sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:1943715 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
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1943715
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: who sang the song queen of my heart
  sentences:
  - Queen of My Heart Queen of My Heart "Queen of My Heart" is a song by Irish boy
    band Westlife. It was released on 8 November 2001 as the first single from their
    third studio album, "World of Our Own". It was released as a double A-side single
    with "When You're Looking Like That" in UK and Ireland. It debuted at number one
    on the UK Singles Chart, giving the band their ninth UK number one single in two
    and a half years, staying at the top of the chart for one week. It remains one
    of the band's most successful singles, becoming the
  - Stephanie Edwards (Grey's Anatomy) Stephanie Edwards (Grey's Anatomy) Stephanie
    Edwards, M.D. is a fictional character from the medical drama television series
    "Grey's Anatomy", which airs on the American Broadcasting Company (ABC) in the
    United States. The character was created by series producer Shonda Rhimes, and
    was portrayed by actress Jerrika Hinton from 2012 to 2017. Introduced as a surgical
    intern at the fictional Seattle Grace Mercy West Hospital, later renamed Grey
    Sloan Memorial Hospital, Stephanie works her way up to resident level with fellow
    intern and friend, Jo Wilson (Camilla Luddington). The character was described
    by Hinton as "innovative" who strives to be the
  - Heart of My Heart the 1926 song by Max, the Chief, and detect-o-tune operator
    Arrick. Heart of My Heart "The Gang that Sang Heart of My Heart" is a popular
    song. The music and lyrics were written by Ben Ryan (1892–1968) in 1926. It reminisces
    about being in a youthful quartet, singing "Heart of My Heart". The quoted line,
    "Heart of My Heart", so longed for in the 1926 song, begins the chorus of "The
    Story of the Rose", written by Andrew Mack (1863–1931) in 1899. Mack was a popular
    American actor, singer and comedian who reportedly first sang this song in an
    1899
- source_sentence: when did gretsch stop making guitars in america
  sentences:
  - Get Low (Lil Jon & the East Side Boyz song) Get Low (Lil Jon & the East Side Boyz
    song) "Get Low" is a song by Lil Jon & the East Side Boyz, featuring Ying Yang
    Twins, released in 2003. It is featured on the 2002 album "Kings of Crunk". The
    song reached number two on the US "Billboard" Hot 100 behind "Baby Boy" by Beyoncé
    featuring Sean Paul and number 20 on the US Hot Digital Songs. It was number five
    on the top Hot R&B/Hip-Hop songs of 2003. It is also known as a breakthrough single
    for the crunk genre, as the song's success helped it become mainstream.
  - TV Jones guitarist Brian Setzer, whose guitar sound relied heavily on vintage
    Gretsch guitars. When the Gretsch Guitar Company was in the process of creating
    a Brian Setzer signature model, Brian conducted a “blind sound test” of various
    pickup models that were to be considered for use in these guitars. Tom's Hotrod
    pickup design was chosen because of its sound being the most faithful to the original.
    (At this point, the pickups Gretsch was using in their guitars were made of overseas
    parts and ceramic magnets). Word soon spread that TV Jones was making “true-to-the-original”
    Filter’tron pickups and many famous players demanded
  - Gretsch South Carolina, where it remains today. The first new guitar model introduced
    was the Traveling Wilburys model - an Asian import - which looked much like a
    Danelectro. While this guitar model did little to bolster Gretsch's reputation
    for producing classic guitars, it served notice that Gretsch was back. After numerous
    failed attempts to acquire facilities or contract production in the United States,
    Fred Gretsch and long-time Gretsch employee Duke Kramer, who advised Gretsch,
    turned to Terada of Japan, and production began there. A range of reissues appeared
    throughout the 1990s to mixed reviews. They were of generally high quality,
- source_sentence: 'Examining playfulness in adults: Testing its correlates with personality,
    positive psychological functioning, goal aspirations, and multi-methodically assessed
    ingenuity'
  sentences:
  - Implementation of Evolutionary Algorithms for Deep Architectures
  - Chadwick Boseman Chadwick Boseman Chadwick Aaron Boseman (born November 29, 1976)
    is an American actor, director, and producer known for his portrayals of real-life
    historical figures such as Jackie Robinson in "42" (2013), James Brown in "Get
    on Up" (2014) and Thurgood Marshall in "Marshall" (2017) and for his portrayal
    of the superhero Black Panther in the Marvel Cinematic Universe films "" (2016),
    "Black Panther" (2018), "" (2018) and the upcoming "" (2019). Boseman has also
    had roles in the television series "Lincoln Heights" (2008) and "Persons Unknown"
    (2010) and the films "The Express" (2008), "Draft Day" (2014) and "Message from
    the
  - 'Assessment of Play and Leisure: Delineation of the Problem'
- source_sentence: 1 in what part of italy was gelato first made
  sentences:
  - Domínguez Domínguez Domínguez is a name of Spanish origin. It used to mean "son
    of Domingo" (i.e., son of Dominic). The surname is usually written Dominguez in
    the Philippines and United States. Written as Domínguez in Spanish speaking countries
    like Spain, Mexico, Argentina, etc... As of 2014, 40.7% of all known bearers of
    the surname "Domínguez" were residents of Mexico (frequency 1:242), 12.8% of Spain
    (1:288), 8.5% of Argentina (1:396), 7.7% of the United States (1:3,721), 4.3%
    of Cuba (1:212), 3.2% of Colombia (1:1,186), 3.0% of Peru (1:831), 2.6% of Venezuela
    (1:904), 2.6% of Honduras (1:265), 2.4% of Paraguay (1:241), 2.0%
  - Frost Gelato to the taste of the ice cream they had in Italy concluding that the
    only way to get gelato at the time was to make another trip to Italy. Thus both
    owners searched for a way to make gelato in the United States eventually locating
    a company that imports ingredients directly from Italy, after spending days studying
    how to make gelato, the owners created their first batch and after sampling it
    felt the tastes they had come across in Italy. Both owners wanted to share the
    taste of gelato with their community and thus after a few months, Frost Gelato
  - Gelato any way that ice cream is, including cup, cone, sandwich, cake, pie, or
    on a stick. Gelato was invented by Buontalenti, in Florence (Tuscany), during
    the Renaissance period. The Buontalenti created the dessert for the Grand Duke
    Cosimo I de’ Medici, who wanted him to organize an opulent banquet to celebrate
    the Spanish deputation. It was October 5, 1600, and Buontalenti had worked for
    four months to prepare such a banquet. In Florence, most shops selling hand-made
    ice-cream also usually offer a "Buontalenti" flavour. In 1686, the Sicilian fisherman
    Francesco Procopio dei Coltelli perfected the first ice cream machine. However,
- source_sentence: who does george nelson represent in o brother where art thou
  sentences:
  - O Brother, Where Art Thou? the film got together and performed the music from
    the film in a Down from the Mountain concert tour which was filmed for TV and
    DVD. This included Ralph Stanley, John Hartford, Alison Krauss, Emmylou Harris,
    Gillian Welch, Chris Sharp, and others. O Brother, Where Art Thou? O Brother,
    Where Art Thou? is a 2000 crime comedy film written, produced, and directed by
    Joel and Ethan Coen, and starring George Clooney, John Turturro, and Tim Blake
    Nelson, with John Goodman, Holly Hunter, and Charles Durning in supporting roles.
    The film is set in 1937 rural Mississippi during the Great Depression.
  - O Brother, Where Art Thou? omitted all instances of the words "damn" and "hell"
    from the Coens' script, which only became known to Clooney after the directors
    pointed this out to him during shooting. This was the fourth film of the brothers
    in which John Turturro has starred. Other actors in "O Brother, Where Art Thou?"
    who had worked previously with the Coens include John Goodman (three films), Holly
    Hunter (two), Michael Badalucco and Charles Durning (one film each). The Coens
    used digital color correction to give the film a sepia-tinted look. Joel stated
    this was because the actual set was "greener than Ireland". Cinematographer
  - 'Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching
    Movies and Reading Books'
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: 0.9116536208878427
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.08101154961957414
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9119820460890032
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9114894082872625
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9119820460890032
      name: Max 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 384-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 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **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): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## 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-final")
# Run inference
sentences = [
    'who does george nelson represent in o brother where art thou',
    'O Brother, Where Art Thou? omitted all instances of the words "damn" and "hell" from the Coens\' script, which only became known to Clooney after the directors pointed this out to him during shooting. This was the fourth film of the brothers in which John Turturro has starred. Other actors in "O Brother, Where Art Thou?" who had worked previously with the Coens include John Goodman (three films), Holly Hunter (two), Michael Badalucco and Charles Durning (one film each). The Coens used digital color correction to give the film a sepia-tinted look. Joel stated this was because the actual set was "greener than Ireland". Cinematographer',
    'O Brother, Where Art Thou? the film got together and performed the music from the film in a Down from the Mountain concert tour which was filmed for TV and DVD. This included Ralph Stanley, John Hartford, Alison Krauss, Emmylou Harris, Gillian Welch, Chris Sharp, and others. O Brother, Where Art Thou? O Brother, Where Art Thou? is a 2000 crime comedy film written, produced, and directed by Joel and Ethan Coen, and starring George Clooney, John Turturro, and Tim Blake Nelson, with John Goodman, Holly Hunter, and Charles Durning in supporting roles. The film is set in 1937 rural Mississippi during the Great Depression.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## 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    | 0.9117    |
| dot_accuracy       | 0.081     |
| manhattan_accuracy | 0.912     |
| euclidean_accuracy | 0.9115    |
| **max_accuracy**   | **0.912** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Datasets

#### NQ

* Dataset: NQ
* Size: 49,676 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: 11.91 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 111 tokens</li><li>mean: 137.95 tokens</li><li>max: 212 tokens</li></ul> | <ul><li>min: 113 tokens</li><li>mean: 138.79 tokens</li><li>max: 209 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 29,908 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: 22.81 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 93 tokens</li><li>mean: 240.49 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 73 tokens</li><li>mean: 239.5 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 49,676 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: 15.69 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.12 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.39 tokens</li><li>max: 64 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 99,352 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: 20 tokens</li><li>mean: 196.74 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 203.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 208.09 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 74,514 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: 12.49 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 112.67 tokens</li><li>max: 154 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 113.92 tokens</li><li>max: 163 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,838 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: 11.92 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.11 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 63.73 tokens</li><li>max: 150 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 15,210 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: 28.96 tokens</li><li>max: 143 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 134.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 163.95 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 5,070 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: 8.05 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 45.27 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 35.68 tokens</li><li>max: 75 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,838 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: 12.79 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.93 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.13 tokens</li><li>max: 44 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,838 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: 21.16 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 100.92 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 112.62 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 99,352 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: 23.56 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 52.4 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 62.09 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 29,908 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: 19.62 tokens</li><li>max: 167 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 110.24 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 111.99 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 15,210 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: 8.7 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 112.73 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 115.71 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 49,676 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: 19.22 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 246.01 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 232.19 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,838 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: 12.6 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 112 tokens</li><li>mean: 136.78 tokens</li><li>max: 205 tokens</li></ul> | <ul><li>min: 110 tokens</li><li>mean: 135.66 tokens</li><li>max: 254 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,838 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: 18 tokens</li><li>mean: 150.59 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 142.04 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 198.29 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 29,908 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: 9.55 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 104.71 tokens</li><li>max: 212 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 118.22 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,838 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: 15.95 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.68 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.15 tokens</li><li>max: 52 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 40,048 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: 23.83 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 113.6 tokens</li><li>max: 194 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 115.33 tokens</li><li>max: 178 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 11 tokens</li><li>mean: 12.69 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 12.52 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 12.23 tokens</li><li>max: 19 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 117 tokens</li><li>mean: 208.96 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 117 tokens</li><li>mean: 208.27 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 119 tokens</li><li>mean: 210.46 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 46 tokens</li><li>mean: 208.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 49 tokens</li><li>mean: 203.97 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 208.78 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 15 tokens</li><li>mean: 41.73 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 40.48 tokens</li><li>max: 198 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 32.23 tokens</li><li>max: 135 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 20 tokens</li><li>mean: 27.77 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 27.22 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 27.46 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17 tokens</li><li>mean: 29.98 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 28.9 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 30.33 tokens</li><li>max: 100 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18.47 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 16.89 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 16.86 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 11.25 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.47 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.12 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 59.1 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 59.35 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 58.47 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17.61 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: 16.67 tokens</li><li>max: 75 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16 tokens</li><li>mean: 57.26 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 60.03 tokens</li><li>max: 153 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.89 tokens</li><li>max: 169 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 35 tokens</li><li>mean: 79.52 tokens</li><li>max: 159 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 80.36 tokens</li><li>max: 165 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 79.14 tokens</li><li>max: 161 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 20.44 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.33 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 19.14 tokens</li><li>max: 69 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17 tokens</li><li>mean: 32.06 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 32.13 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 31.58 tokens</li><li>max: 68 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 37 tokens</li><li>mean: 52.96 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 52.53 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 52.92 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 91 tokens</li><li>mean: 240.57 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 84 tokens</li><li>mean: 239.31 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 97 tokens</li><li>mean: 245.05 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 35 tokens</li><li>mean: 140.99 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 142.44 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 143.37 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 33.28 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 31.59 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 31.9 tokens</li><li>max: 71 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 43.07 tokens</li><li>max: 152 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 46.55 tokens</li><li>max: 152 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 43.18 tokens</li><li>max: 152 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 51.87 tokens</li><li>max: 136 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 51.68 tokens</li><li>max: 144 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 51.93 tokens</li><li>max: 114 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 41.77 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 44.27 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 40.08 tokens</li><li>max: 141 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17 tokens</li><li>mean: 26.8 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 26.15 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 26.4 tokens</li><li>max: 55 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 28.53 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 24.21 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 27.94 tokens</li><li>max: 67 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 29.91 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 30.18 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 30.06 tokens</li><li>max: 68 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 39 tokens</li><li>mean: 226.28 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 50 tokens</li><li>mean: 229.51 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 229.59 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 48 tokens</li><li>mean: 167.12 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 173.01 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 178.89 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 88 tokens</li><li>mean: 242.08 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 90 tokens</li><li>mean: 246.93 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 86 tokens</li><li>mean: 244.36 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 24 tokens</li><li>mean: 35.46 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 34.33 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 34.52 tokens</li><li>max: 57 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 36.88 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 36.12 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 36.59 tokens</li><li>max: 92 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16 tokens</li><li>mean: 135.8 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 140.06 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 134.53 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 28 tokens</li><li>mean: 56.05 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 56.59 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 56.05 tokens</li><li>max: 81 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 80.75 tokens</li><li>max: 163 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 96.06 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 45.02 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 15.01 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.1 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.36 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18.75 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: 18.3 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 40 tokens</li><li>mean: 123.98 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 62 tokens</li><li>mean: 127.04 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 58 tokens</li><li>mean: 128.35 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 53 tokens</li><li>mean: 80.81 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 81.14 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 55 tokens</li><li>mean: 79.79 tokens</li><li>max: 115 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 19.78 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 18.35 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.82 tokens</li><li>max: 52 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17 tokens</li><li>mean: 24.57 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 24.23 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 24.2 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13 tokens</li><li>mean: 43.83 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 43.44 tokens</li><li>max: 197 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 44.95 tokens</li><li>max: 197 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 30 tokens</li><li>mean: 153.01 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 161.19 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 167.06 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 174 tokens</li><li>mean: 252.54 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 169 tokens</li><li>mean: 252.57 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 178 tokens</li><li>mean: 252.73 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17 tokens</li><li>mean: 125.31 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 130.35 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 135.56 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 54.84 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 62.09 tokens</li><li>max: 249 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 62.43 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 25 tokens</li><li>mean: 44.81 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 45.07 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 44.7 tokens</li><li>max: 88 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 31.21 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 30.13 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 32.21 tokens</li><li>max: 67 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 29 tokens</li><li>mean: 36.36 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 35.59 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 34.87 tokens</li><li>max: 55 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 29 tokens</li><li>mean: 31.98 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 31.78 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 31.8 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 27 tokens</li><li>mean: 182.22 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 180.87 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 184.07 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 36 tokens</li><li>mean: 55.58 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 54.96 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 55.99 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 36 tokens</li><li>mean: 253.19 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 252.56 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 157 tokens</li><li>mean: 254.12 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13 tokens</li><li>mean: 127.06 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 133.94 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 142.64 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 29.93 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 30.22 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 29.85 tokens</li><li>max: 68 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 45 tokens</li><li>mean: 92.5 tokens</li><li>max: 176 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 93.22 tokens</li><li>max: 174 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 94.89 tokens</li><li>max: 183 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 40.13 tokens</li><li>max: 120 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 39.18 tokens</li><li>max: 186 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 40.4 tokens</li><li>max: 148 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 42 tokens</li><li>mean: 66.89 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 67.42 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 67.0 tokens</li><li>max: 92 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13.77 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.82 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.31 tokens</li><li>max: 20 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 99 tokens</li><li>mean: 241.8 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 245.95 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 99 tokens</li><li>mean: 246.6 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 76 tokens</li><li>mean: 230.72 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 86 tokens</li><li>mean: 234.59 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 81 tokens</li><li>mean: 235.71 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16.47 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 16.67 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 45 tokens</li><li>mean: 165.1 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 54 tokens</li><li>mean: 167.21 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 166.49 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 24 tokens</li><li>mean: 73.0 tokens</li><li>max: 218 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 73.42 tokens</li><li>max: 170 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 72.48 tokens</li><li>max: 205 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 31.3 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 30.49 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 30.87 tokens</li><li>max: 53 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 40.74 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 40.56 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 38.5 tokens</li><li>max: 86 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 24 tokens</li><li>mean: 114.82 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 121.93 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 124.38 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18.18 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.03 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 18.39 tokens</li><li>max: 67 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 29 tokens</li><li>mean: 42.34 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 41.63 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 41.73 tokens</li><li>max: 75 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17.39 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.19 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.71 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 116 tokens</li><li>mean: 255.36 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 146 tokens</li><li>mean: 255.71 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 68 tokens</li><li>mean: 252.09 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 23 tokens</li><li>mean: 27.03 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 26.8 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 26.94 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 7.42 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.31 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.27 tokens</li><li>max: 11 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17.91 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.43 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.2 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 9.62 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.6 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.69 tokens</li><li>max: 16 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 29.63 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 28.69 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 28.59 tokens</li><li>max: 86 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 28 tokens</li><li>mean: 47.83 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 45.51 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 45.81 tokens</li><li>max: 123 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 39.82 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 39.88 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 35.83 tokens</li><li>max: 111 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 20.61 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 18.62 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 19.51 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13 tokens</li><li>mean: 172.18 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 175.43 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 180.36 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 78 tokens</li><li>mean: 223.56 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 76 tokens</li><li>mean: 220.51 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 64 tokens</li><li>mean: 220.0 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 10.33 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.18 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.08 tokens</li><li>max: 16 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 248.66 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 248.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 249.11 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 20 tokens</li><li>mean: 41.01 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 37.95 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 41.14 tokens</li><li>max: 104 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 37 tokens</li><li>mean: 63.19 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 62.66 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 63.44 tokens</li><li>max: 95 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14.16 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.55 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 31.82 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 30.75 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 33.25 tokens</li><li>max: 105 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 82 tokens</li><li>mean: 219.39 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 57 tokens</li><li>mean: 222.63 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 96 tokens</li><li>mean: 232.08 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 28 tokens</li><li>mean: 32.21 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: 31.64 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 11 tokens</li><li>mean: 39.62 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 34.2 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 33.53 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 19.55 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.27 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.12 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16 tokens</li><li>mean: 25.41 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 24.94 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 25.13 tokens</li><li>max: 57 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 15 tokens</li><li>mean: 44.09 tokens</li><li>max: 197 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 44.97 tokens</li><li>max: 211 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 39.22 tokens</li><li>max: 115 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 51.02 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 49.57 tokens</li><li>max: 180 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 54.59 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17.14 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.31 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.13 tokens</li><li>max: 34 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 15.72 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.33 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.21 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 23 tokens</li><li>mean: 127.55 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 139.34 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 133.69 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 35.03 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 34.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 27.84 tokens</li><li>max: 148 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 37 tokens</li><li>mean: 125.55 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 123.85 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 134.16 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 33 tokens</li><li>mean: 52.09 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 52.16 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 51.84 tokens</li><li>max: 95 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 54 tokens</li><li>mean: 143.98 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 57 tokens</li><li>mean: 143.52 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 66 tokens</li><li>mean: 147.45 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16 tokens</li><li>mean: 30.23 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 29.82 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 29.44 tokens</li><li>max: 71 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 38 tokens</li><li>mean: 67.61 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 67.14 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 68.46 tokens</li><li>max: 107 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18.37 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.55 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.88 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 33 tokens</li><li>mean: 69.62 tokens</li><li>max: 194 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 67.98 tokens</li><li>max: 194 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 67.81 tokens</li><li>max: 165 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 36.8 tokens</li><li>max: 234 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 40.85 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 45.53 tokens</li><li>max: 244 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 28.69 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 26.7 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 26.92 tokens</li><li>max: 90 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 51.02 tokens</li><li>max: 247 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 60.56 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 64.19 tokens</li><li>max: 249 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 15 tokens</li><li>mean: 120.75 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 122.1 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 134.06 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 24.78 tokens</li><li>max: 114 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 24.2 tokens</li><li>max: 114 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 23.85 tokens</li><li>max: 84 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 39 tokens</li><li>mean: 59.5 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 58.38 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 58.22 tokens</li><li>max: 95 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17 tokens</li><li>mean: 46.37 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 45.05 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 49.47 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 9.68 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.71 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.57 tokens</li><li>max: 17 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 44 tokens</li><li>mean: 146.46 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 142.09 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 50 tokens</li><li>mean: 144.22 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 19.55 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 19.39 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 19 tokens</li><li>mean: 52.28 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 51.88 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 53.03 tokens</li><li>max: 97 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 229.76 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 236.43 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 235.02 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 11 tokens</li><li>mean: 15.08 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 15.05 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 15.39 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 47 tokens</li><li>mean: 72.64 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 72.66 tokens</li><li>max: 109 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 73.26 tokens</li><li>max: 108 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 24 tokens</li><li>mean: 72.63 tokens</li><li>max: 198 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 72.69 tokens</li><li>max: 224 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 73.44 tokens</li><li>max: 226 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 30 tokens</li><li>mean: 150.23 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 160.48 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 164.59 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 26.52 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 25.55 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 25.85 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 19 tokens</li><li>mean: 33.82 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 33.49 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 33.34 tokens</li><li>max: 77 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 19.18 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.45 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.48 tokens</li><li>max: 79 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 59.48 tokens</li><li>max: 147 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 61.95 tokens</li><li>max: 154 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 48.32 tokens</li><li>max: 124 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 42 tokens</li><li>mean: 81.83 tokens</li><li>max: 181 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 79.75 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 77.43 tokens</li><li>max: 166 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 67 tokens</li><li>mean: 242.04 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 76 tokens</li><li>mean: 243.28 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 67 tokens</li><li>mean: 247.07 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 47 tokens</li><li>mean: 110.04 tokens</li><li>max: 196 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 110.49 tokens</li><li>max: 195 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 110.67 tokens</li><li>max: 196 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 147 tokens</li><li>mean: 198.24 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 145 tokens</li><li>mean: 196.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 147 tokens</li><li>mean: 198.41 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13 tokens</li><li>mean: 129.55 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 136.75 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 137.33 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 214 tokens</li><li>mean: 255.54 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 214 tokens</li><li>mean: 255.53 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 224 tokens</li><li>mean: 255.61 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 61 tokens</li><li>mean: 84.69 tokens</li><li>max: 134 tokens</li></ul> | <ul><li>min: 59 tokens</li><li>mean: 85.39 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>min: 58 tokens</li><li>mean: 84.83 tokens</li><li>max: 125 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 27.72 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 27.43 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 27.37 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 7.37 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.32 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 24 tokens</li><li>mean: 55.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 52.2 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 55.59 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 65.45 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 77.38 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 74.07 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13 tokens</li><li>mean: 125.21 tokens</li><li>max: 245 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 123.54 tokens</li><li>max: 244 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 120.48 tokens</li><li>max: 238 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 74.07 tokens</li><li>max: 250 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 73.6 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 76.08 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 67.5 tokens</li><li>max: 174 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 72.52 tokens</li><li>max: 170 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 67.55 tokens</li><li>max: 164 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16 tokens</li><li>mean: 109.08 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 116.35 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 119.65 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 32 tokens</li><li>mean: 85.83 tokens</li><li>max: 246 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 85.03 tokens</li><li>max: 225 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 93.96 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 52.73 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 55.65 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 63.01 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 22.15 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 22.07 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 22.07 tokens</li><li>max: 55 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 23.03 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 18.42 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.36 tokens</li><li>max: 68 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 19.66 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 19.39 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 19.57 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 29.85 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 28.96 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 28.77 tokens</li><li>max: 58 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 138 tokens</li><li>mean: 242.59 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 165 tokens</li><li>mean: 242.86 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 173 tokens</li><li>mean: 243.06 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 26 tokens</li><li>mean: 31.69 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 31.28 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 31.16 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 22.62 tokens</li><li>max: 118 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 18.82 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 21.32 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 28 tokens</li><li>mean: 32.06 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 39 tokens</li><li>mean: 54.2 tokens</li><li>max: 132 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 53.61 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 40 tokens</li><li>mean: 54.16 tokens</li><li>max: 84 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 25 tokens</li><li>mean: 193.73 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 55 tokens</li><li>mean: 193.64 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 198.12 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 23.64 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 24.12 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 22.49 tokens</li><li>max: 120 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 35 tokens</li><li>mean: 68.15 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 68.3 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 68.79 tokens</li><li>max: 99 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 42 tokens</li><li>mean: 67.71 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 66.7 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 41 tokens</li><li>mean: 66.92 tokens</li><li>max: 96 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 28 tokens</li><li>mean: 57.78 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 57.44 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 56.86 tokens</li><li>max: 102 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 52 tokens</li><li>mean: 80.49 tokens</li><li>max: 143 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 82.22 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 49 tokens</li><li>mean: 78.96 tokens</li><li>max: 145 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 140 tokens</li><li>mean: 210.71 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: 208.74 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 110 tokens</li><li>mean: 243.25 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 101 tokens</li><li>mean: 243.17 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 113 tokens</li><li>mean: 251.67 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 19.72 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.13 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.0 tokens</li><li>max: 35 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 28.11 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 27.8 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 27.68 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 22.1 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 22.07 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 21.5 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 128 tokens</li><li>mean: 241.77 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 127 tokens</li><li>mean: 241.97 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 136 tokens</li><li>mean: 242.04 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 27.79 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 27.16 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 27.61 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 39.54 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 38.94 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 38.26 tokens</li><li>max: 89 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 44 tokens</li><li>mean: 50.37 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 50.35 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 50.61 tokens</li><li>max: 66 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 37 tokens</li><li>mean: 50.73 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 50.28 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 50.52 tokens</li><li>max: 74 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 66.26 tokens</li><li>max: 248 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 76.73 tokens</li><li>max: 248 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 75.5 tokens</li><li>max: 251 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13 tokens</li><li>mean: 22.04 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 22.03 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 21.9 tokens</li><li>max: 40 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 87 tokens</li><li>mean: 229.02 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 74 tokens</li><li>mean: 230.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 71 tokens</li><li>mean: 232.43 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 9.97 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.03 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.06 tokens</li><li>max: 18 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16 tokens</li><li>mean: 18.45 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 18.38 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 149 tokens</li><li>mean: 251.76 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 147 tokens</li><li>mean: 252.05 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 148 tokens</li><li>mean: 251.76 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 27.77 tokens</li><li>max: 199 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 27.23 tokens</li><li>max: 205 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 29.87 tokens</li><li>max: 194 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17 tokens</li><li>mean: 80.33 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 80.74 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 87.22 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 19.76 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 19.88 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.2 tokens</li><li>max: 44 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 55 tokens</li><li>mean: 79.4 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 79.17 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 53 tokens</li><li>mean: 80.1 tokens</li><li>max: 110 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16 tokens</li><li>mean: 21.76 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 21.66 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 21.78 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 35 tokens</li><li>mean: 52.41 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 51.02 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 50.98 tokens</li><li>max: 87 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 41.24 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 40.21 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 40.93 tokens</li><li>max: 185 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 24 tokens</li><li>mean: 73.7 tokens</li><li>max: 190 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 76.06 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 74.56 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 48 tokens</li><li>mean: 73.06 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 73.24 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 49 tokens</li><li>mean: 71.71 tokens</li><li>max: 102 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 24 tokens</li><li>mean: 225.8 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 232.86 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 231.22 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 26.75 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 26.13 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 26.34 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 12.16 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.05 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.96 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 32.85 tokens</li><li>max: 145 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 30.81 tokens</li><li>max: 171 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 30.26 tokens</li><li>max: 169 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 31.55 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 31.31 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 32.91 tokens</li><li>max: 84 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 31.46 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 31.8 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 28.95 tokens</li><li>max: 96 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14.87 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 14.54 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.22 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17 tokens</li><li>mean: 36.26 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 32.85 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 36.27 tokens</li><li>max: 72 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 33 tokens</li><li>mean: 70.42 tokens</li><li>max: 177 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 68.99 tokens</li><li>max: 146 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 69.69 tokens</li><li>max: 160 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 53 tokens</li><li>mean: 176.83 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 57 tokens</li><li>mean: 177.07 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 53 tokens</li><li>mean: 176.78 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 129 tokens</li><li>mean: 176.29 tokens</li><li>max: 253 tokens</li></ul> | <ul><li>min: 126 tokens</li><li>mean: 176.3 tokens</li><li>max: 249 tokens</li></ul> | <ul><li>min: 128 tokens</li><li>mean: 178.24 tokens</li><li>max: 254 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 38.33 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 36.79 tokens</li><li>max: 109 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 36.61 tokens</li><li>max: 113 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 52.9 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 50.44 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 55.89 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16.8 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 25.75 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 24.34 tokens</li><li>max: 59 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13 tokens</li><li>mean: 31.88 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 30.88 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 32.75 tokens</li><li>max: 89 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 31 tokens</li><li>mean: 36.16 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 35.75 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 35.41 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 59 tokens</li><li>mean: 239.92 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 58 tokens</li><li>mean: 240.94 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 64 tokens</li><li>mean: 248.75 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 43.81 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 42.63 tokens</li><li>max: 129 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 42.82 tokens</li><li>max: 133 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 194 tokens</li><li>mean: 253.35 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 198 tokens</li><li>mean: 252.85 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 195 tokens</li><li>mean: 252.78 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 26 tokens</li><li>mean: 134.17 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 138.56 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 138.25 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 33.28 tokens</li><li>max: 146 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 32.14 tokens</li><li>max: 146 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 32.96 tokens</li><li>max: 127 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 35 tokens</li><li>mean: 67.63 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 66.95 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 68.94 tokens</li><li>max: 105 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 27.7 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 27.88 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 26.97 tokens</li><li>max: 93 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16 tokens</li><li>mean: 21.79 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 21.63 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 21.79 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 198 tokens</li><li>mean: 255.88 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 238 tokens</li><li>mean: 255.93 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 135 tokens</li><li>mean: 238.3 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 155 tokens</li><li>mean: 237.61 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 154 tokens</li><li>mean: 239.64 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 45 tokens</li><li>mean: 72.32 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 72.29 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 70.87 tokens</li><li>max: 105 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 9.6 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.93 tokens</li><li>max: 20 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 39 tokens</li><li>mean: 67.66 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 67.16 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 68.48 tokens</li><li>max: 103 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 47.01 tokens</li><li>max: 137 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 46.18 tokens</li><li>max: 119 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 47.2 tokens</li><li>max: 128 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 67.77 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 71.21 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 72.24 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 38.73 tokens</li><li>max: 175 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 35.74 tokens</li><li>max: 175 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 34.18 tokens</li><li>max: 170 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 224.14 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 233.63 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 227.59 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 39 tokens</li><li>mean: 160.13 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 159.86 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 162.75 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 32.87 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 31.92 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 31.83 tokens</li><li>max: 75 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 11 tokens</li><li>mean: 16.25 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 16.02 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 16.89 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 22.54 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 21.16 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.91 tokens</li><li>max: 67 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 217.53 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 218.59 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 221.41 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 29 tokens</li><li>mean: 239.72 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 76 tokens</li><li>mean: 238.38 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 245.03 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 22.14 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.6 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.96 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 63 tokens</li><li>mean: 100.05 tokens</li><li>max: 158 tokens</li></ul> | <ul><li>min: 63 tokens</li><li>mean: 98.61 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 62 tokens</li><li>mean: 100.35 tokens</li><li>max: 141 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 15 tokens</li><li>mean: 29.61 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 29.31 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 29.28 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 201 tokens</li><li>mean: 254.96 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 99 tokens</li><li>mean: 254.28 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 173 tokens</li><li>mean: 255.13 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 12.86 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.64 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 18.79 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: 18.51 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 10.82 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.77 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.03 tokens</li><li>max: 26 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17 tokens</li><li>mean: 35.5 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 35.66 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 36.66 tokens</li><li>max: 55 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 27 tokens</li><li>mean: 51.18 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 50.72 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 11.46 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 11.44 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.4 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 17 tokens</li><li>mean: 31.13 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: 30.85 tokens</li><li>max: 92 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 25 tokens</li><li>mean: 28.87 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: 28.7 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 43 tokens</li><li>mean: 67.87 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 67.11 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 69.05 tokens</li><li>max: 96 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 2 tokens</li><li>mean: 19.89 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.13 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.22 tokens</li><li>max: 137 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16.78 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.12 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.72 tokens</li><li>max: 35 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 32 tokens</li><li>mean: 40.62 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 40.3 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 40.28 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 19.86 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 18.91 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 18.22 tokens</li><li>max: 36 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 9.3 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.08 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.11 tokens</li><li>max: 15 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 49 tokens</li><li>mean: 79.95 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>min: 53 tokens</li><li>mean: 79.51 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 79.5 tokens</li><li>max: 114 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 145 tokens</li><li>mean: 235.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 142 tokens</li><li>mean: 233.59 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 151 tokens</li><li>mean: 235.1 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 27 tokens</li><li>mean: 30.99 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 30.8 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 30.5 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 20.91 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 20.65 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 118.38 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 118.92 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 111.39 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 136.66 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 146.65 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 146.05 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16.69 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 14.58 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.26 tokens</li><li>max: 34 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 115 tokens</li><li>mean: 254.87 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 137 tokens</li><li>mean: 254.4 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 171 tokens</li><li>mean: 255.44 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 15.84 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.96 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.25 tokens</li><li>max: 36 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13 tokens</li><li>mean: 30.24 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 29.39 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 29.32 tokens</li><li>max: 122 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 69.68 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 70.66 tokens</li><li>max: 123 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 70.61 tokens</li><li>max: 123 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 11.71 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 11.65 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 11.73 tokens</li><li>max: 25 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 66.3 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 64.61 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 61.49 tokens</li><li>max: 161 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 15 tokens</li><li>mean: 32.3 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 31.56 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 32.13 tokens</li><li>max: 159 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13 tokens</li><li>mean: 39.73 tokens</li><li>max: 147 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 38.83 tokens</li><li>max: 149 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 39.61 tokens</li><li>max: 148 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 12.94 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: 12.76 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 19 tokens</li><li>mean: 35.21 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 34.25 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 33.3 tokens</li><li>max: 65 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 225.76 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 233.16 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 235.28 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 31 tokens</li><li>mean: 61.75 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 62.23 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 61.7 tokens</li><li>max: 92 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16.86 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.83 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.62 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 48 tokens</li><li>mean: 72.41 tokens</li><li>max: 118 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 72.48 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 72.1 tokens</li><li>max: 106 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 24.87 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 23.39 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 23.07 tokens</li><li>max: 61 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16 tokens</li><li>mean: 53.65 tokens</li><li>max: 200 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 53.64 tokens</li><li>max: 220 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 54.8 tokens</li><li>max: 202 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 72.71 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 71.93 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 72.67 tokens</li><li>max: 155 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 38 tokens</li><li>mean: 160.28 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 159.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 41 tokens</li><li>mean: 167.8 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 13 tokens</li><li>mean: 43.07 tokens</li><li>max: 127 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 44.72 tokens</li><li>max: 149 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 43.81 tokens</li><li>max: 113 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 150 tokens</li><li>mean: 200.63 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 150 tokens</li><li>mean: 200.9 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 142 tokens</li><li>mean: 200.85 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 53.59 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 53.09 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 53.95 tokens</li><li>max: 161 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 20.33 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 19.01 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.96 tokens</li><li>max: 34 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 40.09 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 40.77 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 39.86 tokens</li><li>max: 86 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 231.55 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 235.21 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 234.47 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 62 tokens</li><li>mean: 86.17 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 60 tokens</li><li>mean: 85.49 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 59 tokens</li><li>mean: 86.18 tokens</li><li>max: 131 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14.6 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.95 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.89 tokens</li><li>max: 93 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18 tokens</li><li>mean: 100.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 107.28 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 113.07 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 27.9 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 26.98 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 27.48 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 9.86 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.97 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.02 tokens</li><li>max: 20 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 15.04 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.05 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 13.84 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 179 tokens</li><li>mean: 246.31 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 170 tokens</li><li>mean: 245.93 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 161 tokens</li><li>mean: 247.13 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 44 tokens</li><li>mean: 95.17 tokens</li><li>max: 183 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 95.18 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 95.42 tokens</li><li>max: 183 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 229.66 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 118 tokens</li><li>mean: 235.49 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 229.46 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 101 tokens</li><li>mean: 254.9 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 218 tokens</li><li>mean: 255.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 101 tokens</li><li>mean: 254.9 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 75 tokens</li><li>mean: 254.99 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 204 tokens</li><li>mean: 255.6 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 75 tokens</li><li>mean: 255.19 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 20 tokens</li><li>mean: 54.89 tokens</li><li>max: 193 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 54.64 tokens</li><li>max: 175 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 54.78 tokens</li><li>max: 187 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 7.59 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.49 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.38 tokens</li><li>max: 11 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 188.4 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 190.16 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 192.07 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 16 tokens</li><li>mean: 30.48 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 30.05 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 30.65 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 21.2 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 20.77 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 21.41 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 24 tokens</li><li>mean: 28.0 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 27.91 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 28.3 tokens</li><li>max: 39 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 171 tokens</li><li>mean: 235.1 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 171 tokens</li><li>mean: 234.38 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 171 tokens</li><li>mean: 232.38 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 18.56 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 18.16 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 17.5 tokens</li><li>max: 55 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 248.87 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 250.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 84 tokens</li><li>mean: 251.62 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 11.6 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.22 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.34 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 29 tokens</li><li>mean: 50.99 tokens</li><li>max: 113 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 49.39 tokens</li><li>max: 134 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 51.48 tokens</li><li>max: 113 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 235 tokens</li><li>mean: 255.96 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 156 tokens</li><li>mean: 255.46 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 226 tokens</li><li>mean: 255.94 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 14 tokens</li><li>mean: 39.8 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 38.45 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 39.06 tokens</li><li>max: 86 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 26 tokens</li><li>mean: 106.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 108.12 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 110.93 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 44 tokens</li><li>mean: 94.08 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 97.06 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 118.77 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 30 tokens</li><li>mean: 52.36 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 50.14 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 53.21 tokens</li><li>max: 88 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 143 tokens</li><li>mean: 235.25 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 155 tokens</li><li>mean: 235.25 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 146 tokens</li><li>mean: 236.25 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 106.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 112.36 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 112.66 tokens</li><li>max: 249 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 1,018 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: 11 tokens</li><li>mean: 12.66 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 12.48 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> |
* Samples:
* Loss: [<code>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: 1,018 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: 25 tokens</li><li>mean: 51.53 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 52.0 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 51.44 tokens</li><li>max: 87 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,838 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: 12.74 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 152.32 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 119.75 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 49,676 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: 21.62 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.07 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.21 tokens</li><li>max: 44 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 5,070 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: 29.35 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 33.94 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 56.42 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 99,352 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: 11.86 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 88.89 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 70.8 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,838 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: 15.24 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 210.26 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 194.92 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 11.73 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 39.85 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 45.43 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 10,140 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: 27.77 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 226.87 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 41 tokens</li><li>mean: 232.14 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 173,354 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: 9.07 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 82.14 tokens</li><li>max: 237 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 80.54 tokens</li><li>max: 252 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,838 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: 16.73 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 82.94 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 86.15 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,838 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: 14.05 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 153.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 162.67 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 29,908 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: 88.36 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 100 tokens</li><li>mean: 112.02 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 83 tokens</li><li>mean: 113.07 tokens</li><li>max: 147 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 4,055 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: 27.68 tokens</li><li>max: 137 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 26.84 tokens</li><li>max: 137 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 26.34 tokens</li><li>max: 91 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 11,661 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: 9.5 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.05 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.45 tokens</li><li>max: 25 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 11,661 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: 9.62 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.19 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.59 tokens</li><li>max: 24 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 198,192 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: 49.55 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 49.51 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 48.42 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 10,139 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: 15.81 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.77 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.1 tokens</li><li>max: 73 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 16,224 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: 22.04 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.71 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 21.99 tokens</li><li>max: 72 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 24,839 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: 207.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 223.93 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 206.87 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 15,715 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: 10.27 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.62 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.01 tokens</li><li>max: 33 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 15,715 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: 10.22 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.74 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.43 tokens</li><li>max: 28 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 49,677 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: 67.17 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 88.29 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 64.96 tokens</li><li>max: 252 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 27,373 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: 20.58 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.26 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 21.1 tokens</li><li>max: 59 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 5,070 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: 15.28 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 37.6 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 38.13 tokens</li><li>max: 239 tokens</li></ul> |
* Samples:
* Loss: [<code>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: 4 tokens</li><li>mean: 16.04 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 142.75 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 144.56 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
* Loss: [<code>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`: 512
- `per_device_eval_batch_size`: 512
- `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`: 512
- `per_device_eval_batch_size`: 512
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step  | Training Loss | loss   | medi-mteb-dev_max_accuracy |
|:------:|:-----:|:-------------:|:------:|:--------------------------:|
| 0      | 0     | -             | -      | 0.8705                     |
| 0.1308 | 500   | 2.1744        | 1.5723 | 0.8786                     |
| 0.2616 | 1000  | 1.9245        | 1.5045 | 0.8851                     |
| 0.3925 | 1500  | 1.9833        | 1.4719 | 0.8882                     |
| 0.5233 | 2000  | 1.7492        | 1.4434 | 0.8909                     |
| 0.6541 | 2500  | 1.8815        | 1.4244 | 0.8935                     |
| 0.7849 | 3000  | 1.7921        | 1.4064 | 0.8949                     |
| 0.9158 | 3500  | 1.8495        | 1.3894 | 0.8956                     |
| 1.0466 | 4000  | 1.7415        | 1.3744 | 0.8966                     |
| 1.1774 | 4500  | 1.8663        | 1.3619 | 0.9005                     |
| 1.3082 | 5000  | 1.7016        | 1.3520 | 0.8979                     |
| 1.4390 | 5500  | 1.7308        | 1.3467 | 0.9007                     |
| 1.5699 | 6000  | 1.6965        | 1.3346 | 0.9021                     |
| 1.7007 | 6500  | 1.7355        | 1.3251 | 0.9018                     |
| 1.8315 | 7000  | 1.6783        | 1.3156 | 0.9031                     |
| 1.9623 | 7500  | 1.6381        | 1.3101 | 0.9047                     |
| 2.0931 | 8000  | 1.7169        | 1.3056 | 0.9044                     |
| 2.2240 | 8500  | 1.6527        | 1.3070 | 0.9039                     |
| 2.3548 | 9000  | 1.7078        | 1.2977 | 0.9055                     |
| 2.4856 | 9500  | 1.533         | 1.2991 | 0.9050                     |
| 2.6164 | 10000 | 1.6676        | 1.2916 | 0.9057                     |
| 2.7473 | 10500 | 1.5866        | 1.2885 | 0.9053                     |
| 2.8781 | 11000 | 1.641         | 1.2765 | 0.9066                     |
| 3.0089 | 11500 | 1.5193        | 1.2816 | 0.9062                     |
| 3.1397 | 12000 | 1.6907        | 1.2804 | 0.9065                     |
| 3.2705 | 12500 | 1.557         | 1.2684 | 0.9065                     |
| 3.4014 | 13000 | 1.6808        | 1.2711 | 0.9075                     |
| 3.5322 | 13500 | 1.4751        | 1.2700 | 0.9072                     |
| 3.6630 | 14000 | 1.5934        | 1.2692 | 0.9081                     |
| 3.7938 | 14500 | 1.5395        | 1.2672 | 0.9087                     |
| 3.9246 | 15000 | 1.5809        | 1.2678 | 0.9072                     |
| 4.0555 | 15500 | 1.4972        | 1.2621 | 0.9089                     |
| 4.1863 | 16000 | 1.614         | 1.2690 | 0.9070                     |
| 4.3171 | 16500 | 1.5186        | 1.2625 | 0.9091                     |
| 4.4479 | 17000 | 1.5239        | 1.2629 | 0.9079                     |
| 4.5788 | 17500 | 1.5354        | 1.2569 | 0.9086                     |
| 4.7096 | 18000 | 1.5134        | 1.2559 | 0.9095                     |
| 4.8404 | 18500 | 1.5237        | 1.2494 | 0.9100                     |
| 4.9712 | 19000 | 1.5038        | 1.2486 | 0.9113                     |
| 5.1020 | 19500 | 1.5527        | 1.2493 | 0.9098                     |
| 5.2329 | 20000 | 1.5018        | 1.2521 | 0.9102                     |
| 5.3637 | 20500 | 1.584         | 1.2496 | 0.9095                     |
| 5.4945 | 21000 | 1.3948        | 1.2467 | 0.9102                     |
| 5.6253 | 21500 | 1.5118        | 1.2487 | 0.9098                     |
| 5.7561 | 22000 | 1.458         | 1.2471 | 0.9098                     |
| 5.8870 | 22500 | 1.5158        | 1.2367 | 0.9105                     |
| 6.0178 | 23000 | 1.4091        | 1.2480 | 0.9096                     |
| 6.1486 | 23500 | 1.5823        | 1.2456 | 0.9114                     |
| 6.2794 | 24000 | 1.4383        | 1.2404 | 0.9101                     |
| 6.4103 | 24500 | 1.5606        | 1.2431 | 0.9100                     |
| 6.5411 | 25000 | 1.3906        | 1.2386 | 0.9112                     |
| 6.6719 | 25500 | 1.4887        | 1.2382 | 0.9103                     |
| 6.8027 | 26000 | 1.4347        | 1.2384 | 0.9112                     |
| 6.9335 | 26500 | 1.4733        | 1.2395 | 0.9113                     |
| 7.0644 | 27000 | 1.4323        | 1.2385 | 0.9111                     |
| 7.1952 | 27500 | 1.505         | 1.2413 | 0.9107                     |
| 7.3260 | 28000 | 1.4648        | 1.2362 | 0.9114                     |
| 7.4568 | 28500 | 1.4252        | 1.2361 | 0.9116                     |
| 7.5877 | 29000 | 1.458         | 1.2344 | 0.9118                     |
| 7.7185 | 29500 | 1.4309        | 1.2357 | 0.9120                     |
| 7.8493 | 30000 | 1.4431        | 1.2330 | 0.9114                     |
| 7.9801 | 30500 | 1.4266        | 1.2306 | 0.9127                     |
| 8.1109 | 31000 | 1.4803        | 1.2328 | 0.9118                     |
| 8.2418 | 31500 | 1.414         | 1.2345 | 0.9110                     |
| 8.3726 | 32000 | 1.5456        | 1.2343 | 0.9116                     |
| 8.5034 | 32500 | 1.346         | 1.2324 | 0.9118                     |
| 8.6342 | 33000 | 1.4467        | 1.2315 | 0.9118                     |
| 8.7650 | 33500 | 1.3864        | 1.2330 | 0.9119                     |
| 8.8959 | 34000 | 1.4806        | 1.2277 | 0.9119                     |
| 9.0267 | 34500 | 1.3381        | 1.2330 | 0.9119                     |
| 9.1575 | 35000 | 1.5277        | 1.2315 | 0.9121                     |
| 9.2883 | 35500 | 1.3966        | 1.2309 | 0.9112                     |
| 9.4192 | 36000 | 1.4921        | 1.2321 | 0.9117                     |
| 9.5500 | 36500 | 1.3668        | 1.2303 | 0.9118                     |
| 9.6808 | 37000 | 1.4407        | 1.2308 | 0.9121                     |
| 9.8116 | 37500 | 1.3852        | 1.2314 | 0.9118                     |
| 9.9424 | 38000 | 1.4329        | 1.2300 | 0.9120                     |


### Framework Versions
- Python: 3.10.10
- Sentence Transformers: 3.1.0.dev0
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->