_answer_generation
    - task217_rocstories_ordering_answer_generation
    - task568_circa_question_generation
    - task614_glucose_cause_event_detection
    - task361_spolin_yesand_prompt_response_classification
    - task421_persent_sentence_sentiment_classification
    - task203_mnli_sentence_generation
    - task420_persent_document_sentiment_classification
    - task153_tomqa_find_location_hard_clean
    - task346_hybridqa_classification
    - task1211_atomic_classification_hassubevent
    - task360_spolin_yesand_response_generation
    - task510_reddit_tifu_title_summarization
    - task511_reddit_tifu_long_text_summarization
    - task345_hybridqa_answer_generation
    - task270_csrg_counterfactual_context_generation
    - task307_jeopardy_answer_generation_final
    - task001_quoref_question_generation
    - task089_swap_words_verification
    - task1196_atomic_classification_oeffect
    - task080_piqa_answer_generation
    - task1598_nyc_long_text_generation
    - task240_tweetqa_question_generation
    - task615_moviesqa_answer_generation
    - task1347_glue_sts-b_similarity_classification
    - task114_is_the_given_word_longest
    - task292_storycommonsense_character_text_generation
    - task115_help_advice_classification
    - task431_senteval_object_count
    - task1360_numer_sense_multiple_choice_qa_generation
    - task177_para-nmt_paraphrasing
    - task132_dais_text_modification
    - task269_csrg_counterfactual_story_generation
    - task233_iirc_link_exists_classification
    - task161_count_words_containing_letter
    - task1205_atomic_classification_isafter
    - task571_recipe_nlg_ner_generation
    - task1292_yelp_review_full_text_categorization
    - task428_senteval_inversion
    - task311_race_question_generation
    - task429_senteval_tense
    - task403_creak_commonsense_inference
    - task929_products_reviews_classification
    - task582_naturalquestion_answer_generation
    - task237_iirc_answer_from_subtext_answer_generation
    - task050_multirc_answerability
    - task184_break_generate_question
    - task669_ambigqa_answer_generation
    - task169_strategyqa_sentence_generation
    - task500_scruples_anecdotes_title_generation
    - task241_tweetqa_classification
    - task1345_glue_qqp_question_paraprashing
    - task218_rocstories_swap_order_answer_generation
    - task613_politifact_text_generation
    - task1167_penn_treebank_coarse_pos_tagging
    - task1422_mathqa_physics
    - task247_dream_answer_generation
    - task199_mnli_classification
    - task164_mcscript_question_answering_text
    - task1541_agnews_classification
    - task516_senteval_conjoints_inversion
    - task294_storycommonsense_motiv_text_generation
    - task501_scruples_anecdotes_post_type_verification
    - task213_rocstories_correct_ending_classification
    - task821_protoqa_question_generation
    - task493_review_polarity_classification
    - task308_jeopardy_answer_generation_all
    - task1595_event2mind_text_generation_1
    - task040_qasc_question_generation
    - task231_iirc_link_classification
    - task1727_wiqa_what_is_the_effect
    - task578_curiosity_dialogs_answer_generation
    - task310_race_classification
    - task309_race_answer_generation
    - task379_agnews_topic_classification
    - task030_winogrande_full_person
    - task1540_parsed_pdfs_summarization
    - task039_qasc_find_overlapping_words
    - task1206_atomic_classification_isbefore
    - task157_count_vowels_and_consonants
    - task339_record_answer_generation
    - task453_swag_answer_generation
    - task848_pubmedqa_classification
    - task673_google_wellformed_query_classification
    - task676_ollie_relationship_answer_generation
    - task268_casehold_legal_answer_generation
    - task844_financial_phrasebank_classification
    - task330_gap_answer_generation
    - task595_mocha_answer_generation
    - task1285_kpa_keypoint_matching
    - task234_iirc_passage_line_answer_generation
    - task494_review_polarity_answer_generation
    - task670_ambigqa_question_generation
    - task289_gigaword_summarization
    - npr
    - nli
    - SimpleWiki
    - amazon_review_2018
    - ccnews_title_text
    - agnews
    - xsum
    - msmarco
    - yahoo_answers_title_answer
    - squad_pairs
    - wow
    - mteb-amazon_counterfactual-avs_triplets
    - mteb-amazon_massive_intent-avs_triplets
    - mteb-amazon_massive_scenario-avs_triplets
    - mteb-amazon_reviews_multi-avs_triplets
    - mteb-banking77-avs_triplets
    - mteb-emotion-avs_triplets
    - mteb-imdb-avs_triplets
    - mteb-mtop_domain-avs_triplets
    - mteb-mtop_intent-avs_triplets
    - mteb-toxic_conversations_50k-avs_triplets
    - mteb-tweet_sentiment_extraction-avs_triplets
    - covid-bing-query-gpt4-avs_triplets
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): RandomProjection({'in_features': 384, 'out_features': 768, 'seed': 42})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("avsolatorio/all-MiniLM-L6-v2-MEDI-MTEB-triplet-randproj-64-final")
# Run inference
sentences = [
    'Does early second-trimester sonography predict adverse perinatal outcomes in monochorionic diamniotic twin pregnancies?',
    'To determine whether intertwin discordant abdominal circumference, femur length, head circumference, and estimated fetal weight sonographic measurements in early second-trimester monochorionic diamniotic twins predict adverse obstetric and neonatal outcomes.We conducted a multicenter retrospective cohort study involving 9 regional perinatal centers in the United States. We examined the records of all monochorionic diamniotic twin pregnancies with two live fetuses at the 16- to 18-week sonographic examination who had serial follow-up sonography until delivery. The intertwin discordance in abdominal circumference, femur length, head circumference, and estimated fetal weight was calculated as the difference between the two fetuses, expressed as a percentage of the larger using the 16- to 18-week sonographic measurements. An adverse composite obstetric outcome was defined as the occurrence of 1 or more of the following in either fetus: intrauterine growth restriction, twin-twin transfusion syndrome, intrauterine fetal death, abnormal growth discordance (≥20% difference), and very preterm birth at or before 28 weeks. An adverse composite neonatal outcome was defined as the occurrence of 1 or more of the following: respiratory distress syndrome, any stage of intraventricular hemorrhage, 5-minute Apgar score less than 7, necrotizing enterocolitis, culture-proven early-onset sepsis, and neonatal death. Receiver operating characteristic and logistic regression-with-generalized estimating equation analyses were constructed.Among the 177 monochorionic diamniotic twin pregnancies analyzed, intertwin abdominal circumference and estimated fetal weight discordances were only predictive of adverse composite obstetric outcomes (areas under the curve, 79% and 80%, respectively). Receiver operating characteristic curves showed that intertwin discordances in abdominal circumference, femur length, head circumference, and estimated fetal weight were not acceptable predictors of twin-twin transfusion syndrome or adverse neonatal outcomes.',
    'Calcium and vitamin D are essential nutrients for bone metabolism Vitamin D can either be obtained from dietary sources or cutaneous synthesis. The study was conducted in subtropic weather; therefore, some might believe that the levels of solar radiation would be sufficient in this area.To evaluate calcium and vitamin D supplementation in postmenopausal women with osteoporosis living in a sunny country.A 3-month controlled clinical trial with 64 postmenopausal women with osteoporosis, mean age 62 + or - 8 years. They were randomly assigned to either the supplement group, who received 1,200 mg of calcium carbonate and 400 IU (10 microg) of vitamin D(3,) or the control group. Dietary intake assessment was performed, bone mineral density and body composition were measured, and biochemical markers of bone metabolism were analyzed.Considering all participants at baseline, serum vitamin D was under 75 nmol/l in 91.4% of the participants. The concentration of serum 25(OH)D increased significantly (p = 0.023) after 3 months of supplementation from 46.67 + or - 13.97 to 59.47 + or - 17.50 nmol/l. However, the dose given was limited in effect, and 86.2% of the supplement group did not reach optimal levels of 25(OH)D. Parathyroid hormone was elevated in 22.4% of the study group. After the intervention period, mean parathyroid hormone tended to decrease in the supplement group (p = 0.063).',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

#### Triplet

* Dataset: `medi-mteb-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9153** |

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## Training Details

### Training Datasets

#### NQ

* Dataset: NQ
* Size: 49,548 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                              | negative                                                                              |
  |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                                | string                                                                                |
  | details | <ul><li>min: 10 tokens</li><li>mean: 11.77 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 113 tokens</li><li>mean: 137.23 tokens</li><li>max: 220 tokens</li></ul> | <ul><li>min: 110 tokens</li><li>mean: 138.25 tokens</li><li>max: 239 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### pubmed

* Dataset: pubmed
* Size: 29,716 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                             | negative                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | string                                                                               |
  | details | <ul><li>min: 6 tokens</li><li>mean: 22.99 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 78 tokens</li><li>mean: 240.63 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 50 tokens</li><li>mean: 239.04 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### specter_train_triples

* Dataset: specter_train_triples
* Size: 49,548 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 15.21 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.87 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.01 tokens</li><li>max: 70 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### S2ORC_citations_abstracts

* Dataset: S2ORC_citations_abstracts
* Size: 99,032 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                               | positive                                                                            | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                              | string                                                                               |
  | details | <ul><li>min: 22 tokens</li><li>mean: 198.64 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 203.8 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 203.03 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### fever

* Dataset: fever
* Size: 74,258 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                             | negative                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | string                                                                               |
  | details | <ul><li>min: 6 tokens</li><li>mean: 12.23 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 111.79 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 113.24 tokens</li><li>max: 179 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### gooaq_pairs

* Dataset: gooaq_pairs
* Size: 24,774 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                            | negative                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 59.94 tokens</li><li>max: 138 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 63.35 tokens</li><li>max: 149 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### codesearchnet

* Dataset: codesearchnet
* Size: 14,890 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                             | negative                                                                             |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               | string                                                                               |
  | details | <ul><li>min: 4 tokens</li><li>mean: 29.54 tokens</li><li>max: 124 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 132.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 163.79 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### wikihow

* Dataset: wikihow
* Size: 5,006 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                            | negative                                                                           |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              | string                                                                             |
  | details | <ul><li>min: 3 tokens</li><li>mean: 8.16 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 44.62 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 36.33 tokens</li><li>max: 100 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### WikiAnswers

* Dataset: WikiAnswers
* Size: 24,774 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 12.83 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.7 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.12 tokens</li><li>max: 42 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### eli5_question_answer

* Dataset: eli5_question_answer
* Size: 24,774 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                             | negative                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | string                                                                               |
  | details | <ul><li>min: 4 tokens</li><li>mean: 20.98 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 103.88 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 111.38 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### amazon-qa

* Dataset: amazon-qa
* Size: 99,032 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                              |
  | details | <ul><li>min: 6 tokens</li><li>mean: 23.07 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 54.48 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 61.35 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### medmcqa

* Dataset: medmcqa
* Size: 29,716 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                              |
  | details | <ul><li>min: 5 tokens</li><li>mean: 19.86 tokens</li><li>max: 176 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 113.43 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 108.04 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### zeroshot

* Dataset: zeroshot
* Size: 14,890 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                             | negative                                                                             |
  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                               | string                                                                               |
  | details | <ul><li>min: 5 tokens</li><li>mean: 8.65 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 112.61 tokens</li><li>max: 163 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 117.07 tokens</li><li>max: 214 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### TriviaQA_pairs

* Dataset: TriviaQA_pairs
* Size: 49,548 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                             | negative                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | string                                                                              |
  | details | <ul><li>min: 7 tokens</li><li>mean: 19.79 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 245.73 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 231.5 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### PAQ_pairs

* Dataset: PAQ_pairs
* Size: 24,774 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                              | negative                                                                              |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                                | string                                                                                |
  | details | <ul><li>min: 7 tokens</li><li>mean: 12.67 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 110 tokens</li><li>mean: 135.61 tokens</li><li>max: 223 tokens</li></ul> | <ul><li>min: 111 tokens</li><li>mean: 135.86 tokens</li><li>max: 254 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### stackexchange_duplicate_questions_title-body_title-body

* Dataset: stackexchange_duplicate_questions_title-body_title-body
* Size: 24,774 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 21 tokens</li><li>mean: 146.64 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 141.12 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 200.51 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### trex

* Dataset: trex
* Size: 29,716 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                            | negative                                                                             |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              | string                                                                               |
  | details | <ul><li>min: 5 tokens</li><li>mean: 9.43 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 102.9 tokens</li><li>max: 166 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 118.59 tokens</li><li>max: 236 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### flickr30k_captions

* Dataset: flickr30k_captions
* Size: 24,774 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 15.87 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.83 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.13 tokens</li><li>max: 61 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### hotpotqa

* Dataset: hotpotqa
* Size: 39,600 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                             | negative                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | string                                                                               |
  | details | <ul><li>min: 9 tokens</li><li>mean: 24.46 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 113.58 tokens</li><li>max: 176 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 114.85 tokens</li><li>max: 167 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task671_ambigqa_text_generation

* Dataset: task671_ambigqa_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 11 tokens</li><li>mean: 12.64 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 12.44 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 12.2 tokens</li><li>max: 19 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task061_ropes_answer_generation

* Dataset: task061_ropes_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 117 tokens</li><li>mean: 209.31 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 117 tokens</li><li>mean: 208.62 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 119 tokens</li><li>mean: 211.39 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task285_imdb_answer_generation

* Dataset: task285_imdb_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 46 tokens</li><li>mean: 209.96 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 49 tokens</li><li>mean: 205.18 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 209.96 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task905_hate_speech_offensive_classification

* Dataset: task905_hate_speech_offensive_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 15 tokens</li><li>mean: 41.48 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 40.59 tokens</li><li>max: 198 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 32.37 tokens</li><li>max: 135 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task566_circa_classification

* Dataset: task566_circa_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                            |
  | details | <ul><li>min: 20 tokens</li><li>mean: 27.85 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 27.3 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 27.5 tokens</li><li>max: 47 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task184_snli_entailment_to_neutral_text_modification

* Dataset: task184_snli_entailment_to_neutral_text_modification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                              |
  | details | <ul><li>min: 17 tokens</li><li>mean: 29.79 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 28.88 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 30.16 tokens</li><li>max: 100 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task280_stereoset_classification_stereotype_type

* Dataset: task280_stereoset_classification_stereotype_type
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 18.4 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 16.82 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 16.81 tokens</li><li>max: 51 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1599_smcalflow_classification

* Dataset: task1599_smcalflow_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 11.32 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.48 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.23 tokens</li><li>max: 45 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1384_deal_or_no_dialog_classification

* Dataset: task1384_deal_or_no_dialog_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 14 tokens</li><li>mean: 59.18 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 58.75 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 58.81 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task591_sciq_answer_generation

* Dataset: task591_sciq_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 17.64 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.17 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.76 tokens</li><li>max: 75 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task823_peixian-rtgender_sentiment_analysis

* Dataset: task823_peixian-rtgender_sentiment_analysis
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 16 tokens</li><li>mean: 57.03 tokens</li><li>max: 129 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 59.85 tokens</li><li>max: 153 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.39 tokens</li><li>max: 169 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task023_cosmosqa_question_generation

* Dataset: task023_cosmosqa_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 35 tokens</li><li>mean: 79.22 tokens</li><li>max: 159 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 80.25 tokens</li><li>max: 165 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 79.05 tokens</li><li>max: 161 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task900_freebase_qa_category_classification

* Dataset: task900_freebase_qa_category_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 20.33 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.3 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 19.08 tokens</li><li>max: 69 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task924_event2mind_word_generation

* Dataset: task924_event2mind_word_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 17 tokens</li><li>mean: 32.19 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 32.09 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 31.45 tokens</li><li>max: 68 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task152_tomqa_find_location_easy_noise

* Dataset: task152_tomqa_find_location_easy_noise
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 37 tokens</li><li>mean: 52.67 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 52.21 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 52.78 tokens</li><li>max: 82 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1368_healthfact_sentence_generation

* Dataset: task1368_healthfact_sentence_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 91 tokens</li><li>mean: 240.92 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 84 tokens</li><li>mean: 239.86 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 97 tokens</li><li>mean: 245.16 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1661_super_glue_classification

* Dataset: task1661_super_glue_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 35 tokens</li><li>mean: 140.96 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 144.29 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 143.59 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1187_politifact_classification

* Dataset: task1187_politifact_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 33.19 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 31.7 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 31.87 tokens</li><li>max: 71 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1728_web_nlg_data_to_text

* Dataset: task1728_web_nlg_data_to_text
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 7 tokens</li><li>mean: 42.96 tokens</li><li>max: 152 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 46.52 tokens</li><li>max: 152 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 42.39 tokens</li><li>max: 152 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task112_asset_simple_sentence_identification

* Dataset: task112_asset_simple_sentence_identification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 18 tokens</li><li>mean: 51.98 tokens</li><li>max: 136 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 51.84 tokens</li><li>max: 144 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 51.97 tokens</li><li>max: 114 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1340_msr_text_compression_compression

* Dataset: task1340_msr_text_compression_compression
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 14 tokens</li><li>mean: 42.15 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 44.46 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 40.14 tokens</li><li>max: 141 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task072_abductivenli_answer_generation

* Dataset: task072_abductivenli_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 17 tokens</li><li>mean: 26.9 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 26.28 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 26.46 tokens</li><li>max: 55 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1504_hatexplain_answer_generation

* Dataset: task1504_hatexplain_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 29.09 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 24.67 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 27.96 tokens</li><li>max: 67 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task684_online_privacy_policy_text_information_type_generation

* Dataset: task684_online_privacy_policy_text_information_type_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 30.02 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 30.19 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 30.18 tokens</li><li>max: 68 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1290_xsum_summarization

* Dataset: task1290_xsum_summarization
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 39 tokens</li><li>mean: 226.27 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 50 tokens</li><li>mean: 228.93 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 229.41 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task075_squad1.1_answer_generation

* Dataset: task075_squad1.1_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                            | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                              | string                                                                               |
  | details | <ul><li>min: 48 tokens</li><li>mean: 168.58 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 172.1 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 181.15 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1587_scifact_classification

* Dataset: task1587_scifact_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 88 tokens</li><li>mean: 242.35 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 90 tokens</li><li>mean: 246.75 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 86 tokens</li><li>mean: 244.87 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task384_socialiqa_question_classification

* Dataset: task384_socialiqa_question_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 24 tokens</li><li>mean: 35.44 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 34.35 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 34.51 tokens</li><li>max: 57 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1555_scitail_answer_generation

* Dataset: task1555_scitail_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 18 tokens</li><li>mean: 36.72 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 36.31 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 36.73 tokens</li><li>max: 92 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1532_daily_dialog_emotion_classification

* Dataset: task1532_daily_dialog_emotion_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 16 tokens</li><li>mean: 137.07 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 140.81 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 132.89 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task239_tweetqa_answer_generation

* Dataset: task239_tweetqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 28 tokens</li><li>mean: 55.78 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 56.32 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 55.92 tokens</li><li>max: 81 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task596_mocha_question_generation

* Dataset: task596_mocha_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 34 tokens</li><li>mean: 80.49 tokens</li><li>max: 163 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 95.93 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 44.93 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1411_dart_subject_identification

* Dataset: task1411_dart_subject_identification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 14.86 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.02 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.25 tokens</li><li>max: 38 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1359_numer_sense_answer_generation

* Dataset: task1359_numer_sense_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 18.67 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 18.43 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 18.34 tokens</li><li>max: 30 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task329_gap_classification

* Dataset: task329_gap_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 40 tokens</li><li>mean: 122.88 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 62 tokens</li><li>mean: 127.47 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 58 tokens</li><li>mean: 127.71 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task220_rocstories_title_classification

* Dataset: task220_rocstories_title_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 53 tokens</li><li>mean: 80.81 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 81.08 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 55 tokens</li><li>mean: 79.99 tokens</li><li>max: 115 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task316_crows-pairs_classification_stereotype

* Dataset: task316_crows-pairs_classification_stereotype
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 19.78 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.31 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.87 tokens</li><li>max: 52 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task495_semeval_headline_classification

* Dataset: task495_semeval_headline_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 17 tokens</li><li>mean: 24.57 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 24.29 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 24.14 tokens</li><li>max: 38 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1168_brown_coarse_pos_tagging

* Dataset: task1168_brown_coarse_pos_tagging
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                           | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | string                                                                              |
  | details | <ul><li>min: 13 tokens</li><li>mean: 43.61 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 42.6 tokens</li><li>max: 197 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 44.23 tokens</li><li>max: 197 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task348_squad2.0_unanswerable_question_generation

* Dataset: task348_squad2.0_unanswerable_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 30 tokens</li><li>mean: 153.88 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 161.26 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 166.13 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task049_multirc_questions_needed_to_answer

* Dataset: task049_multirc_questions_needed_to_answer
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                              | negative                                                                              |
  |:--------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                                | string                                                                                |
  | details | <ul><li>min: 174 tokens</li><li>mean: 252.7 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 169 tokens</li><li>mean: 252.85 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 178 tokens</li><li>mean: 252.93 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1534_daily_dialog_question_classification

* Dataset: task1534_daily_dialog_question_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                             | negative                                                                             |
  |:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                               | string                                                                               |
  | details | <ul><li>min: 17 tokens</li><li>mean: 124.7 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 130.68 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 135.16 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task322_jigsaw_classification_threat

* Dataset: task322_jigsaw_classification_threat
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 7 tokens</li><li>mean: 54.9 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 62.74 tokens</li><li>max: 249 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 61.92 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task295_semeval_2020_task4_commonsense_reasoning

* Dataset: task295_semeval_2020_task4_commonsense_reasoning
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 25 tokens</li><li>mean: 45.35 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 44.74 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 44.53 tokens</li><li>max: 88 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task186_snli_contradiction_to_entailment_text_modification

* Dataset: task186_snli_contradiction_to_entailment_text_modification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                           | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | string                                                                             |
  | details | <ul><li>min: 18 tokens</li><li>mean: 31.09 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 30.26 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 32.22 tokens</li><li>max: 67 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task034_winogrande_question_modification_object

* Dataset: task034_winogrande_question_modification_object
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 29 tokens</li><li>mean: 36.26 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 35.64 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 34.85 tokens</li><li>max: 55 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task160_replace_letter_in_a_sentence

* Dataset: task160_replace_letter_in_a_sentence
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 29 tokens</li><li>mean: 32.03 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 31.76 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 31.77 tokens</li><li>max: 48 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task469_mrqa_answer_generation

* Dataset: task469_mrqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 27 tokens</li><li>mean: 182.13 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 180.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 183.72 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task105_story_cloze-rocstories_sentence_generation

* Dataset: task105_story_cloze-rocstories_sentence_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 36 tokens</li><li>mean: 55.65 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 55.02 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 55.88 tokens</li><li>max: 76 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task649_race_blank_question_generation

* Dataset: task649_race_blank_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                              |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                                |
  | details | <ul><li>min: 36 tokens</li><li>mean: 252.95 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 252.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 157 tokens</li><li>mean: 253.91 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1536_daily_dialog_happiness_classification

* Dataset: task1536_daily_dialog_happiness_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                            |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                              |
  | details | <ul><li>min: 13 tokens</li><li>mean: 127.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 134.02 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 143.7 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task683_online_privacy_policy_text_purpose_answer_generation

* Dataset: task683_online_privacy_policy_text_purpose_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 30.09 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 30.5 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 30.07 tokens</li><li>max: 68 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task024_cosmosqa_answer_generation

* Dataset: task024_cosmosqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                             |
  | details | <ul><li>min: 45 tokens</li><li>mean: 92.62 tokens</li><li>max: 176 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 93.35 tokens</li><li>max: 174 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 94.9 tokens</li><li>max: 183 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task584_udeps_eng_fine_pos_tagging

* Dataset: task584_udeps_eng_fine_pos_tagging
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 12 tokens</li><li>mean: 40.09 tokens</li><li>max: 120 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 39.35 tokens</li><li>max: 186 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 40.38 tokens</li><li>max: 148 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task066_timetravel_binary_consistency_classification

* Dataset: task066_timetravel_binary_consistency_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 42 tokens</li><li>mean: 66.69 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 67.34 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 67.19 tokens</li><li>max: 92 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task413_mickey_en_sentence_perturbation_generation

* Dataset: task413_mickey_en_sentence_perturbation_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 13.71 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.75 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.29 tokens</li><li>max: 20 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task182_duorc_question_generation

* Dataset: task182_duorc_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                              | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                                | string                                                                               |
  | details | <ul><li>min: 99 tokens</li><li>mean: 242.77 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 246.47 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 99 tokens</li><li>mean: 246.38 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task028_drop_answer_generation

* Dataset: task028_drop_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 76 tokens</li><li>mean: 230.94 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 86 tokens</li><li>mean: 234.89 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 81 tokens</li><li>mean: 235.48 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1601_webquestions_answer_generation

* Dataset: task1601_webquestions_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                            |
  | details | <ul><li>min: 9 tokens</li><li>mean: 16.49 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 16.71 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 16.76 tokens</li><li>max: 27 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1295_adversarial_qa_question_answering

* Dataset: task1295_adversarial_qa_question_answering
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 45 tokens</li><li>mean: 163.69 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 54 tokens</li><li>mean: 166.23 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 166.52 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task201_mnli_neutral_classification

* Dataset: task201_mnli_neutral_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 24 tokens</li><li>mean: 72.97 tokens</li><li>max: 218 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 73.29 tokens</li><li>max: 170 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 72.24 tokens</li><li>max: 205 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task038_qasc_combined_fact

* Dataset: task038_qasc_combined_fact
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 18 tokens</li><li>mean: 31.25 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 30.61 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 30.86 tokens</li><li>max: 53 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task293_storycommonsense_emotion_text_generation

* Dataset: task293_storycommonsense_emotion_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 40.25 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 40.27 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 38.11 tokens</li><li>max: 86 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task572_recipe_nlg_text_generation

* Dataset: task572_recipe_nlg_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 24 tokens</li><li>mean: 115.66 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 122.27 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 124.11 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task517_emo_classify_emotion_of_dialogue

* Dataset: task517_emo_classify_emotion_of_dialogue
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 18.13 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.07 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 18.5 tokens</li><li>max: 67 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task382_hybridqa_answer_generation

* Dataset: task382_hybridqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 29 tokens</li><li>mean: 42.28 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 41.56 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 41.74 tokens</li><li>max: 75 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task176_break_decompose_questions

* Dataset: task176_break_decompose_questions
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                           |
  | details | <ul><li>min: 9 tokens</li><li>mean: 17.48 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.2 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.6 tokens</li><li>max: 38 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1291_multi_news_summarization

* Dataset: task1291_multi_news_summarization
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                             |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                               |
  | details | <ul><li>min: 116 tokens</li><li>mean: 255.49 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 146 tokens</li><li>mean: 255.55 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 68 tokens</li><li>mean: 251.87 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task155_count_nouns_verbs

* Dataset: task155_count_nouns_verbs
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 23 tokens</li><li>mean: 27.05 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 26.81 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 26.98 tokens</li><li>max: 46 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task031_winogrande_question_generation_object

* Dataset: task031_winogrande_question_generation_object
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                        | negative                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                          | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 7.42 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.3 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.25 tokens</li><li>max: 11 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task279_stereoset_classification_stereotype

* Dataset: task279_stereoset_classification_stereotype
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 17.85 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.47 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.28 tokens</li><li>max: 50 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1336_peixian_equity_evaluation_corpus_gender_classifier

* Dataset: task1336_peixian_equity_evaluation_corpus_gender_classifier
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                           |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.66 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.61 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.72 tokens</li><li>max: 16 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task508_scruples_dilemmas_more_ethical_isidentifiable

* Dataset: task508_scruples_dilemmas_more_ethical_isidentifiable
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                             |
  | details | <ul><li>min: 12 tokens</li><li>mean: 29.84 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 28.5 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 28.66 tokens</li><li>max: 86 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task518_emo_different_dialogue_emotions

* Dataset: task518_emo_different_dialogue_emotions
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                              |
  | details | <ul><li>min: 28 tokens</li><li>mean: 47.9 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 45.44 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 46.17 tokens</li><li>max: 123 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task077_splash_explanation_to_sql

* Dataset: task077_splash_explanation_to_sql
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 8 tokens</li><li>mean: 39.24 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 39.15 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 35.65 tokens</li><li>max: 111 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task923_event2mind_classifier

* Dataset: task923_event2mind_classifier
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 20.63 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 18.75 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 19.63 tokens</li><li>max: 46 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task470_mrqa_question_generation

* Dataset: task470_mrqa_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 13 tokens</li><li>mean: 173.13 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 175.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 181.16 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task638_multi_woz_classification

* Dataset: task638_multi_woz_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                             | negative                                                                             |
  |:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                               | string                                                                               |
  | details | <ul><li>min: 78 tokens</li><li>mean: 223.5 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 76 tokens</li><li>mean: 220.15 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 64 tokens</li><li>mean: 220.29 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1412_web_questions_question_answering

* Dataset: task1412_web_questions_question_answering
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 10.32 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.23 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.06 tokens</li><li>max: 16 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task847_pubmedqa_question_generation

* Dataset: task847_pubmedqa_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 21 tokens</li><li>mean: 249.15 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 248.61 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 248.86 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task678_ollie_actual_relationship_answer_generation

* Dataset: task678_ollie_actual_relationship_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                              |
  | details | <ul><li>min: 20 tokens</li><li>mean: 40.63 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 38.38 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 40.99 tokens</li><li>max: 104 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task290_tellmewhy_question_answerability

* Dataset: task290_tellmewhy_question_answerability
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 37 tokens</li><li>mean: 62.58 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 62.21 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 62.91 tokens</li><li>max: 95 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task575_air_dialogue_classification

* Dataset: task575_air_dialogue_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                           |
  | details | <ul><li>min: 4 tokens</li><li>mean: 14.18 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.6 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.3 tokens</li><li>max: 42 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task189_snli_neutral_to_contradiction_text_modification

* Dataset: task189_snli_neutral_to_contradiction_text_modification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                              |
  | details | <ul><li>min: 18 tokens</li><li>mean: 31.89 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 30.66 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 33.29 tokens</li><li>max: 105 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task026_drop_question_generation

* Dataset: task026_drop_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 82 tokens</li><li>mean: 219.82 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 57 tokens</li><li>mean: 222.71 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 96 tokens</li><li>mean: 232.56 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task162_count_words_starting_with_letter

* Dataset: task162_count_words_starting_with_letter
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 28 tokens</li><li>mean: 32.16 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 31.77 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 31.65 tokens</li><li>max: 46 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task079_conala_concat_strings

* Dataset: task079_conala_concat_strings
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 11 tokens</li><li>mean: 39.94 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 34.24 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 33.86 tokens</li><li>max: 76 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task610_conllpp_ner

* Dataset: task610_conllpp_ner
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 19.74 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.71 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.24 tokens</li><li>max: 54 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task046_miscellaneous_question_typing

* Dataset: task046_miscellaneous_question_typing
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 16 tokens</li><li>mean: 25.26 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 24.84 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 25.2 tokens</li><li>max: 57 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task197_mnli_domain_answer_generation

* Dataset: task197_mnli_domain_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 15 tokens</li><li>mean: 44.08 tokens</li><li>max: 197 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 44.95 tokens</li><li>max: 211 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 39.27 tokens</li><li>max: 115 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1325_qa_zre_question_generation_on_subject_relation

* Dataset: task1325_qa_zre_question_generation_on_subject_relation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 18 tokens</li><li>mean: 50.63 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 49.26 tokens</li><li>max: 180 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 54.42 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task430_senteval_subject_count

* Dataset: task430_senteval_subject_count
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 17.26 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.37 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.07 tokens</li><li>max: 34 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task672_nummersense

* Dataset: task672_nummersense
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 15.66 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.43 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.25 tokens</li><li>max: 30 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task402_grailqa_paraphrase_generation

* Dataset: task402_grailqa_paraphrase_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 23 tokens</li><li>mean: 129.84 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 139.54 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 136.75 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task904_hate_speech_offensive_classification

* Dataset: task904_hate_speech_offensive_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 34.35 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 34.38 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 27.8 tokens</li><li>max: 148 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task192_hotpotqa_sentence_generation

* Dataset: task192_hotpotqa_sentence_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 37 tokens</li><li>mean: 124.56 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 123.35 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 132.67 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task069_abductivenli_classification

* Dataset: task069_abductivenli_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 33 tokens</li><li>mean: 52.03 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 51.87 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 52.01 tokens</li><li>max: 95 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task574_air_dialogue_sentence_generation

* Dataset: task574_air_dialogue_sentence_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                            | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                              | string                                                                               |
  | details | <ul><li>min: 54 tokens</li><li>mean: 144.28 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 57 tokens</li><li>mean: 144.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 66 tokens</li><li>mean: 148.22 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task187_snli_entailment_to_contradiction_text_modification

* Dataset: task187_snli_entailment_to_contradiction_text_modification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                             |
  | details | <ul><li>min: 16 tokens</li><li>mean: 30.35 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 29.87 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 29.47 tokens</li><li>max: 71 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task749_glucose_reverse_cause_emotion_detection

* Dataset: task749_glucose_reverse_cause_emotion_detection
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 38 tokens</li><li>mean: 67.51 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 67.07 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 68.56 tokens</li><li>max: 107 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1552_scitail_question_generation

* Dataset: task1552_scitail_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 18.34 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.5 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.81 tokens</li><li>max: 54 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task750_aqua_multiple_choice_answering

* Dataset: task750_aqua_multiple_choice_answering
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                              |
  | details | <ul><li>min: 33 tokens</li><li>mean: 69.8 tokens</li><li>max: 194 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 68.34 tokens</li><li>max: 194 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 68.21 tokens</li><li>max: 165 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task327_jigsaw_classification_toxic

* Dataset: task327_jigsaw_classification_toxic
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 36.99 tokens</li><li>max: 234 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 41.72 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 44.88 tokens</li><li>max: 244 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1502_hatexplain_classification

* Dataset: task1502_hatexplain_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                           | negative                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                             | string                                                                           |
  | details | <ul><li>min: 5 tokens</li><li>mean: 28.7 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 26.89 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 26.9 tokens</li><li>max: 90 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task328_jigsaw_classification_insult

* Dataset: task328_jigsaw_classification_insult
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 50.28 tokens</li><li>max: 247 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 60.6 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 64.07 tokens</li><li>max: 249 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task304_numeric_fused_head_resolution

* Dataset: task304_numeric_fused_head_resolution
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 15 tokens</li><li>mean: 116.82 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 118.84 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 131.78 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1293_kilt_tasks_hotpotqa_question_answering

* Dataset: task1293_kilt_tasks_hotpotqa_question_answering
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 10 tokens</li><li>mean: 24.8 tokens</li><li>max: 114 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 24.33 tokens</li><li>max: 114 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 23.79 tokens</li><li>max: 84 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task216_rocstories_correct_answer_generation

* Dataset: task216_rocstories_correct_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 39 tokens</li><li>mean: 59.37 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 58.11 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 58.26 tokens</li><li>max: 95 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1326_qa_zre_question_generation_from_answer

* Dataset: task1326_qa_zre_question_generation_from_answer
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 17 tokens</li><li>mean: 46.71 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 45.51 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 49.23 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1338_peixian_equity_evaluation_corpus_sentiment_classifier

* Dataset: task1338_peixian_equity_evaluation_corpus_sentiment_classifier
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                           |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.72 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.73 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.61 tokens</li><li>max: 17 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1729_personachat_generate_next

* Dataset: task1729_personachat_generate_next
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 44 tokens</li><li>mean: 147.13 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 142.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 50 tokens</li><li>mean: 144.33 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1202_atomic_classification_xneed

* Dataset: task1202_atomic_classification_xneed
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 19.54 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 19.41 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 19.22 tokens</li><li>max: 28 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task400_paws_paraphrase_classification

* Dataset: task400_paws_paraphrase_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 19 tokens</li><li>mean: 52.25 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 51.75 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 52.95 tokens</li><li>max: 97 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task502_scruples_anecdotes_whoiswrong_verification

* Dataset: task502_scruples_anecdotes_whoiswrong_verification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 12 tokens</li><li>mean: 230.24 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 236.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 235.21 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task088_identify_typo_verification

* Dataset: task088_identify_typo_verification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 11 tokens</li><li>mean: 15.12 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 15.06 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 15.45 tokens</li><li>max: 47 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task221_rocstories_two_choice_classification

* Dataset: task221_rocstories_two_choice_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                             |
  | details | <ul><li>min: 47 tokens</li><li>mean: 72.36 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 72.48 tokens</li><li>max: 109 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 73.1 tokens</li><li>max: 108 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task200_mnli_entailment_classification

* Dataset: task200_mnli_entailment_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 24 tokens</li><li>mean: 72.71 tokens</li><li>max: 198 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 73.01 tokens</li><li>max: 224 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 73.39 tokens</li><li>max: 226 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task074_squad1.1_question_generation

* Dataset: task074_squad1.1_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 30 tokens</li><li>mean: 150.13 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 160.24 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 164.44 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task581_socialiqa_question_generation

* Dataset: task581_socialiqa_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 12 tokens</li><li>mean: 26.5 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 25.65 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 25.77 tokens</li><li>max: 48 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1186_nne_hrngo_classification

* Dataset: task1186_nne_hrngo_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 19 tokens</li><li>mean: 33.8 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 33.54 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 33.65 tokens</li><li>max: 77 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task898_freebase_qa_answer_generation

* Dataset: task898_freebase_qa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 19.39 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.69 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.38 tokens</li><li>max: 79 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1408_dart_similarity_classification

* Dataset: task1408_dart_similarity_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                             |
  | details | <ul><li>min: 22 tokens</li><li>mean: 59.5 tokens</li><li>max: 147 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 61.89 tokens</li><li>max: 154 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 48.9 tokens</li><li>max: 124 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task168_strategyqa_question_decomposition

* Dataset: task168_strategyqa_question_decomposition
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                             |
  | details | <ul><li>min: 42 tokens</li><li>mean: 79.99 tokens</li><li>max: 181 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 79.63 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 76.6 tokens</li><li>max: 166 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1357_xlsum_summary_generation

* Dataset: task1357_xlsum_summary_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 67 tokens</li><li>mean: 241.38 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 69 tokens</li><li>mean: 243.16 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 67 tokens</li><li>mean: 246.78 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task390_torque_text_span_selection

* Dataset: task390_torque_text_span_selection
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 47 tokens</li><li>mean: 110.58 tokens</li><li>max: 196 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 110.41 tokens</li><li>max: 195 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 111.15 tokens</li><li>max: 196 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task165_mcscript_question_answering_commonsense

* Dataset: task165_mcscript_question_answering_commonsense
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                              | negative                                                                              |
  |:--------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                                | string                                                                                |
  | details | <ul><li>min: 147 tokens</li><li>mean: 199.7 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 145 tokens</li><li>mean: 198.04 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 147 tokens</li><li>mean: 200.11 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1533_daily_dialog_formal_classification

* Dataset: task1533_daily_dialog_formal_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                            | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                              | string                                                                               |
  | details | <ul><li>min: 13 tokens</li><li>mean: 130.14 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 136.4 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 137.09 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task002_quoref_answer_generation

* Dataset: task002_quoref_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                             | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                               | string                                                                                |
  | details | <ul><li>min: 214 tokens</li><li>mean: 255.53 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 214 tokens</li><li>mean: 255.5 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 224 tokens</li><li>mean: 255.58 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1297_qasc_question_answering

* Dataset: task1297_qasc_question_answering
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 61 tokens</li><li>mean: 84.44 tokens</li><li>max: 134 tokens</li></ul> | <ul><li>min: 59 tokens</li><li>mean: 85.31 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>min: 58 tokens</li><li>mean: 84.94 tokens</li><li>max: 125 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task305_jeopardy_answer_generation_normal

* Dataset: task305_jeopardy_answer_generation_normal
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 27.68 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 27.48 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 27.42 tokens</li><li>max: 46 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task029_winogrande_full_object

* Dataset: task029_winogrande_full_object
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 7.38 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.34 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.24 tokens</li><li>max: 10 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1327_qa_zre_answer_generation_from_question

* Dataset: task1327_qa_zre_answer_generation_from_question
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 24 tokens</li><li>mean: 54.88 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 52.02 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 56.19 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task326_jigsaw_classification_obscene

* Dataset: task326_jigsaw_classification_obscene
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 63.85 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 76.17 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 72.28 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1542_every_ith_element_from_starting

* Dataset: task1542_every_ith_element_from_starting
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 13 tokens</li><li>mean: 125.18 tokens</li><li>max: 245 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 123.56 tokens</li><li>max: 244 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 121.24 tokens</li><li>max: 238 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task570_recipe_nlg_ner_generation

* Dataset: task570_recipe_nlg_ner_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 74.84 tokens</li><li>max: 250 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 73.97 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 76.51 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1409_dart_text_generation

* Dataset: task1409_dart_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                              |
  | details | <ul><li>min: 18 tokens</li><li>mean: 67.5 tokens</li><li>max: 174 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 72.28 tokens</li><li>max: 170 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 67.22 tokens</li><li>max: 164 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task401_numeric_fused_head_reference

* Dataset: task401_numeric_fused_head_reference
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 16 tokens</li><li>mean: 109.31 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 114.71 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 120.55 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task846_pubmedqa_classification

* Dataset: task846_pubmedqa_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 32 tokens</li><li>mean: 86.22 tokens</li><li>max: 246 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 85.64 tokens</li><li>max: 225 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 94.03 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1712_poki_classification

* Dataset: task1712_poki_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 53.16 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 56.97 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 63.57 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task344_hybridqa_answer_generation

* Dataset: task344_hybridqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 9 tokens</li><li>mean: 22.21 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 21.92 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 22.19 tokens</li><li>max: 55 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task875_emotion_classification

* Dataset: task875_emotion_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 23.18 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 18.52 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.35 tokens</li><li>max: 68 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1214_atomic_classification_xwant

* Dataset: task1214_atomic_classification_xwant
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 19.64 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 19.36 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 19.54 tokens</li><li>max: 31 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task106_scruples_ethical_judgment

* Dataset: task106_scruples_ethical_judgment
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 12 tokens</li><li>mean: 30.0 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 28.89 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 28.73 tokens</li><li>max: 58 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task238_iirc_answer_from_passage_answer_generation

* Dataset: task238_iirc_answer_from_passage_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                             |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                               |
  | details | <ul><li>min: 138 tokens</li><li>mean: 242.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 165 tokens</li><li>mean: 242.57 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 173 tokens</li><li>mean: 243.0 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1391_winogrande_easy_answer_generation

* Dataset: task1391_winogrande_easy_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 26 tokens</li><li>mean: 31.63 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 31.36 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 31.3 tokens</li><li>max: 49 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task195_sentiment140_classification

* Dataset: task195_sentiment140_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 22.47 tokens</li><li>max: 118 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 18.84 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 21.25 tokens</li><li>max: 51 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task163_count_words_ending_with_letter

* Dataset: task163_count_words_ending_with_letter
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 28 tokens</li><li>mean: 32.05 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 31.69 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 31.58 tokens</li><li>max: 43 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task579_socialiqa_classification

* Dataset: task579_socialiqa_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                             |
  | details | <ul><li>min: 39 tokens</li><li>mean: 54.11 tokens</li><li>max: 132 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 53.52 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 40 tokens</li><li>mean: 54.12 tokens</li><li>max: 84 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task569_recipe_nlg_text_generation

* Dataset: task569_recipe_nlg_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 25 tokens</li><li>mean: 192.16 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 55 tokens</li><li>mean: 193.74 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 199.11 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1602_webquestion_question_genreation

* Dataset: task1602_webquestion_question_genreation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                           | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | string                                                                             |
  | details | <ul><li>min: 12 tokens</li><li>mean: 23.95 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 24.6 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 22.6 tokens</li><li>max: 120 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task747_glucose_cause_emotion_detection

* Dataset: task747_glucose_cause_emotion_detection
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                             |
  | details | <ul><li>min: 35 tokens</li><li>mean: 68.23 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 68.25 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 68.75 tokens</li><li>max: 99 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task219_rocstories_title_answer_generation

* Dataset: task219_rocstories_title_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 42 tokens</li><li>mean: 67.62 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 66.65 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 41 tokens</li><li>mean: 66.89 tokens</li><li>max: 96 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task178_quartz_question_answering

* Dataset: task178_quartz_question_answering
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 28 tokens</li><li>mean: 57.96 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 57.18 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 56.74 tokens</li><li>max: 102 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task103_facts2story_long_text_generation

* Dataset: task103_facts2story_long_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 52 tokens</li><li>mean: 80.34 tokens</li><li>max: 143 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 82.24 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 49 tokens</li><li>mean: 78.57 tokens</li><li>max: 136 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task301_record_question_generation

* Dataset: task301_record_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 140 tokens</li><li>mean: 210.76 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 139 tokens</li><li>mean: 209.62 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 143 tokens</li><li>mean: 209.06 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1369_healthfact_sentence_generation

* Dataset: task1369_healthfact_sentence_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 110 tokens</li><li>mean: 243.14 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 101 tokens</li><li>mean: 242.95 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 113 tokens</li><li>mean: 251.89 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task515_senteval_odd_word_out

* Dataset: task515_senteval_odd_word_out
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 19.75 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.02 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 18.93 tokens</li><li>max: 35 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task496_semeval_answer_generation

* Dataset: task496_semeval_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 28.06 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 27.74 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 27.69 tokens</li><li>max: 45 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1658_billsum_summarization

* Dataset: task1658_billsum_summarization
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 256 tokens</li><li>mean: 256.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 256 tokens</li><li>mean: 256.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 256 tokens</li><li>mean: 256.0 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1204_atomic_classification_hinderedby

* Dataset: task1204_atomic_classification_hinderedby
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 21.98 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 22.01 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 21.48 tokens</li><li>max: 38 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1392_superglue_multirc_answer_verification

* Dataset: task1392_superglue_multirc_answer_verification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                             |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                               |
  | details | <ul><li>min: 128 tokens</li><li>mean: 241.47 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 127 tokens</li><li>mean: 241.68 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 136 tokens</li><li>mean: 241.8 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task306_jeopardy_answer_generation_double

* Dataset: task306_jeopardy_answer_generation_double
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 27.73 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 27.13 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 27.69 tokens</li><li>max: 47 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1286_openbookqa_question_answering

* Dataset: task1286_openbookqa_question_answering
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 22 tokens</li><li>mean: 39.38 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 38.71 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 38.22 tokens</li><li>max: 89 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task159_check_frequency_of_words_in_sentence_pair

* Dataset: task159_check_frequency_of_words_in_sentence_pair
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 44 tokens</li><li>mean: 50.34 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 50.29 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 50.51 tokens</li><li>max: 66 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task151_tomqa_find_location_easy_clean

* Dataset: task151_tomqa_find_location_easy_clean
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 37 tokens</li><li>mean: 50.63 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 50.35 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 50.53 tokens</li><li>max: 74 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task323_jigsaw_classification_sexually_explicit

* Dataset: task323_jigsaw_classification_sexually_explicit
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 66.74 tokens</li><li>max: 248 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 77.15 tokens</li><li>max: 248 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 75.88 tokens</li><li>max: 251 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task037_qasc_generate_related_fact

* Dataset: task037_qasc_generate_related_fact
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 13 tokens</li><li>mean: 22.02 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 21.97 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 21.87 tokens</li><li>max: 40 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task027_drop_answer_type_generation

* Dataset: task027_drop_answer_type_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 87 tokens</li><li>mean: 229.25 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 74 tokens</li><li>mean: 230.99 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 71 tokens</li><li>mean: 232.46 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1596_event2mind_text_generation_2

* Dataset: task1596_event2mind_text_generation_2
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.92 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.05 tokens</li><li>max: 18 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task141_odd-man-out_classification_category

* Dataset: task141_odd-man-out_classification_category
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 16 tokens</li><li>mean: 18.45 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 18.39 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 18.46 tokens</li><li>max: 25 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task194_duorc_answer_generation

* Dataset: task194_duorc_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 149 tokens</li><li>mean: 251.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 147 tokens</li><li>mean: 252.15 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 148 tokens</li><li>mean: 251.93 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task679_hope_edi_english_text_classification

* Dataset: task679_hope_edi_english_text_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 27.42 tokens</li><li>max: 199 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.83 tokens</li><li>max: 205 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 29.66 tokens</li><li>max: 194 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task246_dream_question_generation

* Dataset: task246_dream_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 17 tokens</li><li>mean: 80.19 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 80.98 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 86.73 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1195_disflqa_disfluent_to_fluent_conversion

* Dataset: task1195_disflqa_disfluent_to_fluent_conversion
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                            |
  | details | <ul><li>min: 9 tokens</li><li>mean: 19.8 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 19.78 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.34 tokens</li><li>max: 44 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task065_timetravel_consistent_sentence_classification

* Dataset: task065_timetravel_consistent_sentence_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 55 tokens</li><li>mean: 79.64 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 79.21 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 53 tokens</li><li>mean: 79.78 tokens</li><li>max: 110 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task351_winomt_classification_gender_identifiability_anti

* Dataset: task351_winomt_classification_gender_identifiability_anti
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 16 tokens</li><li>mean: 21.77 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 21.69 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 21.8 tokens</li><li>max: 30 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task580_socialiqa_answer_generation

* Dataset: task580_socialiqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                          | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                            | string                                                                             |
  | details | <ul><li>min: 35 tokens</li><li>mean: 52.45 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 51.1 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 50.97 tokens</li><li>max: 87 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task583_udeps_eng_coarse_pos_tagging

* Dataset: task583_udeps_eng_coarse_pos_tagging
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 12 tokens</li><li>mean: 40.78 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 40.09 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 40.55 tokens</li><li>max: 185 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task202_mnli_contradiction_classification

* Dataset: task202_mnli_contradiction_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                              |
  | details | <ul><li>min: 24 tokens</li><li>mean: 74.1 tokens</li><li>max: 190 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 76.44 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 75.12 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task222_rocstories_two_chioce_slotting_classification

* Dataset: task222_rocstories_two_chioce_slotting_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 48 tokens</li><li>mean: 73.15 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 73.22 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 49 tokens</li><li>mean: 72.05 tokens</li><li>max: 102 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task498_scruples_anecdotes_whoiswrong_classification

* Dataset: task498_scruples_anecdotes_whoiswrong_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 24 tokens</li><li>mean: 225.53 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 231.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 230.65 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task067_abductivenli_answer_generation

* Dataset: task067_abductivenli_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 26.79 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 26.12 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 26.33 tokens</li><li>max: 38 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task616_cola_classification

* Dataset: task616_cola_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 12.79 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.55 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.25 tokens</li><li>max: 29 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task286_olid_offense_judgment

* Dataset: task286_olid_offense_judgment
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 33.05 tokens</li><li>max: 145 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 31.09 tokens</li><li>max: 171 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 30.89 tokens</li><li>max: 169 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task188_snli_neutral_to_entailment_text_modification

* Dataset: task188_snli_neutral_to_entailment_text_modification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 18 tokens</li><li>mean: 31.81 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 31.16 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 33.04 tokens</li><li>max: 84 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task223_quartz_explanation_generation

* Dataset: task223_quartz_explanation_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 12 tokens</li><li>mean: 31.45 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 31.82 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 29.1 tokens</li><li>max: 96 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task820_protoqa_answer_generation

* Dataset: task820_protoqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 14.84 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 14.52 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.23 tokens</li><li>max: 29 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task196_sentiment140_answer_generation

* Dataset: task196_sentiment140_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 17 tokens</li><li>mean: 36.15 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 32.89 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 36.14 tokens</li><li>max: 72 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1678_mathqa_answer_selection

* Dataset: task1678_mathqa_answer_selection
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 33 tokens</li><li>mean: 69.95 tokens</li><li>max: 177 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 68.73 tokens</li><li>max: 146 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 69.24 tokens</li><li>max: 160 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task349_squad2.0_answerable_unanswerable_question_classification

* Dataset: task349_squad2.0_answerable_unanswerable_question_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 53 tokens</li><li>mean: 175.57 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 57 tokens</li><li>mean: 175.84 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 53 tokens</li><li>mean: 175.49 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task154_tomqa_find_location_hard_noise

* Dataset: task154_tomqa_find_location_hard_noise
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                             |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                               |
  | details | <ul><li>min: 129 tokens</li><li>mean: 175.63 tokens</li><li>max: 253 tokens</li></ul> | <ul><li>min: 126 tokens</li><li>mean: 175.85 tokens</li><li>max: 249 tokens</li></ul> | <ul><li>min: 128 tokens</li><li>mean: 177.2 tokens</li><li>max: 254 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task333_hateeval_classification_hate_en

* Dataset: task333_hateeval_classification_hate_en
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 8 tokens</li><li>mean: 38.62 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 37.48 tokens</li><li>max: 109 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 36.83 tokens</li><li>max: 113 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task235_iirc_question_from_subtext_answer_generation

* Dataset: task235_iirc_question_from_subtext_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 14 tokens</li><li>mean: 52.54 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 50.77 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 55.44 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1554_scitail_classification

* Dataset: task1554_scitail_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 16.72 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 25.6 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 24.39 tokens</li><li>max: 59 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task210_logic2text_structured_text_generation

* Dataset: task210_logic2text_structured_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                           | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | string                                                                             |
  | details | <ul><li>min: 13 tokens</li><li>mean: 31.83 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 30.89 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 32.76 tokens</li><li>max: 89 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task035_winogrande_question_modification_person

* Dataset: task035_winogrande_question_modification_person
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                             |
  | details | <ul><li>min: 31 tokens</li><li>mean: 36.24 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 35.8 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 35.46 tokens</li><li>max: 48 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task230_iirc_passage_classification

* Dataset: task230_iirc_passage_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 256 tokens</li><li>mean: 256.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 256 tokens</li><li>mean: 256.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 256 tokens</li><li>mean: 256.0 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1356_xlsum_title_generation

* Dataset: task1356_xlsum_title_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 59 tokens</li><li>mean: 239.39 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 58 tokens</li><li>mean: 241.03 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 64 tokens</li><li>mean: 248.12 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1726_mathqa_correct_answer_generation

* Dataset: task1726_mathqa_correct_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 43.95 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 42.44 tokens</li><li>max: 129 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 42.8 tokens</li><li>max: 133 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task302_record_classification

* Dataset: task302_record_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                             |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                               |
  | details | <ul><li>min: 194 tokens</li><li>mean: 253.52 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 198 tokens</li><li>mean: 252.98 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 195 tokens</li><li>mean: 252.9 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task380_boolq_yes_no_question

* Dataset: task380_boolq_yes_no_question
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 26 tokens</li><li>mean: 133.18 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 138.06 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 137.06 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task212_logic2text_classification

* Dataset: task212_logic2text_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 14 tokens</li><li>mean: 33.56 tokens</li><li>max: 146 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 32.24 tokens</li><li>max: 146 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 33.17 tokens</li><li>max: 127 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task748_glucose_reverse_cause_event_detection

* Dataset: task748_glucose_reverse_cause_event_detection
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                              |
  | details | <ul><li>min: 35 tokens</li><li>mean: 68.0 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 67.24 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 68.82 tokens</li><li>max: 105 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task834_mathdataset_classification

* Dataset: task834_mathdataset_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 27.89 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 28.2 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 27.11 tokens</li><li>max: 93 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task350_winomt_classification_gender_identifiability_pro

* Dataset: task350_winomt_classification_gender_identifiability_pro
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 16 tokens</li><li>mean: 21.8 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 21.62 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 21.81 tokens</li><li>max: 30 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task191_hotpotqa_question_generation

* Dataset: task191_hotpotqa_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                             |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                               |
  | details | <ul><li>min: 198 tokens</li><li>mean: 255.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 238 tokens</li><li>mean: 255.94 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 255 tokens</li><li>mean: 256.0 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task236_iirc_question_from_passage_answer_generation

* Dataset: task236_iirc_question_from_passage_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                             | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                               | string                                                                                |
  | details | <ul><li>min: 135 tokens</li><li>mean: 238.16 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 155 tokens</li><li>mean: 237.5 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 154 tokens</li><li>mean: 239.56 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task217_rocstories_ordering_answer_generation

* Dataset: task217_rocstories_ordering_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 45 tokens</li><li>mean: 72.48 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 72.44 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 71.11 tokens</li><li>max: 105 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task568_circa_question_generation

* Dataset: task568_circa_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                           |
  | details | <ul><li>min: 4 tokens</li><li>mean: 9.65 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.52 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.98 tokens</li><li>max: 20 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task614_glucose_cause_event_detection

* Dataset: task614_glucose_cause_event_detection
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                           | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | string                                                                              |
  | details | <ul><li>min: 39 tokens</li><li>mean: 67.94 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 67.3 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 68.61 tokens</li><li>max: 103 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task361_spolin_yesand_prompt_response_classification

* Dataset: task361_spolin_yesand_prompt_response_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                             |
  | details | <ul><li>min: 18 tokens</li><li>mean: 46.89 tokens</li><li>max: 137 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 46.11 tokens</li><li>max: 119 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 47.3 tokens</li><li>max: 128 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task421_persent_sentence_sentiment_classification

* Dataset: task421_persent_sentence_sentiment_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 22 tokens</li><li>mean: 67.26 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 70.21 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 72.11 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task203_mnli_sentence_generation

* Dataset: task203_mnli_sentence_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 14 tokens</li><li>mean: 38.83 tokens</li><li>max: 175 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 35.68 tokens</li><li>max: 175 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 33.77 tokens</li><li>max: 170 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task420_persent_document_sentiment_classification

* Dataset: task420_persent_document_sentiment_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 22 tokens</li><li>mean: 222.98 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 233.17 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 228.48 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task153_tomqa_find_location_hard_clean

* Dataset: task153_tomqa_find_location_hard_clean
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 39 tokens</li><li>mean: 161.63 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 160.81 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 164.26 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task346_hybridqa_classification

* Dataset: task346_hybridqa_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 18 tokens</li><li>mean: 32.85 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 32.03 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 31.88 tokens</li><li>max: 75 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1211_atomic_classification_hassubevent

* Dataset: task1211_atomic_classification_hassubevent
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 11 tokens</li><li>mean: 16.25 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 16.07 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 16.8 tokens</li><li>max: 29 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task360_spolin_yesand_response_generation

* Dataset: task360_spolin_yesand_response_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 22.68 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 21.02 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.67 tokens</li><li>max: 67 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task510_reddit_tifu_title_summarization

* Dataset: task510_reddit_tifu_title_summarization
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                             |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                               |
  | details | <ul><li>min: 9 tokens</li><li>mean: 216.21 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 218.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 221.49 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task511_reddit_tifu_long_text_summarization

* Dataset: task511_reddit_tifu_long_text_summarization
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 29 tokens</li><li>mean: 239.99 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 76 tokens</li><li>mean: 239.55 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 244.85 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task345_hybridqa_answer_generation

* Dataset: task345_hybridqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                            |
  | details | <ul><li>min: 9 tokens</li><li>mean: 22.24 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.66 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.97 tokens</li><li>max: 47 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task270_csrg_counterfactual_context_generation

* Dataset: task270_csrg_counterfactual_context_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                            | negative                                                                            |
  |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                              | string                                                                              |
  | details | <ul><li>min: 63 tokens</li><li>mean: 100.12 tokens</li><li>max: 158 tokens</li></ul> | <ul><li>min: 63 tokens</li><li>mean: 98.52 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 62 tokens</li><li>mean: 100.4 tokens</li><li>max: 141 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task307_jeopardy_answer_generation_final

* Dataset: task307_jeopardy_answer_generation_final
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 15 tokens</li><li>mean: 29.63 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 29.27 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 29.25 tokens</li><li>max: 43 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task001_quoref_question_generation

* Dataset: task001_quoref_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                              |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                                |
  | details | <ul><li>min: 201 tokens</li><li>mean: 255.1 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 99 tokens</li><li>mean: 254.46 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 173 tokens</li><li>mean: 255.11 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task089_swap_words_verification

* Dataset: task089_swap_words_verification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 9 tokens</li><li>mean: 12.91 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.67 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.26 tokens</li><li>max: 22 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1196_atomic_classification_oeffect

* Dataset: task1196_atomic_classification_oeffect
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 14 tokens</li><li>mean: 18.77 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 18.57 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 18.5 tokens</li><li>max: 29 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task080_piqa_answer_generation

* Dataset: task080_piqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 10.89 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.71 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.16 tokens</li><li>max: 26 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1598_nyc_long_text_generation

* Dataset: task1598_nyc_long_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                             |
  | details | <ul><li>min: 17 tokens</li><li>mean: 35.48 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 35.6 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 36.56 tokens</li><li>max: 55 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task240_tweetqa_question_generation

* Dataset: task240_tweetqa_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                             |
  | details | <ul><li>min: 27 tokens</li><li>mean: 51.19 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 50.8 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 51.63 tokens</li><li>max: 95 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task615_moviesqa_answer_generation

* Dataset: task615_moviesqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 11.44 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 11.45 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.41 tokens</li><li>max: 22 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1347_glue_sts-b_similarity_classification

* Dataset: task1347_glue_sts-b_similarity_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 17 tokens</li><li>mean: 31.16 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 31.12 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 31.04 tokens</li><li>max: 92 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task114_is_the_given_word_longest

* Dataset: task114_is_the_given_word_longest
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 25 tokens</li><li>mean: 28.95 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 28.46 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 28.75 tokens</li><li>max: 47 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task292_storycommonsense_character_text_generation

* Dataset: task292_storycommonsense_character_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 43 tokens</li><li>mean: 68.1 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 67.4 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 69.04 tokens</li><li>max: 96 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task115_help_advice_classification

* Dataset: task115_help_advice_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                          | negative                                                                           |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                             |
  | details | <ul><li>min: 2 tokens</li><li>mean: 19.9 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.14 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.28 tokens</li><li>max: 137 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task431_senteval_object_count

* Dataset: task431_senteval_object_count
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 16.75 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.14 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.78 tokens</li><li>max: 35 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1360_numer_sense_multiple_choice_qa_generation

* Dataset: task1360_numer_sense_multiple_choice_qa_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 32 tokens</li><li>mean: 40.58 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 40.28 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 40.2 tokens</li><li>max: 60 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task177_para-nmt_paraphrasing

* Dataset: task177_para-nmt_paraphrasing
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 19.73 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 18.88 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 18.29 tokens</li><li>max: 36 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task132_dais_text_modification

* Dataset: task132_dais_text_modification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                          | positive                                                                        | negative                                                                          |
  |:--------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                          | string                                                                          | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.3 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.1 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.14 tokens</li><li>max: 15 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task269_csrg_counterfactual_story_generation

* Dataset: task269_csrg_counterfactual_story_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 49 tokens</li><li>mean: 79.75 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>min: 53 tokens</li><li>mean: 79.41 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 79.46 tokens</li><li>max: 114 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task233_iirc_link_exists_classification

* Dataset: task233_iirc_link_exists_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 145 tokens</li><li>mean: 235.19 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 142 tokens</li><li>mean: 233.32 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 151 tokens</li><li>mean: 234.78 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task161_count_words_containing_letter

* Dataset: task161_count_words_containing_letter
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 27 tokens</li><li>mean: 31.0 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 30.83 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 30.52 tokens</li><li>max: 42 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1205_atomic_classification_isafter

* Dataset: task1205_atomic_classification_isafter
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 20.94 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 20.64 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 21.51 tokens</li><li>max: 37 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task571_recipe_nlg_ner_generation

* Dataset: task571_recipe_nlg_ner_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 5 tokens</li><li>mean: 117.62 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 117.51 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 109.25 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1292_yelp_review_full_text_categorization

* Dataset: task1292_yelp_review_full_text_categorization
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 4 tokens</li><li>mean: 135.37 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 144.75 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 145.27 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task428_senteval_inversion

* Dataset: task428_senteval_inversion
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 16.59 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 14.63 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.31 tokens</li><li>max: 34 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task311_race_question_generation

* Dataset: task311_race_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 115 tokens</li><li>mean: 254.55 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 137 tokens</li><li>mean: 254.56 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 171 tokens</li><li>mean: 255.54 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task429_senteval_tense

* Dataset: task429_senteval_tense
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 15.9 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.12 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.33 tokens</li><li>max: 36 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task403_creak_commonsense_inference

* Dataset: task403_creak_commonsense_inference
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                           | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | string                                                                              |
  | details | <ul><li>min: 13 tokens</li><li>mean: 30.04 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 29.3 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 29.47 tokens</li><li>max: 122 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task929_products_reviews_classification

* Dataset: task929_products_reviews_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 69.18 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 70.54 tokens</li><li>max: 123 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 70.28 tokens</li><li>max: 123 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task582_naturalquestion_answer_generation

* Dataset: task582_naturalquestion_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 11.64 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 11.72 tokens</li><li>max: 25 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task237_iirc_answer_from_subtext_answer_generation

* Dataset: task237_iirc_answer_from_subtext_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                             |
  | details | <ul><li>min: 22 tokens</li><li>mean: 66.47 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 64.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 61.4 tokens</li><li>max: 161 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task050_multirc_answerability

* Dataset: task050_multirc_answerability
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                           | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | string                                                                              |
  | details | <ul><li>min: 15 tokens</li><li>mean: 32.35 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 31.51 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 32.03 tokens</li><li>max: 159 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task184_break_generate_question

* Dataset: task184_break_generate_question
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 13 tokens</li><li>mean: 39.76 tokens</li><li>max: 147 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 38.97 tokens</li><li>max: 149 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 39.62 tokens</li><li>max: 148 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task669_ambigqa_answer_generation

* Dataset: task669_ambigqa_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 12.91 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 12.88 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 12.72 tokens</li><li>max: 22 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task169_strategyqa_sentence_generation

* Dataset: task169_strategyqa_sentence_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 19 tokens</li><li>mean: 35.3 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 34.36 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 33.36 tokens</li><li>max: 65 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task500_scruples_anecdotes_title_generation

* Dataset: task500_scruples_anecdotes_title_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                            |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                              |
  | details | <ul><li>min: 14 tokens</li><li>mean: 224.51 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 232.39 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 234.4 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task241_tweetqa_classification

* Dataset: task241_tweetqa_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                             |
  | details | <ul><li>min: 31 tokens</li><li>mean: 61.75 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 61.98 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 61.67 tokens</li><li>max: 92 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1345_glue_qqp_question_paraprashing

* Dataset: task1345_glue_qqp_question_paraprashing
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 16.62 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.77 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.61 tokens</li><li>max: 51 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task218_rocstories_swap_order_answer_generation

* Dataset: task218_rocstories_swap_order_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 48 tokens</li><li>mean: 72.42 tokens</li><li>max: 118 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 72.62 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 72.14 tokens</li><li>max: 106 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task613_politifact_text_generation

* Dataset: task613_politifact_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 24.71 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 23.58 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 22.87 tokens</li><li>max: 61 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1167_penn_treebank_coarse_pos_tagging

* Dataset: task1167_penn_treebank_coarse_pos_tagging
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 16 tokens</li><li>mean: 53.81 tokens</li><li>max: 200 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 53.49 tokens</li><li>max: 220 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 54.95 tokens</li><li>max: 202 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1422_mathqa_physics

* Dataset: task1422_mathqa_physics
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 34 tokens</li><li>mean: 72.14 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 71.53 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 72.08 tokens</li><li>max: 155 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task247_dream_answer_generation

* Dataset: task247_dream_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                             | negative                                                                             |
  |:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                               | string                                                                               |
  | details | <ul><li>min: 38 tokens</li><li>mean: 159.4 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 157.79 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 41 tokens</li><li>mean: 167.32 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task199_mnli_classification

* Dataset: task199_mnli_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 13 tokens</li><li>mean: 43.33 tokens</li><li>max: 127 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 44.68 tokens</li><li>max: 149 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 44.31 tokens</li><li>max: 113 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task164_mcscript_question_answering_text

* Dataset: task164_mcscript_question_answering_text
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 150 tokens</li><li>mean: 200.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 150 tokens</li><li>mean: 200.46 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 142 tokens</li><li>mean: 200.89 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1541_agnews_classification

* Dataset: task1541_agnews_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 21 tokens</li><li>mean: 53.39 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 52.89 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 53.84 tokens</li><li>max: 161 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task516_senteval_conjoints_inversion

* Dataset: task516_senteval_conjoints_inversion
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 20.31 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.97 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.91 tokens</li><li>max: 34 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task294_storycommonsense_motiv_text_generation

* Dataset: task294_storycommonsense_motiv_text_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 40.09 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 40.44 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 39.58 tokens</li><li>max: 86 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task501_scruples_anecdotes_post_type_verification

* Dataset: task501_scruples_anecdotes_post_type_verification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 18 tokens</li><li>mean: 231.44 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 235.23 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 234.84 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task213_rocstories_correct_ending_classification

* Dataset: task213_rocstories_correct_ending_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 62 tokens</li><li>mean: 86.03 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 60 tokens</li><li>mean: 85.66 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 59 tokens</li><li>mean: 86.01 tokens</li><li>max: 131 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task821_protoqa_question_generation

* Dataset: task821_protoqa_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 14.61 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.97 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.79 tokens</li><li>max: 93 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task493_review_polarity_classification

* Dataset: task493_review_polarity_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                             | negative                                                                             |
  |:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                               | string                                                                               |
  | details | <ul><li>min: 18 tokens</li><li>mean: 99.85 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 104.97 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 112.97 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task308_jeopardy_answer_generation_all

* Dataset: task308_jeopardy_answer_generation_all
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                            |
  | details | <ul><li>min: 12 tokens</li><li>mean: 27.97 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 27.0 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 27.52 tokens</li><li>max: 48 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1595_event2mind_text_generation_1

* Dataset: task1595_event2mind_text_generation_1
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                          | positive                                                                         | negative                                                                          |
  |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                          | string                                                                           | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.9 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.96 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.03 tokens</li><li>max: 20 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task040_qasc_question_generation

* Dataset: task040_qasc_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 15.03 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.04 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 13.79 tokens</li><li>max: 32 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task231_iirc_link_classification

* Dataset: task231_iirc_link_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 179 tokens</li><li>mean: 246.14 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 170 tokens</li><li>mean: 246.33 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 161 tokens</li><li>mean: 246.99 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1727_wiqa_what_is_the_effect

* Dataset: task1727_wiqa_what_is_the_effect
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                           | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | string                                                                              |
  | details | <ul><li>min: 44 tokens</li><li>mean: 95.04 tokens</li><li>max: 183 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 95.1 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 95.37 tokens</li><li>max: 183 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task578_curiosity_dialogs_answer_generation

* Dataset: task578_curiosity_dialogs_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                              | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                                | string                                                                               |
  | details | <ul><li>min: 10 tokens</li><li>mean: 230.36 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 118 tokens</li><li>mean: 235.58 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 229.92 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task310_race_classification

* Dataset: task310_race_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 101 tokens</li><li>mean: 254.92 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 218 tokens</li><li>mean: 255.81 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 101 tokens</li><li>mean: 254.92 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task309_race_answer_generation

* Dataset: task309_race_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                              | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                                | string                                                                               |
  | details | <ul><li>min: 75 tokens</li><li>mean: 254.76 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 204 tokens</li><li>mean: 255.48 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 75 tokens</li><li>mean: 255.23 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task379_agnews_topic_classification

* Dataset: task379_agnews_topic_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 20 tokens</li><li>mean: 54.44 tokens</li><li>max: 193 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 54.58 tokens</li><li>max: 175 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 55.12 tokens</li><li>max: 187 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task030_winogrande_full_person

* Dataset: task030_winogrande_full_person
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 7.63 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.52 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.39 tokens</li><li>max: 11 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1540_parsed_pdfs_summarization

* Dataset: task1540_parsed_pdfs_summarization
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                             | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               | string                                                                              |
  | details | <ul><li>min: 3 tokens</li><li>mean: 188.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 189.34 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 192.03 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task039_qasc_find_overlapping_words

* Dataset: task039_qasc_find_overlapping_words
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 16 tokens</li><li>mean: 30.57 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 30.03 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 30.68 tokens</li><li>max: 60 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1206_atomic_classification_isbefore

* Dataset: task1206_atomic_classification_isbefore
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 21.27 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 20.85 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 21.37 tokens</li><li>max: 31 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task157_count_vowels_and_consonants

* Dataset: task157_count_vowels_and_consonants
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 24 tokens</li><li>mean: 27.98 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 27.87 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 28.32 tokens</li><li>max: 39 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task339_record_answer_generation

* Dataset: task339_record_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 171 tokens</li><li>mean: 234.55 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 171 tokens</li><li>mean: 233.87 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 171 tokens</li><li>mean: 232.63 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task453_swag_answer_generation

* Dataset: task453_swag_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 9 tokens</li><li>mean: 18.38 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 18.13 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 17.47 tokens</li><li>max: 55 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task848_pubmedqa_classification

* Dataset: task848_pubmedqa_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 21 tokens</li><li>mean: 249.24 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 249.85 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 84 tokens</li><li>mean: 251.72 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task673_google_wellformed_query_classification

* Dataset: task673_google_wellformed_query_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 11.6 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.2 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.37 tokens</li><li>max: 22 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task676_ollie_relationship_answer_generation

* Dataset: task676_ollie_relationship_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 29 tokens</li><li>mean: 50.98 tokens</li><li>max: 113 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 48.82 tokens</li><li>max: 134 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 51.69 tokens</li><li>max: 113 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task268_casehold_legal_answer_generation

* Dataset: task268_casehold_legal_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                             | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                               | string                                                                                |
  | details | <ul><li>min: 235 tokens</li><li>mean: 255.94 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 156 tokens</li><li>mean: 255.5 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 226 tokens</li><li>mean: 255.95 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task844_financial_phrasebank_classification

* Dataset: task844_financial_phrasebank_classification
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 40.06 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 38.31 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 38.91 tokens</li><li>max: 86 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task330_gap_answer_generation

* Dataset: task330_gap_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                               | positive                                                                            | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                              | string                                                                               |
  | details | <ul><li>min: 26 tokens</li><li>mean: 107.15 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 108.5 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 111.29 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task595_mocha_answer_generation

* Dataset: task595_mocha_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                             |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                               |
  | details | <ul><li>min: 44 tokens</li><li>mean: 94.29 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 95.79 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 117.82 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task1285_kpa_keypoint_matching

* Dataset: task1285_kpa_keypoint_matching
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 30 tokens</li><li>mean: 52.19 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 50.09 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 53.0 tokens</li><li>max: 88 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task234_iirc_passage_line_answer_generation

* Dataset: task234_iirc_passage_line_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                                | positive                                                                              | negative                                                                              |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | string                                                                                |
  | details | <ul><li>min: 143 tokens</li><li>mean: 234.48 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 155 tokens</li><li>mean: 235.32 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 146 tokens</li><li>mean: 236.21 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task494_review_polarity_answer_generation

* Dataset: task494_review_polarity_answer_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                              | positive                                                                             | negative                                                                             |
  |:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                               | string                                                                               |
  | details | <ul><li>min: 3 tokens</li><li>mean: 107.59 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 114.18 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 114.95 tokens</li><li>max: 249 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task670_ambigqa_question_generation

* Dataset: task670_ambigqa_question_generation
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 11 tokens</li><li>mean: 12.7 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 12.46 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 12.24 tokens</li><li>max: 18 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### task289_gigaword_summarization

* Dataset: task289_gigaword_summarization
* Size: 634 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 634 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 25 tokens</li><li>mean: 51.28 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 51.71 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 51.14 tokens</li><li>max: 87 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### npr

* Dataset: npr
* Size: 24,774 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                             | negative                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | string                                                                               |
  | details | <ul><li>min: 4 tokens</li><li>mean: 12.18 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 146.68 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 109.65 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### nli

* Dataset: nli
* Size: 49,548 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 21.0 tokens</li><li>max: 229 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.74 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.98 tokens</li><li>max: 45 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### SimpleWiki

* Dataset: SimpleWiki
* Size: 5,006 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 8 tokens</li><li>mean: 29.27 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 33.55 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 55.34 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### amazon_review_2018

* Dataset: amazon_review_2018
* Size: 99,032 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                            | negative                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              | string                                                                              |
  | details | <ul><li>min: 4 tokens</li><li>mean: 11.29 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 87.93 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 69.37 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### ccnews_title_text

* Dataset: ccnews_title_text
* Size: 24,774 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                             | negative                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | string                                                                               |
  | details | <ul><li>min: 6 tokens</li><li>mean: 15.71 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 209.36 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 197.52 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### agnews

* Dataset: agnews
* Size: 44,606 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                              |
  | details | <ul><li>min: 5 tokens</li><li>mean: 11.84 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 40.9 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 44.47 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### xsum

* Dataset: xsum
* Size: 9,948 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                             | negative                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | string                                                                               |
  | details | <ul><li>min: 8 tokens</li><li>mean: 27.96 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 227.43 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 229.78 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### msmarco

* Dataset: msmarco
* Size: 173,290 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                            | negative                                                                            |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              | string                                                                              |
  | details | <ul><li>min: 4 tokens</li><li>mean: 9.09 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 82.25 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 79.69 tokens</li><li>max: 220 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### yahoo_answers_title_answer

* Dataset: yahoo_answers_title_answer
* Size: 24,774 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                           | negative                                                                            |
  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                             | string                                                                              |
  | details | <ul><li>min: 6 tokens</li><li>mean: 16.8 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 78.53 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 87.35 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### squad_pairs

* Dataset: squad_pairs
* Size: 24,774 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                             | negative                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | string                                                                               |
  | details | <ul><li>min: 4 tokens</li><li>mean: 14.48 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 152.39 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 160.54 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### wow

* Dataset: wow
* Size: 29,716 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                             | negative                                                                             |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               | string                                                                               |
  | details | <ul><li>min: 5 tokens</li><li>mean: 90.07 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 111.81 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 92 tokens</li><li>mean: 113.15 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-amazon_counterfactual-avs_triplets

* Dataset: mteb-amazon_counterfactual-avs_triplets
* Size: 3,991 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                              | positive                                                                           | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | string                                                                             |
  | details | <ul><li>min: 12 tokens</li><li>mean: 27.26 tokens</li><li>max: 137 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 26.57 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 26.88 tokens</li><li>max: 96 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-amazon_massive_intent-avs_triplets

* Dataset: mteb-amazon_massive_intent-avs_triplets
* Size: 11,405 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                           |
  | details | <ul><li>min: 3 tokens</li><li>mean: 9.49 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.19 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.49 tokens</li><li>max: 25 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-amazon_massive_scenario-avs_triplets

* Dataset: mteb-amazon_massive_scenario-avs_triplets
* Size: 11,405 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                           |
  | details | <ul><li>min: 3 tokens</li><li>mean: 9.59 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.97 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.69 tokens</li><li>max: 29 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-amazon_reviews_multi-avs_triplets

* Dataset: mteb-amazon_reviews_multi-avs_triplets
* Size: 198,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 49.83 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 51.32 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 49.66 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-banking77-avs_triplets

* Dataset: mteb-banking77-avs_triplets
* Size: 9,947 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 16.19 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.76 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.78 tokens</li><li>max: 87 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-emotion-avs_triplets

* Dataset: mteb-emotion-avs_triplets
* Size: 15,840 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 21.76 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.13 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 21.95 tokens</li><li>max: 65 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-imdb-avs_triplets

* Dataset: mteb-imdb-avs_triplets
* Size: 24,647 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                               | positive                                                                             | negative                                                                             |
  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                               | string                                                                               |
  | details | <ul><li>min: 34 tokens</li><li>mean: 207.65 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 57 tokens</li><li>mean: 222.57 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 207.98 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-mtop_domain-avs_triplets

* Dataset: mteb-mtop_domain-avs_triplets
* Size: 15,523 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                        | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                          | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 10.29 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.7 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.01 tokens</li><li>max: 28 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-mtop_intent-avs_triplets

* Dataset: mteb-mtop_intent-avs_triplets
* Size: 15,523 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 10.11 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.64 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.13 tokens</li><li>max: 33 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-toxic_conversations_50k-avs_triplets

* Dataset: mteb-toxic_conversations_50k-avs_triplets
* Size: 49,421 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 68.39 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 91.3 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 70.1 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### mteb-tweet_sentiment_extraction-avs_triplets

* Dataset: mteb-tweet_sentiment_extraction-avs_triplets
* Size: 27,245 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 20.32 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 20.24 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 20.98 tokens</li><li>max: 51 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### covid-bing-query-gpt4-avs_triplets

* Dataset: covid-bing-query-gpt4-avs_triplets
* Size: 4,942 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                            | negative                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              | string                                                                              |
  | details | <ul><li>min: 6 tokens</li><li>mean: 15.22 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 37.46 tokens</li><li>max: 167 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 37.77 tokens</li><li>max: 128 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 18,269 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                            | negative                                                                            |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              | string                                                                              |
  | details | <ul><li>min: 5 tokens</li><li>mean: 15.5 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 143.45 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 145.01 tokens</li><li>max: 256 tokens</li></ul> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `fp16`: True
- `gradient_checkpointing`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: True
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step   | Training Loss | Validation Loss | medi-mteb-dev_cosine_accuracy |
|:------:|:------:|:-------------:|:---------------:|:-----------------------------:|
| 0      | 0      | -             | -               | 0.8503                        |
| 0.0175 | 500    | 1.9411        | 1.9039          | 0.8588                        |
| 0.0351 | 1000   | 1.5495        | 0.9698          | 0.8698                        |
| 0.0526 | 1500   | 1.3527        | 0.7684          | 0.8753                        |
| 0.0701 | 2000   | 1.1995        | 0.7102          | 0.8777                        |
| 0.0877 | 2500   | 1.1782        | 0.6829          | 0.8793                        |
| 0.1052 | 3000   | 1.1662        | 0.6633          | 0.8830                        |
| 0.1227 | 3500   | 1.139         | 0.6510          | 0.8844                        |
| 0.1403 | 4000   | 1.1389        | 0.6429          | 0.8851                        |
| 0.1578 | 4500   | 1.1381        | 0.6273          | 0.8863                        |
| 0.1753 | 5000   | 1.0616        | 0.6225          | 0.8869                        |
| 0.1929 | 5500   | 1.114         | 0.6169          | 0.8872                        |
| 0.2104 | 6000   | 0.9854        | 0.6108          | 0.8886                        |
| 0.2279 | 6500   | 1.081         | 0.6047          | 0.8900                        |
| 0.2455 | 7000   | 0.9899        | 0.5983          | 0.8912                        |
| 0.2630 | 7500   | 1.0551        | 0.5931          | 0.8921                        |
| 0.2805 | 8000   | 1.0515        | 0.5882          | 0.8930                        |
| 0.2981 | 8500   | 1.0384        | 0.5768          | 0.8946                        |
| 0.3156 | 9000   | 1.0545        | 0.5716          | 0.8945                        |
| 0.3331 | 9500   | 1.006         | 0.5744          | 0.8959                        |
| 0.3507 | 10000  | 0.9629        | 0.5719          | 0.8960                        |
| 0.3682 | 10500  | 1.0877        | 0.5600          | 0.8958                        |
| 0.3857 | 11000  | 1.0594        | 0.5639          | 0.8975                        |
| 0.4033 | 11500  | 1.0708        | 0.5672          | 0.8975                        |
| 0.4208 | 12000  | 1.0275        | 0.5481          | 0.8986                        |
| 0.4383 | 12500  | 0.9467        | 0.5552          | 0.9007                        |
| 0.4559 | 13000  | 1.0048        | 0.5524          | 0.9008                        |
| 0.4734 | 13500  | 1.0135        | 0.5482          | 0.9002                        |
| 0.4909 | 14000  | 0.9579        | 0.5428          | 0.9002                        |
| 0.5085 | 14500  | 0.9534        | 0.5373          | 0.9015                        |
| 0.5260 | 15000  | 0.9225        | 0.5347          | 0.9025                        |
| 0.5435 | 15500  | 0.9936        | 0.5384          | 0.9011                        |
| 0.5611 | 16000  | 0.926         | 0.5298          | 0.9028                        |
| 0.5786 | 16500  | 0.9904        | 0.5338          | 0.9034                        |
| 0.5961 | 17000  | 0.9302        | 0.5281          | 0.9033                        |
| 0.6137 | 17500  | 0.908         | 0.5332          | 0.9025                        |
| 0.6312 | 18000  | 0.8936        | 0.5322          | 0.9046                        |
| 0.6487 | 18500  | 0.9549        | 0.5312          | 0.9039                        |
| 0.6663 | 19000  | 0.9498        | 0.5319          | 0.9030                        |
| 0.6838 | 19500  | 0.9291        | 0.5279          | 0.9038                        |
| 0.7013 | 20000  | 0.9573        | 0.5165          | 0.9017                        |
| 0.7189 | 20500  | 0.9395        | 0.5223          | 0.9039                        |
| 0.7364 | 21000  | 0.8753        | 0.5335          | 0.9009                        |
| 0.7539 | 21500  | 0.95          | 0.5173          | 0.9040                        |
| 0.7715 | 22000  | 0.9656        | 0.5451          | 0.9043                        |
| 0.7890 | 22500  | 0.9145        | 0.5305          | 0.9033                        |
| 0.8065 | 23000  | 0.9768        | 0.5135          | 0.9041                        |
| 0.8241 | 23500  | 0.8779        | 0.5185          | 0.9037                        |
| 0.8416 | 24000  | 0.9603        | 0.5338          | 0.9036                        |
| 0.8591 | 24500  | 0.9045        | 0.5090          | 0.9056                        |
| 0.8767 | 25000  | 0.9536        | 0.5254          | 0.9043                        |
| 0.8942 | 25500  | 0.8499        | 0.5388          | 0.9023                        |
| 0.9117 | 26000  | 0.88          | 0.5676          | 0.9011                        |
| 0.9293 | 26500  | 0.8884        | 0.5127          | 0.9046                        |
| 0.9468 | 27000  | 0.8556        | 0.5227          | 0.9065                        |
| 0.9643 | 27500  | 0.8641        | 0.5901          | 0.9027                        |
| 0.9819 | 28000  | 0.8884        | 0.4982          | 0.9054                        |
| 0.9994 | 28500  | 0.8404        | 0.5078          | 0.9064                        |
| 1.0169 | 29000  | 0.8613        | 0.5211          | 0.9052                        |
| 1.0345 | 29500  | 0.8971        | 0.5061          | 0.9065                        |
| 1.0520 | 30000  | 0.9426        | 0.5118          | 0.9062                        |
| 1.0695 | 30500  | 0.8791        | 0.5062          | 0.9062                        |
| 1.0871 | 31000  | 0.8953        | 0.5056          | 0.9044                        |
| 1.1046 | 31500  | 0.9229        | 0.5002          | 0.9065                        |
| 1.1221 | 32000  | 0.8914        | 0.4912          | 0.9088                        |
| 1.1397 | 32500  | 0.9105        | 0.4973          | 0.9086                        |
| 1.1572 | 33000  | 0.9168        | 0.4954          | 0.9074                        |
| 1.1747 | 33500  | 0.845         | 0.5073          | 0.9088                        |
| 1.1923 | 34000  | 0.9209        | 0.4890          | 0.9088                        |
| 1.2098 | 34500  | 0.8014        | 0.5063          | 0.9063                        |
| 1.2273 | 35000  | 0.8888        | 0.5270          | 0.9070                        |
| 1.2449 | 35500  | 0.8269        | 0.5062          | 0.9059                        |
| 1.2624 | 36000  | 0.8637        | 0.4951          | 0.9054                        |
| 1.2799 | 36500  | 0.8796        | 0.4922          | 0.9083                        |
| 1.2975 | 37000  | 0.8644        | 0.4851          | 0.9068                        |
| 1.3150 | 37500  | 0.8907        | 0.5396          | 0.9069                        |
| 1.3325 | 38000  | 0.8477        | 0.4944          | 0.9082                        |
| 1.3501 | 38500  | 0.8237        | 0.4915          | 0.9081                        |
| 1.3676 | 39000  | 0.9217        | 0.4918          | 0.9083                        |
| 1.3851 | 39500  | 0.887         | 0.4955          | 0.9064                        |
| 1.4027 | 40000  | 0.9172        | 0.5259          | 0.9077                        |
| 1.4202 | 40500  | 0.8693        | 0.5002          | 0.9092                        |
| 1.4377 | 41000  | 0.8223        | 0.5109          | 0.9084                        |
| 1.4553 | 41500  | 0.8554        | 0.4859          | 0.9079                        |
| 1.4728 | 42000  | 0.8772        | 0.4850          | 0.9079                        |
| 1.4903 | 42500  | 0.8232        | 0.4860          | 0.9088                        |
| 1.5079 | 43000  | 0.8218        | 0.4917          | 0.9083                        |
| 1.5254 | 43500  | 0.7905        | 0.4839          | 0.9094                        |
| 1.5429 | 44000  | 0.847         | 0.5150          | 0.9081                        |
| 1.5605 | 44500  | 0.7929        | 0.5234          | 0.9082                        |
| 1.5780 | 45000  | 0.8621        | 0.5084          | 0.9094                        |
| 1.5955 | 45500  | 0.7908        | 0.4980          | 0.9092                        |
| 1.6131 | 46000  | 0.792         | 0.5385          | 0.9071                        |
| 1.6306 | 46500  | 0.7569        | 0.5405          | 0.9088                        |
| 1.6481 | 47000  | 0.8178        | 0.5172          | 0.9078                        |
| 1.6657 | 47500  | 0.8101        | 0.5379          | 0.9082                        |
| 1.6832 | 48000  | 0.8013        | 0.5627          | 0.9068                        |
| 1.7007 | 48500  | 0.8298        | 0.5947          | 0.9072                        |
| 1.7183 | 49000  | 0.8028        | 0.5302          | 0.9076                        |
| 1.7358 | 49500  | 0.7663        | 0.5523          | 0.9066                        |
| 1.7533 | 50000  | 0.8255        | 0.5361          | 0.9080                        |
| 1.7709 | 50500  | 0.8354        | 0.5373          | 0.9080                        |
| 1.7884 | 51000  | 0.7917        | 0.5546          | 0.9079                        |
| 1.8059 | 51500  | 0.837         | 0.5113          | 0.9085                        |
| 1.8235 | 52000  | 0.7488        | 0.5037          | 0.9082                        |
| 1.8410 | 52500  | 0.8439        | 0.5349          | 0.9084                        |
| 1.8585 | 53000  | 0.7688        | 0.5279          | 0.9083                        |
| 1.8761 | 53500  | 0.8205        | 0.5496          | 0.9071                        |
| 1.8936 | 54000  | 0.7256        | 0.5454          | 0.9075                        |
| 1.9111 | 54500  | 0.7536        | 0.5582          | 0.9060                        |
| 1.9287 | 55000  | 0.7544        | 0.5331          | 0.9075                        |
| 1.9462 | 55500  | 0.7332        | 0.5139          | 0.9091                        |
| 1.9637 | 56000  | 0.7244        | 0.5767          | 0.9078                        |
| 1.9813 | 56500  | 0.7574        | 0.4962          | 0.9084                        |
| 1.9988 | 57000  | 0.7116        | 0.5210          | 0.9090                        |
| 2.0163 | 57500  | 0.7376        | 0.5196          | 0.9088                        |
| 2.0339 | 58000  | 0.768         | 0.5609          | 0.9086                        |
| 2.0514 | 58500  | 0.8056        | 0.5230          | 0.9081                        |
| 2.0689 | 59000  | 0.7744        | 0.5527          | 0.9077                        |
| 2.0865 | 59500  | 0.7543        | 0.4949          | 0.9090                        |
| 2.1040 | 60000  | 0.8           | 0.4925          | 0.9095                        |
| 2.1215 | 60500  | 0.7664        | 0.4989          | 0.9093                        |
| 2.1391 | 61000  | 0.7849        | 0.4956          | 0.9106                        |
| 2.1566 | 61500  | 0.7955        | 0.5312          | 0.9099                        |
| 2.1741 | 62000  | 0.7326        | 0.5126          | 0.9112                        |
| 2.1917 | 62500  | 0.7975        | 0.4701          | 0.9114                        |
| 2.2092 | 63000  | 0.7001        | 0.5118          | 0.9093                        |
| 2.2267 | 63500  | 0.7477        | 0.5371          | 0.9102                        |
| 2.2443 | 64000  | 0.7227        | 0.5536          | 0.9083                        |
| 2.2618 | 64500  | 0.7687        | 0.5174          | 0.9102                        |
| 2.2793 | 65000  | 0.7633        | 0.4925          | 0.9102                        |
| 2.2969 | 65500  | 0.7572        | 0.5059          | 0.9093                        |
| 2.3144 | 66000  | 0.7846        | 0.5391          | 0.9088                        |
| 2.3319 | 66500  | 0.7434        | 0.4991          | 0.9111                        |
| 2.3495 | 67000  | 0.7124        | 0.5115          | 0.9107                        |
| 2.3670 | 67500  | 0.8085        | 0.4974          | 0.9086                        |
| 2.3845 | 68000  | 0.7879        | 0.5114          | 0.9089                        |
| 2.4021 | 68500  | 0.7977        | 0.5297          | 0.9086                        |
| 2.4196 | 69000  | 0.782         | 0.5251          | 0.9103                        |
| 2.4371 | 69500  | 0.7237        | 0.5568          | 0.9088                        |
| 2.4547 | 70000  | 0.7556        | 0.5008          | 0.9098                        |
| 2.4722 | 70500  | 0.777         | 0.4784          | 0.9097                        |
| 2.4897 | 71000  | 0.7205        | 0.4993          | 0.9097                        |
| 2.5073 | 71500  | 0.7237        | 0.5096          | 0.9102                        |
| 2.5248 | 72000  | 0.6976        | 0.4833          | 0.9107                        |
| 2.5423 | 72500  | 0.7572        | 0.5234          | 0.9092                        |
| 2.5599 | 73000  | 0.7012        | 0.5339          | 0.9096                        |
| 2.5774 | 73500  | 0.7799        | 0.5056          | 0.9107                        |
| 2.5949 | 74000  | 0.7036        | 0.4961          | 0.9101                        |
| 2.6125 | 74500  | 0.6932        | 0.5656          | 0.9088                        |
| 2.6300 | 75000  | 0.6676        | 0.5347          | 0.9097                        |
| 2.6475 | 75500  | 0.7246        | 0.5110          | 0.9101                        |
| 2.6651 | 76000  | 0.715         | 0.5551          | 0.9096                        |
| 2.6826 | 76500  | 0.7298        | 0.5658          | 0.9106                        |
| 2.7001 | 77000  | 0.7349        | 0.5571          | 0.9106                        |
| 2.7177 | 77500  | 0.721         | 0.5667          | 0.9100                        |
| 2.7352 | 78000  | 0.6863        | 0.5616          | 0.9066                        |
| 2.7527 | 78500  | 0.739         | 0.5419          | 0.9101                        |
| 2.7703 | 79000  | 0.7529        | 0.5343          | 0.9107                        |
| 2.7878 | 79500  | 0.7008        | 0.5601          | 0.9107                        |
| 2.8053 | 80000  | 0.7655        | 0.5189          | 0.9097                        |
| 2.8229 | 80500  | 0.6666        | 0.5073          | 0.9106                        |
| 2.8404 | 81000  | 0.7551        | 0.5381          | 0.9102                        |
| 2.8579 | 81500  | 0.6769        | 0.5650          | 0.9092                        |
| 2.8755 | 82000  | 0.7508        | 0.5189          | 0.9097                        |
| 2.8930 | 82500  | 0.6418        | 0.5521          | 0.9094                        |
| 2.9105 | 83000  | 0.6808        | 0.5490          | 0.9095                        |
| 2.9281 | 83500  | 0.6833        | 0.5524          | 0.9092                        |
| 2.9456 | 84000  | 0.6508        | 0.5229          | 0.9105                        |
| 2.9631 | 84500  | 0.6576        | 0.5789          | 0.9100                        |
| 2.9807 | 85000  | 0.6778        | 0.5075          | 0.9108                        |
| 2.9982 | 85500  | 0.642         | 0.5139          | 0.9107                        |
| 3.0157 | 86000  | 0.6596        | 0.5337          | 0.9104                        |
| 3.0333 | 86500  | 0.6769        | 0.5713          | 0.9106                        |
| 3.0508 | 87000  | 0.7349        | 0.5374          | 0.9103                        |
| 3.0683 | 87500  | 0.7034        | 0.5680          | 0.9094                        |
| 3.0859 | 88000  | 0.6853        | 0.5130          | 0.9106                        |
| 3.1034 | 88500  | 0.726         | 0.5093          | 0.9123                        |
| 3.1209 | 89000  | 0.6939        | 0.5078          | 0.9104                        |
| 3.1385 | 89500  | 0.7085        | 0.4847          | 0.9125                        |
| 3.1560 | 90000  | 0.7118        | 0.5154          | 0.9113                        |
| 3.1735 | 90500  | 0.6755        | 0.5066          | 0.9121                        |
| 3.1911 | 91000  | 0.718         | 0.4665          | 0.9129                        |
| 3.2086 | 91500  | 0.6277        | 0.5047          | 0.9111                        |
| 3.2261 | 92000  | 0.6907        | 0.5292          | 0.9123                        |
| 3.2437 | 92500  | 0.6624        | 0.5414          | 0.9103                        |
| 3.2612 | 93000  | 0.6943        | 0.5274          | 0.9101                        |
| 3.2787 | 93500  | 0.6979        | 0.4985          | 0.9110                        |
| 3.2963 | 94000  | 0.6858        | 0.5156          | 0.9099                        |
| 3.3138 | 94500  | 0.7221        | 0.5062          | 0.9114                        |
| 3.3313 | 95000  | 0.6647        | 0.5129          | 0.9108                        |
| 3.3489 | 95500  | 0.6572        | 0.5213          | 0.9127                        |
| 3.3664 | 96000  | 0.7417        | 0.4926          | 0.9119                        |
| 3.3839 | 96500  | 0.7237        | 0.5090          | 0.9104                        |
| 3.4015 | 97000  | 0.7218        | 0.5336          | 0.9111                        |
| 3.4190 | 97500  | 0.7091        | 0.5062          | 0.9128                        |
| 3.4365 | 98000  | 0.668         | 0.5727          | 0.9118                        |
| 3.4541 | 98500  | 0.6724        | 0.5106          | 0.9119                        |
| 3.4716 | 99000  | 0.7331        | 0.4740          | 0.9130                        |
| 3.4891 | 99500  | 0.6427        | 0.5021          | 0.9119                        |
| 3.5067 | 100000 | 0.6659        | 0.5037          | 0.9119                        |
| 3.5242 | 100500 | 0.6413        | 0.5024          | 0.9109                        |
| 3.5417 | 101000 | 0.6889        | 0.5277          | 0.9109                        |
| 3.5593 | 101500 | 0.6401        | 0.5389          | 0.9103                        |
| 3.5768 | 102000 | 0.7116        | 0.5114          | 0.9111                        |
| 3.5943 | 102500 | 0.6511        | 0.5124          | 0.9112                        |
| 3.6119 | 103000 | 0.6392        | 0.5505          | 0.9096                        |
| 3.6294 | 103500 | 0.6049        | 0.5306          | 0.9099                        |
| 3.6469 | 104000 | 0.675         | 0.5219          | 0.9098                        |
| 3.6645 | 104500 | 0.6498        | 0.5392          | 0.9100                        |
| 3.6820 | 105000 | 0.6774        | 0.5609          | 0.9097                        |
| 3.6995 | 105500 | 0.6655        | 0.5441          | 0.9107                        |
| 3.7171 | 106000 | 0.6664        | 0.5713          | 0.9113                        |
| 3.7346 | 106500 | 0.6343        | 0.5742          | 0.9086                        |
| 3.7521 | 107000 | 0.6686        | 0.5225          | 0.9113                        |
| 3.7697 | 107500 | 0.7018        | 0.5221          | 0.9111                        |
| 3.7872 | 108000 | 0.6479        | 0.5641          | 0.9113                        |
| 3.8047 | 108500 | 0.7005        | 0.5352          | 0.9123                        |
| 3.8223 | 109000 | 0.6068        | 0.5007          | 0.9107                        |
| 3.8398 | 109500 | 0.6846        | 0.5593          | 0.9102                        |
| 3.8573 | 110000 | 0.6272        | 0.5458          | 0.9107                        |
| 3.8749 | 110500 | 0.685         | 0.5178          | 0.9100                        |
| 3.8924 | 111000 | 0.5992        | 0.5200          | 0.9102                        |
| 3.9099 | 111500 | 0.6231        | 0.5488          | 0.9101                        |
| 3.9275 | 112000 | 0.6343        | 0.5496          | 0.9100                        |
| 3.9450 | 112500 | 0.593         | 0.5207          | 0.9115                        |
| 3.9625 | 113000 | 0.6017        | 0.5679          | 0.9108                        |
| 3.9801 | 113500 | 0.6218        | 0.5174          | 0.9113                        |
| 3.9976 | 114000 | 0.5916        | 0.5108          | 0.9118                        |
| 4.0151 | 114500 | 0.603         | 0.5259          | 0.9117                        |
| 4.0327 | 115000 | 0.6215        | 0.5362          | 0.9121                        |
| 4.0502 | 115500 | 0.6784        | 0.5343          | 0.9112                        |
| 4.0677 | 116000 | 0.65          | 0.5488          | 0.9114                        |
| 4.0853 | 116500 | 0.632         | 0.4905          | 0.9119                        |
| 4.1028 | 117000 | 0.6708        | 0.5091          | 0.9129                        |
| 4.1203 | 117500 | 0.6374        | 0.5228          | 0.9124                        |
| 4.1379 | 118000 | 0.6593        | 0.4976          | 0.9125                        |
| 4.1554 | 118500 | 0.649         | 0.5151          | 0.9109                        |
| 4.1729 | 119000 | 0.629         | 0.5303          | 0.9124                        |
| 4.1905 | 119500 | 0.6709        | 0.4868          | 0.9121                        |
| 4.2080 | 120000 | 0.5803        | 0.5177          | 0.9130                        |
| 4.2255 | 120500 | 0.6356        | 0.5329          | 0.9140                        |
| 4.2431 | 121000 | 0.6075        | 0.5057          | 0.9129                        |
| 4.2606 | 121500 | 0.6463        | 0.5084          | 0.9126                        |
| 4.2781 | 122000 | 0.6408        | 0.4859          | 0.9127                        |
| 4.2957 | 122500 | 0.6331        | 0.5210          | 0.9114                        |
| 4.3132 | 123000 | 0.6719        | 0.4893          | 0.9122                        |
| 4.3308 | 123500 | 0.6227        | 0.5126          | 0.9129                        |
| 4.3483 | 124000 | 0.6144        | 0.5293          | 0.9136                        |
| 4.3658 | 124500 | 0.6589        | 0.4978          | 0.9127                        |
| 4.3834 | 125000 | 0.6849        | 0.5195          | 0.9122                        |
| 4.4009 | 125500 | 0.6731        | 0.5150          | 0.9119                        |
| 4.4184 | 126000 | 0.658         | 0.4890          | 0.9136                        |
| 4.4360 | 126500 | 0.6256        | 0.5271          | 0.9134                        |
| 4.4535 | 127000 | 0.6295        | 0.5182          | 0.9129                        |
| 4.4710 | 127500 | 0.6804        | 0.4870          | 0.9133                        |
| 4.4886 | 128000 | 0.5868        | 0.4831          | 0.9129                        |
| 4.5061 | 128500 | 0.6316        | 0.4963          | 0.9135                        |
| 4.5236 | 129000 | 0.5873        | 0.5179          | 0.9149                        |
| 4.5412 | 129500 | 0.6383        | 0.5188          | 0.9126                        |
| 4.5587 | 130000 | 0.5936        | 0.5420          | 0.9117                        |
| 4.5762 | 130500 | 0.654         | 0.5248          | 0.9123                        |
| 4.5938 | 131000 | 0.6172        | 0.5067          | 0.9130                        |
| 4.6113 | 131500 | 0.5766        | 0.5335          | 0.9117                        |
| 4.6288 | 132000 | 0.5688        | 0.5345          | 0.9106                        |
| 4.6464 | 132500 | 0.6254        | 0.5352          | 0.9115                        |
| 4.6639 | 133000 | 0.5978        | 0.5244          | 0.9117                        |
| 4.6814 | 133500 | 0.6332        | 0.5511          | 0.9119                        |
| 4.6990 | 134000 | 0.6209        | 0.5356          | 0.9120                        |
| 4.7165 | 134500 | 0.6166        | 0.5532          | 0.9125                        |
| 4.7340 | 135000 | 0.5897        | 0.5888          | 0.9105                        |
| 4.7516 | 135500 | 0.624         | 0.5153          | 0.9123                        |
| 4.7691 | 136000 | 0.6563        | 0.5260          | 0.9134                        |
| 4.7866 | 136500 | 0.6098        | 0.5603          | 0.9122                        |
| 4.8042 | 137000 | 0.6313        | 0.5390          | 0.9124                        |
| 4.8217 | 137500 | 0.5737        | 0.5093          | 0.9129                        |
| 4.8392 | 138000 | 0.6475        | 0.5320          | 0.9114                        |
| 4.8568 | 138500 | 0.5752        | 0.5531          | 0.9120                        |
| 4.8743 | 139000 | 0.6378        | 0.4997          | 0.9114                        |
| 4.8918 | 139500 | 0.5641        | 0.5121          | 0.9120                        |
| 4.9094 | 140000 | 0.5771        | 0.5343          | 0.9114                        |
| 4.9269 | 140500 | 0.5869        | 0.5277          | 0.9124                        |
| 4.9444 | 141000 | 0.5417        | 0.5105          | 0.9143                        |
| 4.9620 | 141500 | 0.5517        | 0.5664          | 0.9133                        |
| 4.9795 | 142000 | 0.589         | 0.5326          | 0.9122                        |
| 4.9970 | 142500 | 0.5449        | 0.5236          | 0.9136                        |
| 5.0146 | 143000 | 0.5687        | 0.5217          | 0.9141                        |
| 5.0321 | 143500 | 0.5815        | 0.5520          | 0.9131                        |
| 5.0496 | 144000 | 0.6309        | 0.5290          | 0.9125                        |
| 5.0672 | 144500 | 0.6086        | 0.5305          | 0.9128                        |
| 5.0847 | 145000 | 0.5905        | 0.5044          | 0.9135                        |
| 5.1022 | 145500 | 0.6242        | 0.5113          | 0.9144                        |
| 5.1198 | 146000 | 0.603         | 0.5263          | 0.9137                        |
| 5.1373 | 146500 | 0.6187        | 0.5086          | 0.9131                        |
| 5.1548 | 147000 | 0.6007        | 0.5291          | 0.9136                        |
| 5.1724 | 147500 | 0.5934        | 0.5113          | 0.9131                        |
| 5.1899 | 148000 | 0.6208        | 0.4981          | 0.9142                        |
| 5.2074 | 148500 | 0.5524        | 0.5414          | 0.9146                        |
| 5.2250 | 149000 | 0.5941        | 0.5274          | 0.9146                        |
| 5.2425 | 149500 | 0.5694        | 0.5315          | 0.9140                        |
| 5.2600 | 150000 | 0.6045        | 0.5177          | 0.9138                        |
| 5.2776 | 150500 | 0.5928        | 0.4923          | 0.9146                        |
| 5.2951 | 151000 | 0.594         | 0.5209          | 0.9138                        |
| 5.3126 | 151500 | 0.6303        | 0.5014          | 0.9137                        |
| 5.3302 | 152000 | 0.5867        | 0.5151          | 0.9135                        |
| 5.3477 | 152500 | 0.5686        | 0.5244          | 0.9142                        |
| 5.3652 | 153000 | 0.6198        | 0.5063          | 0.9140                        |
| 5.3828 | 153500 | 0.6458        | 0.5403          | 0.9131                        |
| 5.4003 | 154000 | 0.6284        | 0.4988          | 0.9140                        |
| 5.4178 | 154500 | 0.6192        | 0.5008          | 0.9143                        |
| 5.4354 | 155000 | 0.5943        | 0.5334          | 0.9134                        |
| 5.4529 | 155500 | 0.5725        | 0.5270          | 0.9141                        |
| 5.4704 | 156000 | 0.656         | 0.4985          | 0.9146                        |
| 5.4880 | 156500 | 0.5562        | 0.4863          | 0.9137                        |
| 5.5055 | 157000 | 0.5888        | 0.5099          | 0.9141                        |
| 5.5230 | 157500 | 0.5329        | 0.5039          | 0.9149                        |
| 5.5406 | 158000 | 0.619         | 0.5232          | 0.9136                        |
| 5.5581 | 158500 | 0.5528        | 0.5471          | 0.9135                        |
| 5.5756 | 159000 | 0.6086        | 0.5226          | 0.9125                        |
| 5.5932 | 159500 | 0.5895        | 0.5072          | 0.9132                        |
| 5.6107 | 160000 | 0.5358        | 0.5419          | 0.9139                        |
| 5.6282 | 160500 | 0.5438        | 0.5334          | 0.9121                        |
| 5.6458 | 161000 | 0.579         | 0.5548          | 0.9118                        |
| 5.6633 | 161500 | 0.5636        | 0.5257          | 0.9127                        |
| 5.6808 | 162000 | 0.5984        | 0.5520          | 0.9136                        |
| 5.6984 | 162500 | 0.581         | 0.5314          | 0.9135                        |
| 5.7159 | 163000 | 0.5923        | 0.5665          | 0.9132                        |
| 5.7334 | 163500 | 0.5433        | 0.5717          | 0.9121                        |
| 5.7510 | 164000 | 0.583         | 0.5338          | 0.9137                        |
| 5.7685 | 164500 | 0.6272        | 0.5275          | 0.9137                        |
| 5.7860 | 165000 | 0.576         | 0.5657          | 0.9130                        |
| 5.8036 | 165500 | 0.5983        | 0.5457          | 0.9131                        |
| 5.8211 | 166000 | 0.5389        | 0.5252          | 0.9141                        |
| 5.8386 | 166500 | 0.6035        | 0.5478          | 0.9131                        |
| 5.8562 | 167000 | 0.5398        | 0.5334          | 0.9136                        |
| 5.8737 | 167500 | 0.5986        | 0.5021          | 0.9136                        |
| 5.8912 | 168000 | 0.5383        | 0.5261          | 0.9137                        |
| 5.9088 | 168500 | 0.5376        | 0.5374          | 0.9128                        |
| 5.9263 | 169000 | 0.5555        | 0.5375          | 0.9136                        |
| 5.9438 | 169500 | 0.5182        | 0.5230          | 0.9137                        |
| 5.9614 | 170000 | 0.5175        | 0.5653          | 0.9143                        |
| 5.9789 | 170500 | 0.5572        | 0.5433          | 0.9141                        |
| 5.9964 | 171000 | 0.5169        | 0.5035          | 0.9151                        |
| 6.0140 | 171500 | 0.5336        | 0.5178          | 0.9149                        |
| 6.0315 | 172000 | 0.5479        | 0.5427          | 0.9141                        |
| 6.0490 | 172500 | 0.5885        | 0.5417          | 0.9137                        |
| 6.0666 | 173000 | 0.5694        | 0.5232          | 0.9138                        |
| 6.0841 | 173500 | 0.5634        | 0.5074          | 0.9142                        |
| 6.1016 | 174000 | 0.5888        | 0.5102          | 0.9145                        |
| 6.1192 | 174500 | 0.576         | 0.5225          | 0.9148                        |
| 6.1367 | 175000 | 0.5843        | 0.5161          | 0.9144                        |
| 6.1542 | 175500 | 0.5635        | 0.5244          | 0.9141                        |
| 6.1718 | 176000 | 0.5666        | 0.5088          | 0.9149                        |
| 6.1893 | 176500 | 0.5868        | 0.5185          | 0.9150                        |
| 6.2068 | 177000 | 0.5211        | 0.5348          | 0.9154                        |
| 6.2244 | 177500 | 0.5672        | 0.5268          | 0.9150                        |
| 6.2419 | 178000 | 0.5286        | 0.5431          | 0.9141                        |
| 6.2594 | 178500 | 0.5723        | 0.5359          | 0.9154                        |
| 6.2770 | 179000 | 0.5648        | 0.5016          | 0.9154                        |
| 6.2945 | 179500 | 0.5566        | 0.5200          | 0.9145                        |
| 6.3120 | 180000 | 0.6074        | 0.5132          | 0.9145                        |
| 6.3296 | 180500 | 0.5473        | 0.5294          | 0.9145                        |
| 6.3471 | 181000 | 0.5325        | 0.5380          | 0.9150                        |
| 6.3646 | 181500 | 0.5868        | 0.5243          | 0.9149                        |
| 6.3822 | 182000 | 0.6155        | 0.5368          | 0.9143                        |
| 6.3997 | 182500 | 0.5944        | 0.4978          | 0.9149                        |
| 6.4172 | 183000 | 0.5838        | 0.5224          | 0.9146                        |
| 6.4348 | 183500 | 0.5644        | 0.5384          | 0.9146                        |
| 6.4523 | 184000 | 0.5471        | 0.5549          | 0.9152                        |
| 6.4698 | 184500 | 0.6198        | 0.5101          | 0.9147                        |
| 6.4874 | 185000 | 0.5304        | 0.5016          | 0.9152                        |
| 6.5049 | 185500 | 0.5621        | 0.5076          | 0.9155                        |
| 6.5224 | 186000 | 0.5027        | 0.5085          | 0.9148                        |
| 6.5400 | 186500 | 0.5882        | 0.5293          | 0.9147                        |
| 6.5575 | 187000 | 0.5228        | 0.5374          | 0.9152                        |
| 6.5750 | 187500 | 0.5717        | 0.5233          | 0.9140                        |
| 6.5926 | 188000 | 0.5651        | 0.5269          | 0.9136                        |
| 6.6101 | 188500 | 0.5182        | 0.5328          | 0.9140                        |
| 6.6276 | 189000 | 0.508         | 0.5250          | 0.9134                        |
| 6.6452 | 189500 | 0.5464        | 0.5427          | 0.9128                        |
| 6.6627 | 190000 | 0.5362        | 0.5137          | 0.9136                        |
| 6.6802 | 190500 | 0.5732        | 0.5161          | 0.9148                        |
| 6.6978 | 191000 | 0.5466        | 0.5416          | 0.9136                        |
| 6.7153 | 191500 | 0.5501        | 0.5736          | 0.9137                        |
| 6.7328 | 192000 | 0.5258        | 0.5528          | 0.9130                        |
| 6.7504 | 192500 | 0.5589        | 0.5380          | 0.9142                        |
| 6.7679 | 193000 | 0.5947        | 0.5297          | 0.9148                        |
| 6.7854 | 193500 | 0.5579        | 0.5590          | 0.9145                        |
| 6.8030 | 194000 | 0.5644        | 0.5412          | 0.9142                        |
| 6.8205 | 194500 | 0.5128        | 0.5181          | 0.9137                        |
| 6.8380 | 195000 | 0.5802        | 0.5451          | 0.9136                        |
| 6.8556 | 195500 | 0.5002        | 0.5293          | 0.9144                        |
| 6.8731 | 196000 | 0.5763        | 0.5153          | 0.9140                        |
| 6.8906 | 196500 | 0.5205        | 0.5261          | 0.9144                        |
| 6.9082 | 197000 | 0.5112        | 0.5342          | 0.9149                        |
| 6.9257 | 197500 | 0.523         | 0.5503          | 0.9140                        |
| 6.9432 | 198000 | 0.4875        | 0.5420          | 0.9148                        |
| 6.9608 | 198500 | 0.4963        | 0.5638          | 0.9142                        |
| 6.9783 | 199000 | 0.5327        | 0.5536          | 0.9149                        |
| 6.9958 | 199500 | 0.4822        | 0.5224          | 0.9141                        |
| 7.0134 | 200000 | 0.5078        | 0.5300          | 0.9140                        |
| 7.0309 | 200500 | 0.5208        | 0.5486          | 0.9149                        |
| 7.0484 | 201000 | 0.5641        | 0.5442          | 0.9148                        |
| 7.0660 | 201500 | 0.5484        | 0.5165          | 0.9143                        |
| 7.0835 | 202000 | 0.5289        | 0.5206          | 0.9142                        |
| 7.1010 | 202500 | 0.557         | 0.5178          | 0.9146                        |
| 7.1186 | 203000 | 0.556         | 0.5190          | 0.9147                        |
| 7.1361 | 203500 | 0.5567        | 0.5244          | 0.9143                        |
| 7.1536 | 204000 | 0.5376        | 0.5212          | 0.9148                        |
| 7.1712 | 204500 | 0.5448        | 0.5138          | 0.9150                        |
| 7.1887 | 205000 | 0.5541        | 0.5231          | 0.9155                        |
| 7.2062 | 205500 | 0.5006        | 0.5261          | 0.9155                        |
| 7.2238 | 206000 | 0.5366        | 0.5184          | 0.9159                        |
| 7.2413 | 206500 | 0.5127        | 0.5360          | 0.9148                        |
| 7.2588 | 207000 | 0.5469        | 0.5225          | 0.9148                        |
| 7.2764 | 207500 | 0.5414        | 0.5080          | 0.9152                        |
| 7.2939 | 208000 | 0.5361        | 0.5135          | 0.9151                        |
| 7.3114 | 208500 | 0.5833        | 0.5132          | 0.9147                        |
| 7.3290 | 209000 | 0.515         | 0.5282          | 0.9137                        |
| 7.3465 | 209500 | 0.5165        | 0.5362          | 0.9154                        |
| 7.3640 | 210000 | 0.5551        | 0.5327          | 0.9159                        |
| 7.3816 | 210500 | 0.5845        | 0.5409          | 0.9143                        |
| 7.3991 | 211000 | 0.5798        | 0.5057          | 0.9147                        |
| 7.4166 | 211500 | 0.5614        | 0.5275          | 0.9149                        |
| 7.4342 | 212000 | 0.5445        | 0.5175          | 0.9153                        |
| 7.4517 | 212500 | 0.5175        | 0.5424          | 0.9139                        |
| 7.4692 | 213000 | 0.6043        | 0.5075          | 0.9148                        |
| 7.4868 | 213500 | 0.5051        | 0.5067          | 0.9154                        |
| 7.5043 | 214000 | 0.5337        | 0.5143          | 0.9153                        |
| 7.5218 | 214500 | 0.4822        | 0.5049          | 0.9156                        |
| 7.5394 | 215000 | 0.5722        | 0.5359          | 0.9153                        |
| 7.5569 | 215500 | 0.5014        | 0.5306          | 0.9147                        |
| 7.5744 | 216000 | 0.5441        | 0.5222          | 0.9138                        |
| 7.5920 | 216500 | 0.5391        | 0.5261          | 0.9138                        |
| 7.6095 | 217000 | 0.494         | 0.5275          | 0.9144                        |
| 7.6270 | 217500 | 0.4881        | 0.5268          | 0.9141                        |
| 7.6446 | 218000 | 0.5263        | 0.5381          | 0.9138                        |
| 7.6621 | 218500 | 0.5017        | 0.5209          | 0.9134                        |
| 7.6796 | 219000 | 0.5566        | 0.5347          | 0.9138                        |
| 7.6972 | 219500 | 0.5201        | 0.5519          | 0.9135                        |
| 7.7147 | 220000 | 0.5269        | 0.5718          | 0.9143                        |
| 7.7322 | 220500 | 0.5125        | 0.5442          | 0.9135                        |
| 7.7498 | 221000 | 0.5307        | 0.5292          | 0.9142                        |
| 7.7673 | 221500 | 0.5718        | 0.5179          | 0.9140                        |
| 7.7848 | 222000 | 0.5345        | 0.5512          | 0.9147                        |
| 7.8024 | 222500 | 0.5456        | 0.5447          | 0.9143                        |
| 7.8199 | 223000 | 0.4889        | 0.5197          | 0.9144                        |
| 7.8374 | 223500 | 0.5532        | 0.5487          | 0.9146                        |
| 7.8550 | 224000 | 0.4902        | 0.5257          | 0.9137                        |
| 7.8725 | 224500 | 0.5535        | 0.5095          | 0.9135                        |
| 7.8900 | 225000 | 0.4988        | 0.5404          | 0.9141                        |
| 7.9076 | 225500 | 0.4883        | 0.5280          | 0.9143                        |
| 7.9251 | 226000 | 0.4975        | 0.5458          | 0.9133                        |
| 7.9426 | 226500 | 0.4698        | 0.5357          | 0.9147                        |
| 7.9602 | 227000 | 0.4831        | 0.5391          | 0.9143                        |
| 7.9777 | 227500 | 0.5073        | 0.5492          | 0.9148                        |
| 7.9952 | 228000 | 0.4637        | 0.5140          | 0.9148                        |
| 8.0128 | 228500 | 0.4817        | 0.5200          | 0.9137                        |
| 8.0303 | 229000 | 0.5078        | 0.5370          | 0.9146                        |
| 8.0478 | 229500 | 0.5342        | 0.5497          | 0.9149                        |
| 8.0654 | 230000 | 0.5317        | 0.5179          | 0.9156                        |
| 8.0829 | 230500 | 0.5074        | 0.5286          | 0.9151                        |
| 8.1004 | 231000 | 0.5302        | 0.5165          | 0.9162                        |
| 8.1180 | 231500 | 0.5481        | 0.5200          | 0.9163                        |
| 8.1355 | 232000 | 0.538         | 0.5216          | 0.9161                        |
| 8.1530 | 232500 | 0.5168        | 0.5189          | 0.9152                        |
| 8.1706 | 233000 | 0.5118        | 0.5195          | 0.9153                        |
| 8.1881 | 233500 | 0.5394        | 0.5192          | 0.9155                        |
| 8.2056 | 234000 | 0.488         | 0.5100          | 0.9153                        |
| 8.2232 | 234500 | 0.5214        | 0.5162          | 0.9161                        |
| 8.2407 | 235000 | 0.4944        | 0.5343          | 0.9149                        |
| 8.2582 | 235500 | 0.5226        | 0.5190          | 0.9152                        |
| 8.2758 | 236000 | 0.5234        | 0.5146          | 0.9159                        |
| 8.2933 | 236500 | 0.5165        | 0.5011          | 0.9153                        |
| 8.3108 | 237000 | 0.5599        | 0.5129          | 0.9152                        |
| 8.3284 | 237500 | 0.4991        | 0.5212          | 0.9154                        |
| 8.3459 | 238000 | 0.5007        | 0.5383          | 0.9148                        |
| 8.3634 | 238500 | 0.5406        | 0.5394          | 0.9154                        |
| 8.3810 | 239000 | 0.5606        | 0.5445          | 0.9147                        |
| 8.3985 | 239500 | 0.5626        | 0.5143          | 0.9149                        |
| 8.4160 | 240000 | 0.5353        | 0.5338          | 0.9156                        |
| 8.4336 | 240500 | 0.5168        | 0.5208          | 0.9158                        |
| 8.4511 | 241000 | 0.5058        | 0.5312          | 0.9146                        |
| 8.4686 | 241500 | 0.5919        | 0.5143          | 0.9149                        |
| 8.4862 | 242000 | 0.4883        | 0.5149          | 0.9159                        |
| 8.5037 | 242500 | 0.5072        | 0.5132          | 0.9156                        |
| 8.5212 | 243000 | 0.4655        | 0.5111          | 0.9148                        |
| 8.5388 | 243500 | 0.5592        | 0.5269          | 0.9155                        |
| 8.5563 | 244000 | 0.4836        | 0.5217          | 0.9152                        |
| 8.5738 | 244500 | 0.5299        | 0.5269          | 0.9143                        |
| 8.5914 | 245000 | 0.5081        | 0.5206          | 0.9136                        |
| 8.6089 | 245500 | 0.48          | 0.5159          | 0.9144                        |
| 8.6264 | 246000 | 0.4713        | 0.5272          | 0.9141                        |
| 8.6440 | 246500 | 0.5038        | 0.5287          | 0.9139                        |
| 8.6615 | 247000 | 0.4872        | 0.5199          | 0.9142                        |
| 8.6790 | 247500 | 0.5429        | 0.5227          | 0.9138                        |
| 8.6966 | 248000 | 0.5042        | 0.5402          | 0.9136                        |
| 8.7141 | 248500 | 0.511         | 0.5530          | 0.9141                        |
| 8.7316 | 249000 | 0.5097        | 0.5374          | 0.9131                        |
| 8.7492 | 249500 | 0.4974        | 0.5312          | 0.9138                        |
| 8.7667 | 250000 | 0.5617        | 0.5381          | 0.9148                        |
| 8.7842 | 250500 | 0.5234        | 0.5476          | 0.9150                        |
| 8.8018 | 251000 | 0.5133        | 0.5447          | 0.9147                        |
| 8.8193 | 251500 | 0.488         | 0.5270          | 0.9148                        |
| 8.8368 | 252000 | 0.5377        | 0.5325          | 0.9144                        |
| 8.8544 | 252500 | 0.479         | 0.5324          | 0.9145                        |
| 8.8719 | 253000 | 0.5329        | 0.5200          | 0.9140                        |
| 8.8894 | 253500 | 0.4744        | 0.5346          | 0.9140                        |
| 8.9070 | 254000 | 0.4827        | 0.5333          | 0.9145                        |
| 8.9245 | 254500 | 0.4757        | 0.5415          | 0.9139                        |
| 8.9420 | 255000 | 0.4504        | 0.5307          | 0.9147                        |
| 8.9596 | 255500 | 0.4657        | 0.5337          | 0.9146                        |
| 8.9771 | 256000 | 0.4976        | 0.5473          | 0.9150                        |
| 8.9946 | 256500 | 0.459         | 0.5214          | 0.9144                        |
| 9.0122 | 257000 | 0.4615        | 0.5296          | 0.9147                        |
| 9.0297 | 257500 | 0.5019        | 0.5312          | 0.9149                        |
| 9.0472 | 258000 | 0.5142        | 0.5379          | 0.9152                        |
| 9.0648 | 258500 | 0.5174        | 0.5197          | 0.9150                        |
| 9.0823 | 259000 | 0.4896        | 0.5277          | 0.9155                        |
| 9.0998 | 259500 | 0.5114        | 0.5240          | 0.9161                        |
| 9.1174 | 260000 | 0.529         | 0.5293          | 0.9155                        |
| 9.1349 | 260500 | 0.5305        | 0.5242          | 0.9157                        |
| 9.1524 | 261000 | 0.4941        | 0.5160          | 0.9155                        |
| 9.1700 | 261500 | 0.5025        | 0.5274          | 0.9153                        |
| 9.1875 | 262000 | 0.5148        | 0.5198          | 0.9155                        |
| 9.2050 | 262500 | 0.4882        | 0.5116          | 0.9160                        |
| 9.2226 | 263000 | 0.4964        | 0.5139          | 0.9155                        |
| 9.2401 | 263500 | 0.4792        | 0.5284          | 0.9153                        |
| 9.2576 | 264000 | 0.5089        | 0.5175          | 0.9154                        |
| 9.2752 | 264500 | 0.5124        | 0.5188          | 0.9154                        |
| 9.2927 | 265000 | 0.4968        | 0.5153          | 0.9152                        |
| 9.3102 | 265500 | 0.5454        | 0.5129          | 0.9152                        |
| 9.3278 | 266000 | 0.4858        | 0.5209          | 0.9147                        |
| 9.3453 | 266500 | 0.4822        | 0.5257          | 0.9148                        |
| 9.3628 | 267000 | 0.5343        | 0.5298          | 0.9148                        |
| 9.3804 | 267500 | 0.5443        | 0.5303          | 0.9145                        |
| 9.3979 | 268000 | 0.546         | 0.5204          | 0.9153                        |
| 9.4154 | 268500 | 0.5253        | 0.5326          | 0.9154                        |
| 9.4330 | 269000 | 0.5062        | 0.5270          | 0.9154                        |
| 9.4505 | 269500 | 0.4901        | 0.5284          | 0.9150                        |
| 9.4680 | 270000 | 0.5675        | 0.5271          | 0.9154                        |
| 9.4856 | 270500 | 0.4831        | 0.5263          | 0.9152                        |
| 9.5031 | 271000 | 0.4873        | 0.5256          | 0.9152                        |
| 9.5206 | 271500 | 0.4576        | 0.5208          | 0.9155                        |
| 9.5382 | 272000 | 0.5392        | 0.5250          | 0.9154                        |
| 9.5557 | 272500 | 0.4716        | 0.5238          | 0.9158                        |
| 9.5732 | 273000 | 0.5202        | 0.5282          | 0.9156                        |
| 9.5908 | 273500 | 0.5036        | 0.5284          | 0.9149                        |
| 9.6083 | 274000 | 0.4645        | 0.5216          | 0.9151                        |
| 9.6258 | 274500 | 0.4683        | 0.5273          | 0.9154                        |
| 9.6434 | 275000 | 0.4881        | 0.5307          | 0.9154                        |
| 9.6609 | 275500 | 0.4677        | 0.5234          | 0.9155                        |
| 9.6784 | 276000 | 0.54          | 0.5212          | 0.9153                        |
| 9.6960 | 276500 | 0.4948        | 0.5277          | 0.9150                        |
| 9.7135 | 277000 | 0.5008        | 0.5293          | 0.9150                        |
| 9.7310 | 277500 | 0.4907        | 0.5307          | 0.9147                        |
| 9.7486 | 278000 | 0.4876        | 0.5276          | 0.9144                        |
| 9.7661 | 278500 | 0.539         | 0.5324          | 0.9145                        |
| 9.7836 | 279000 | 0.5147        | 0.5325          | 0.9145                        |
| 9.8012 | 279500 | 0.5095        | 0.5367          | 0.9150                        |
| 9.8187 | 280000 | 0.476         | 0.5333          | 0.9147                        |
| 9.8362 | 280500 | 0.5189        | 0.5325          | 0.9150                        |
| 9.8538 | 281000 | 0.4633        | 0.5342          | 0.9149                        |
| 9.8713 | 281500 | 0.5199        | 0.5314          | 0.9146                        |
| 9.8888 | 282000 | 0.4645        | 0.5312          | 0.9151                        |
| 9.9064 | 282500 | 0.4702        | 0.5339          | 0.9151                        |
| 9.9239 | 283000 | 0.4609        | 0.5362          | 0.9151                        |
| 9.9414 | 283500 | 0.4365        | 0.5340          | 0.9152                        |
| 9.9590 | 284000 | 0.4587        | 0.5339          | 0.9152                        |
| 9.9765 | 284500 | 0.4861        | 0.5355          | 0.9153                        |
| 9.9940 | 285000 | 0.4473        | 0.5352          | 0.9153                        |

</details>

### Framework Versions
- Python: 3.10.10
- Sentence Transformers: 3.4.0.dev0
- Transformers: 4.46.3
- PyTorch: 2.5.1+cu124
- Accelerate: 0.34.2
- Datasets: 2.21.0
- Tokenizers: 0.20.3

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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