sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:3012496 loss:MatryoshkaLoss loss:CachedMultipleNegativesRankingLoss en dataset:sentence-transformers/gooaq arxiv:1908.10084 arxiv:2205.13147 arxiv:2101.06983 base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index co2_eq_emissions autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: google-bert/bert-base-uncased
datasets:
- sentence-transformers/gooaq
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3012496
- loss:MatryoshkaLoss
- loss:CachedMultipleNegativesRankingLoss
widget:
- source_sentence: are the sequels better than the prequels?
  sentences:
  - '[''Automatically.'', ''When connected to car Bluetooth and,'', ''Manually.'']'
  - The prequels are also not scared to take risks, making movies which are very different
    from the original trilogy. The sequel saga, on the other hand, are technically
    better made films, the acting is more consistent, the CGI is better and the writing
    is stronger, however it falls down in many other places.
  - While both public and private sectors use budgets as a key planning tool, public
    bodies balance budgets, while private sector firms use budgets to predict operating
    results. The public sector budget matches expenditures on mandated assets and
    services with receipts of public money such as taxes and fees.
- source_sentence: are there bbqs at lake leschenaultia?
  sentences:
  - Vestavia Hills. The hummingbird, or, el zunzún as they are often called in the
    Caribbean, have such a nickname because of their quick movements. The ruby-throated
    hummingbird, the most commonly seen hummingbird in Alabama, is the inspiration
    for this restaurant.
  - Common causes of abdominal tenderness Abdominal tenderness is generally a sign
    of inflammation or other acute processes in one or more organs. The organs are
    located around the tender area. Acute processes mean sudden pressure caused by
    something. For example, twisted or blocked organs can cause point tenderness.
  - ​Located on 168 hectares of nature reserve, Lake Leschenaultia is the perfect
    spot for a family day out in the Perth Hills. The Lake offers canoeing, swimming,
    walk and cycle trails, as well as picnic, BBQ and camping facilities. ... There
    are picnic tables set amongst lovely Wandoo trees.
- source_sentence: how much folic acid should you take prenatal?
  sentences:
  - Folic acid is a pregnancy superhero! Taking a prenatal vitamin with the recommended
    400 micrograms (mcg) of folic acid before and during pregnancy can help prevent
    birth defects of your baby's brain and spinal cord. Take it every day and go ahead
    and have a bowl of fortified cereal, too.
  - '[''You must be unemployed through no fault of your own, as defined by Virginia
    law.'', ''You must have earned at least a minimum amount in wages before you were
    unemployed.'', ''You must be able and available to work, and you must be actively
    seeking employment.'']'
  - Wallpaper is printed in batches of rolls. It is important to have the same batch
    number, to ensure colours match exactly. The batch number is usually located on
    the wallpaper label close to the pattern number. Remember batch numbers also apply
    to white wallpapers, as different batches can be different shades of white.
- source_sentence: what is the difference between minerals and electrolytes?
  sentences:
  - 'North: Just head north of Junk Junction like so. South: Head below Lucky Landing.
    East: You''re basically landing between Lonely Lodge and the Racetrack. West:
    The sign is west of Snobby Shores.'
  - The fasting glucose tolerance test is the simplest and fastest way to measure
    blood glucose and diagnose diabetes. Fasting means that you have had nothing to
    eat or drink (except water) for 8 to 12 hours before the test.
  - In other words, the term “electrolyte” typically implies ionized minerals dissolved
    within water and beverages. Electrolytes are typically minerals, whereas minerals
    may or may not be electrolytes.
- source_sentence: how can i download youtube videos with internet download manager?
  sentences:
  - '[''Go to settings and then click on extensions (top left side in chrome).'',
    ''Minimise your browser and open the location (folder) where IDM is installed.
    ... '', ''Find the file “IDMGCExt. ... '', ''Drag this file to your chrome browser
    and drop to install the IDM extension.'']'
  - Coca-Cola might rot your teeth and load your body with sugar and calories, but
    it's actually an effective and safe first line of treatment for some stomach blockages,
    researchers say.
  - To fix a disabled iPhone or iPad without iTunes, you have to erase your device.
    Click on the "Erase iPhone" option and confirm your selection. Wait for a while
    as the "Find My iPhone" feature will remotely erase your iOS device. Needless
    to say, it will also disable its lock.
co2_eq_emissions:
  emissions: 242.52371141034885
  energy_consumed: 0.623932244779674
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 1.619
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: bert-base-uncased adapter finetuned on GooAQ pairs
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoClimateFEVER
      type: NanoClimateFEVER
    metrics:
    - type: cosine_accuracy@1
      value: 0.24
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.42
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.46
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.56
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.24
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15999999999999998
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10800000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.13166666666666665
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.20833333333333337
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.24166666666666664
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.29666666666666663
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.25516520961338873
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3378809523809523
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.20756281994556017
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoDBPedia
      type: NanoDBPedia
    metrics:
    - type: cosine_accuracy@1
      value: 0.54
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.84
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.92
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.54
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.4866666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.4440000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.3899999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.046781664425339056
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.11117774881295754
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.15829952609979633
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.2554819210350403
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4644109757573673
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6797460317460318
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3253011706807197
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoFEVER
      type: NanoFEVER
    metrics:
    - type: cosine_accuracy@1
      value: 0.54
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.82
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.92
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.54
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2733333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.184
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09599999999999997
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.53
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7766666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8566666666666666
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8866666666666667
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7348538316509182
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6961904761904762
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6788071339639872
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoFiQA2018
      type: NanoFiQA2018
    metrics:
    - type: cosine_accuracy@1
      value: 0.24
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.24
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08800000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11474603174603175
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.22874603174603172
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3166031746031746
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3986031746031745
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2925721974861802
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3385
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2372091627126374
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoHotpotQA
      type: NanoHotpotQA
    metrics:
    - type: cosine_accuracy@1
      value: 0.6
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.68
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.74
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.88
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2866666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.192
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.118
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.43
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.48
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.59
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5291588954628265
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6639365079365079
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.45230644038161627
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoMSMARCO
      type: NanoMSMARCO
    metrics:
    - type: cosine_accuracy@1
      value: 0.28
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.48
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.54
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.66
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.28
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10800000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.066
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.28
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.48
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.54
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.66
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.46795689507567784
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4079126984126984
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.42763462709531985
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNFCorpus
      type: NanoNFCorpus
    metrics:
    - type: cosine_accuracy@1
      value: 0.32
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.48
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.56
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.32
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.30666666666666664
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.244
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.184
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.02092621665706462
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.053426190783308986
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.06393651269284006
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.08045448545888809
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.23067635403503162
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.39788888888888885
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.09661097314535905
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNQ
      type: NanoNQ
    metrics:
    - type: cosine_accuracy@1
      value: 0.38
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.54
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.62
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.74
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.38
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.128
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07600000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.38
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.51
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.71
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5386606354769653
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.490547619047619
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.48961052316839493
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoQuoraRetrieval
      type: NanoQuoraRetrieval
    metrics:
    - type: cosine_accuracy@1
      value: 0.84
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.94
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.98
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.84
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.38666666666666655
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.24799999999999997
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.12999999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7573333333333332
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.912
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.946
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9793333333333334
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9157663307482551
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9009999999999999
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8893741502029173
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoSCIDOCS
      type: NanoSCIDOCS
    metrics:
    - type: cosine_accuracy@1
      value: 0.26
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.46
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.68
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.26
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.20666666666666664
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.184
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.126
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.054000000000000006
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.12866666666666668
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.18966666666666668
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.25866666666666666
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.24181947685643387
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3803571428571429
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.18652061021747493
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoArguAna
      type: NanoArguAna
    metrics:
    - type: cosine_accuracy@1
      value: 0.16
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.58
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.74
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.84
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.16
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.19333333333333336
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14800000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08399999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.16
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.58
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.74
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.84
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5045313323048141
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3963333333333333
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.40074428294573644
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoSciFact
      type: NanoSciFact
    metrics:
    - type: cosine_accuracy@1
      value: 0.42
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.58
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.62
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.64
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.42
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.20666666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07600000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.4
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.56
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.605
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.64
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5380316349319392
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5056666666666666
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5079821472790408
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoTouche2020
      type: NanoTouche2020
    metrics:
    - type: cosine_accuracy@1
      value: 0.4489795918367347
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8979591836734694
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9183673469387755
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9795918367346939
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4489795918367347
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.4965986394557823
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.45714285714285713
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.38979591836734706
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.03475887574057735
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.11109807516506923
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1656210426064535
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.2684807614936963
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.43233093716838594
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6532555879494653
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.33493945959592186
      name: Cosine Map@100
  - task:
      type: nano-beir
      name: Nano BEIR
    dataset:
      name: NanoBEIR mean
      type: NanoBEIR_mean
    metrics:
    - type: cosine_accuracy@1
      value: 0.4053061224489796
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6213814756671899
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6891051805337519
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7676609105180533
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4053061224489796
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2694819466248038
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20962637362637365
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.14567660910518054
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.24693944527453943
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3915472856287718
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4541123273847895
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5280272058403178
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4727642081975526
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5268627619545987
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.40266180779497585
      name: Cosine Map@100
---

# bert-base-uncased adapter finetuned on GooAQ pairs

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)
```

## 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("tomaarsen/bert-base-uncased-gooaq")
# Run inference
sentences = [
    'how can i download youtube videos with internet download manager?',
    "['Go to settings and then click on extensions (top left side in chrome).', 'Minimise your browser and open the location (folder) where IDM is installed. ... ', 'Find the file “IDMGCExt. ... ', 'Drag this file to your chrome browser and drop to install the IDM extension.']",
    "Coca-Cola might rot your teeth and load your body with sugar and calories, but it's actually an effective and safe first line of treatment for some stomach blockages, researchers say.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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</details>
-->

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | NanoClimateFEVER | NanoDBPedia | NanoFEVER  | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ     | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| cosine_accuracy@1   | 0.24             | 0.54        | 0.54       | 0.24         | 0.6          | 0.28        | 0.32         | 0.38       | 0.84               | 0.26        | 0.16        | 0.42        | 0.449          |
| cosine_accuracy@3   | 0.42             | 0.8         | 0.82       | 0.4          | 0.68         | 0.48        | 0.48         | 0.54       | 0.94               | 0.46        | 0.58        | 0.58        | 0.898          |
| cosine_accuracy@5   | 0.46             | 0.84        | 0.9        | 0.5          | 0.74         | 0.54        | 0.5          | 0.62       | 0.98               | 0.6         | 0.74        | 0.62        | 0.9184         |
| cosine_accuracy@10  | 0.56             | 0.92        | 0.92       | 0.6          | 0.88         | 0.66        | 0.56         | 0.74       | 1.0                | 0.68        | 0.84        | 0.64        | 0.9796         |
| cosine_precision@1  | 0.24             | 0.54        | 0.54       | 0.24         | 0.6          | 0.28        | 0.32         | 0.38       | 0.84               | 0.26        | 0.16        | 0.42        | 0.449          |
| cosine_precision@3  | 0.16             | 0.4867      | 0.2733     | 0.16         | 0.2867       | 0.16        | 0.3067       | 0.18       | 0.3867             | 0.2067      | 0.1933      | 0.2067      | 0.4966         |
| cosine_precision@5  | 0.108            | 0.444       | 0.184      | 0.14         | 0.192        | 0.108       | 0.244        | 0.128      | 0.248              | 0.184       | 0.148       | 0.14        | 0.4571         |
| cosine_precision@10 | 0.07             | 0.39        | 0.096      | 0.088        | 0.118        | 0.066       | 0.184        | 0.076      | 0.13               | 0.126       | 0.084       | 0.076       | 0.3898         |
| cosine_recall@1     | 0.1317           | 0.0468      | 0.53       | 0.1147       | 0.3          | 0.28        | 0.0209       | 0.38       | 0.7573             | 0.054       | 0.16        | 0.4         | 0.0348         |
| cosine_recall@3     | 0.2083           | 0.1112      | 0.7767     | 0.2287       | 0.43         | 0.48        | 0.0534       | 0.51       | 0.912              | 0.1287      | 0.58        | 0.56        | 0.1111         |
| cosine_recall@5     | 0.2417           | 0.1583      | 0.8567     | 0.3166       | 0.48         | 0.54        | 0.0639       | 0.6        | 0.946              | 0.1897      | 0.74        | 0.605       | 0.1656         |
| cosine_recall@10    | 0.2967           | 0.2555      | 0.8867     | 0.3986       | 0.59         | 0.66        | 0.0805       | 0.71       | 0.9793             | 0.2587      | 0.84        | 0.64        | 0.2685         |
| **cosine_ndcg@10**  | **0.2552**       | **0.4644**  | **0.7349** | **0.2926**   | **0.5292**   | **0.468**   | **0.2307**   | **0.5387** | **0.9158**         | **0.2418**  | **0.5045**  | **0.538**   | **0.4323**     |
| cosine_mrr@10       | 0.3379           | 0.6797      | 0.6962     | 0.3385       | 0.6639       | 0.4079      | 0.3979       | 0.4905     | 0.901              | 0.3804      | 0.3963      | 0.5057      | 0.6533         |
| cosine_map@100      | 0.2076           | 0.3253      | 0.6788     | 0.2372       | 0.4523       | 0.4276      | 0.0966       | 0.4896     | 0.8894             | 0.1865      | 0.4007      | 0.508       | 0.3349         |

#### Nano BEIR

* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.4053     |
| cosine_accuracy@3   | 0.6214     |
| cosine_accuracy@5   | 0.6891     |
| cosine_accuracy@10  | 0.7677     |
| cosine_precision@1  | 0.4053     |
| cosine_precision@3  | 0.2695     |
| cosine_precision@5  | 0.2096     |
| cosine_precision@10 | 0.1457     |
| cosine_recall@1     | 0.2469     |
| cosine_recall@3     | 0.3915     |
| cosine_recall@5     | 0.4541     |
| cosine_recall@10    | 0.528      |
| **cosine_ndcg@10**  | **0.4728** |
| cosine_mrr@10       | 0.5269     |
| cosine_map@100      | 0.4027     |

<!--
## Bias, Risks and Limitations

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### gooaq

* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | question                                                                          | answer                                                                              |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.48 tokens</li><li>max: 138 tokens</li></ul> |
* Samples:
  | question                                                                           | answer                                                                                                                                                                                                                                                                                                                |
  |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what is the difference between broilers and layers?</code>                   | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code>                |
  | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> |
  | <code>is kamagra same as viagra?</code>                                            | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code>                               |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "CachedMultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### gooaq

* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 evaluation samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | question                                                                          | answer                                                                              |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 11.88 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 61.03 tokens</li><li>max: 127 tokens</li></ul> |
* Samples:
  | question                                                                     | answer                                                                                                                                                                                                                                                                                                                                     |
  |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>how do i program my directv remote with my tv?</code>                  | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code>                                                                                               |
  | <code>are rodrigues fruit bats nocturnal?</code>                             | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code>                                                                                                  |
  | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "CachedMultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 1024
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: 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`: 1024
- `per_device_eval_batch_size`: 1024
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `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`: 12
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `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`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0      | 0    | -             | -               | 0.1046                          | 0.2182                     | 0.1573                   | 0.0575                      | 0.2597                      | 0.1602                     | 0.0521                      | 0.0493                | 0.7310                            | 0.1320                     | 0.2309                     | 0.1240                     | 0.0970                        | 0.1826                       |
| 0.0010 | 1    | 28.4268       | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0256 | 25   | 24.7252       | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0512 | 50   | 13.3628       | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0768 | 75   | 7.843         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1024 | 100  | 5.7393        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1279 | 125  | 4.6576        | 2.3368          | 0.2890                          | 0.4610                     | 0.7408                   | 0.2882                      | 0.5446                      | 0.4091                     | 0.2179                      | 0.4664                | 0.9079                            | 0.2394                     | 0.5433                     | 0.5003                     | 0.4318                        | 0.4646                       |
| 0.1535 | 150  | 4.0846        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1791 | 175  | 3.7129        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2047 | 200  | 3.4899        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2303 | 225  | 3.3263        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2559 | 250  | 3.2013        | 1.6545          | 0.2622                          | 0.4744                     | 0.7456                   | 0.2934                      | 0.5371                      | 0.4326                     | 0.2290                      | 0.5157                | 0.9130                            | 0.2577                     | 0.5189                     | 0.5155                     | 0.4302                        | 0.4712                       |
| 0.2815 | 275  | 2.9109        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3071 | 300  | 2.9064        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3327 | 325  | 2.8215        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3582 | 350  | 2.7893        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3838 | 375  | 2.6663        | 1.4146          | 0.2629                          | 0.4657                     | 0.7330                   | 0.2853                      | 0.5299                      | 0.4346                     | 0.2311                      | 0.5216                | 0.9172                            | 0.2513                     | 0.5133                     | 0.5429                     | 0.4287                        | 0.4706                       |
| 0.4094 | 400  | 2.6672        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4350 | 425  | 2.5587        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4606 | 450  | 2.5001        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4862 | 475  | 2.4476        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5118 | 500  | 2.4127        | 1.2843          | 0.2565                          | 0.4668                     | 0.7289                   | 0.2838                      | 0.5392                      | 0.4599                     | 0.2284                      | 0.5238                | 0.9021                            | 0.2416                     | 0.4971                     | 0.5349                     | 0.4320                        | 0.4688                       |
| 0.5374 | 525  | 2.414         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5629 | 550  | 2.3723        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5885 | 575  | 2.3418        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6141 | 600  | 2.2862        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6397 | 625  | 2.207         | 1.2078          | 0.2613                          | 0.4542                     | 0.7382                   | 0.2817                      | 0.5230                      | 0.4664                     | 0.2282                      | 0.5266                | 0.9095                            | 0.2453                     | 0.5127                     | 0.5414                     | 0.4239                        | 0.4702                       |
| 0.6653 | 650  | 2.2305        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6909 | 675  | 2.2409        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7165 | 700  | 2.2001        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7421 | 725  | 2.1923        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7677 | 750  | 2.195         | 1.1538          | 0.2549                          | 0.4671                     | 0.7333                   | 0.2804                      | 0.5265                      | 0.4659                     | 0.2321                      | 0.5331                | 0.9086                            | 0.2429                     | 0.5070                     | 0.5430                     | 0.4369                        | 0.4717                       |
| 0.7932 | 775  | 2.1826        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8188 | 800  | 2.1754        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8444 | 825  | 2.1141        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8700 | 850  | 2.1572        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8956 | 875  | 2.1126        | 1.1256          | 0.2505                          | 0.4622                     | 0.7293                   | 0.2857                      | 0.5286                      | 0.4823                     | 0.2308                      | 0.5397                | 0.9158                            | 0.2412                     | 0.5050                     | 0.5365                     | 0.4387                        | 0.4728                       |
| 0.9212 | 900  | 2.0755        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9468 | 925  | 2.1032        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9724 | 950  | 2.1211        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9980 | 975  | 2.0826        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 1.0    | 977  | -             | -               | 0.2552                          | 0.4644                     | 0.7349                   | 0.2926                      | 0.5292                      | 0.4680                     | 0.2307                      | 0.5387                | 0.9158                            | 0.2418                     | 0.5045                     | 0.5380                     | 0.4323                        | 0.4728                       |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.624 kWh
- **Carbon Emitted**: 0.243 kg of CO2
- **Hours Used**: 1.619 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.4.0.dev0
- Transformers: 4.46.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.35.0.dev0
- Datasets: 2.20.0
- Tokenizers: 0.20.3

## Citation

### BibTeX

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

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

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