metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:3012496
  - loss:MatryoshkaLoss
  - loss:CachedMultipleNegativesRankingLoss
base_model: google-bert/bert-base-uncased
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.
datasets:
  - sentence-transformers/gooaq
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
co2_eq_emissions:
  emissions: 249.86917485332245
  energy_consumed: 0.6428296609055844
  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: 1.727
  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: 0.3
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.42
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.48
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.54
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.3
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11600000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.066
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.14833333333333332
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.21
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.25666666666666665
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.2866666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2612531493211831
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3718333333333333
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2163485410063536
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: cosine_accuracy@1
            value: 0.48
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.78
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.82
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.92
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.48
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.4599999999999999
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.4159999999999999
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.39
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.04444293833661297
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.10924065240694858
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.14497857436843284
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.24069548747927993
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.45073427319400694
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6354682539682539
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3182747550673792
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: cosine_accuracy@1
            value: 0.6
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.84
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.96
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.184
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09799999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.59
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8566666666666666
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9066666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7556216606985078
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.719190476190476
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.701651515151515
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        metrics:
          - type: cosine_accuracy@1
            value: 0.22
            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: 0.22
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.14
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09799999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.11441269841269841
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.21891269841269842
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3109126984126984
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.40793650793650793
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2963633422018188
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.33072222222222225
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.23341351928423923
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: cosine_accuracy@1
            value: 0.64
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.74
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.82
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.84
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.64
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31333333333333335
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.22399999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.11799999999999997
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.32
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.47
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.56
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.59
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5584295792789493
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7015
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.49543351785464007
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.22
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.46
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.54
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.68
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.22
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.15333333333333332
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10800000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.068
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.22
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.46
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.54
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.68
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.44155458168172074
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3666904761904761
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.38140126670451624
            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: 0.44
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.46
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.32
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2866666666666666
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.244
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.17800000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.022867372385014545
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.051610132551984836
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.061993511339545566
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.07344138386002937
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.22405550472948219
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3782222222222222
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.08778657539162772
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.4
            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: 0.7
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.124
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07200000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.53
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.59
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.67
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5271006159134835
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4858809523809523
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4878346435046129
            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: 0.98
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.98
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            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: 0.23999999999999994
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.12999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7573333333333333
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9286666666666668
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9359999999999999
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9793333333333334
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9154478750600358
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9053333333333333
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8889771382049948
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: cosine_accuracy@1
            value: 0.3
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.36
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.54
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.68
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.3
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19200000000000003
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.142
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.06466666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.12466666666666669
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.19666666666666666
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.2906666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2646043570275534
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3836031746031746
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.20582501612453505
            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: 0.52
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.72
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.16
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.17333333333333337
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.14400000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.16
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.52
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.72
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.47137188069353025
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.36633333333333323
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3750999024240443
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: cosine_accuracy@1
            value: 0.38
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.56
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.64
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.38
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.14
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07800000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.345
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.525
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.615
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.68
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.521095291928473
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4848333333333332
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4707221516167083
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: cosine_accuracy@1
            value: 0.3673469387755102
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8571428571428571
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9387755102040817
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.3673469387755102
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.4965986394557823
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.4489795918367347
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.39387755102040817
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.03066633506656198
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.1123508290418132
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.1616156991422983
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.2674040762687923
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.42905651691216934
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6237204405571752
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.32876348596122706
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.40210361067503925
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.6074725274725276
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6891365777080062
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7630769230769231
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.40210361067503925
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26691784406070124
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2093061224489796
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.14706750392464676
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.247517129041094
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.38926520351898297
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.4577308064048442
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5286777529906109
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.47051450989545496
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.519487042436022
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.399348617561261
            name: Cosine Map@100

bert-base-uncased adapter finetuned on GooAQ pairs

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased on the 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- gooaq
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/bert-base-uncased-gooaq-peft")
# 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]

Evaluation

Metrics

Information Retrieval

Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
Evaluated with InformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.3	0.48	0.6	0.22	0.64	0.22	0.32	0.4	0.84	0.3	0.16	0.38	0.3673
cosine_accuracy@3	0.42	0.78	0.84	0.4	0.74	0.46	0.44	0.54	0.98	0.36	0.52	0.56	0.8571
cosine_accuracy@5	0.48	0.82	0.9	0.5	0.82	0.54	0.46	0.62	0.98	0.54	0.72	0.64	0.9388
cosine_accuracy@10	0.54	0.92	0.96	0.6	0.84	0.68	0.5	0.7	1.0	0.68	0.8	0.7	1.0
cosine_precision@1	0.3	0.48	0.6	0.22	0.64	0.22	0.32	0.4	0.84	0.3	0.16	0.38	0.3673
cosine_precision@3	0.16	0.46	0.28	0.18	0.3133	0.1533	0.2867	0.18	0.3867	0.2	0.1733	0.2	0.4966
cosine_precision@5	0.116	0.416	0.184	0.14	0.224	0.108	0.244	0.124	0.24	0.192	0.144	0.14	0.449
cosine_precision@10	0.066	0.39	0.098	0.098	0.118	0.068	0.178	0.072	0.13	0.142	0.08	0.078	0.3939
cosine_recall@1	0.1483	0.0444	0.59	0.1144	0.32	0.22	0.0229	0.4	0.7573	0.0647	0.16	0.345	0.0307
cosine_recall@3	0.21	0.1092	0.8	0.2189	0.47	0.46	0.0516	0.53	0.9287	0.1247	0.52	0.525	0.1124
cosine_recall@5	0.2567	0.145	0.8567	0.3109	0.56	0.54	0.062	0.59	0.936	0.1967	0.72	0.615	0.1616
cosine_recall@10	0.2867	0.2407	0.9067	0.4079	0.59	0.68	0.0734	0.67	0.9793	0.2907	0.8	0.68	0.2674
cosine_ndcg@10	0.2613	0.4507	0.7556	0.2964	0.5584	0.4416	0.2241	0.5271	0.9154	0.2646	0.4714	0.5211	0.4291
cosine_mrr@10	0.3718	0.6355	0.7192	0.3307	0.7015	0.3667	0.3782	0.4859	0.9053	0.3836	0.3663	0.4848	0.6237
cosine_map@100	0.2163	0.3183	0.7017	0.2334	0.4954	0.3814	0.0878	0.4878	0.889	0.2058	0.3751	0.4707	0.3288

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.4021
cosine_accuracy@3	0.6075
cosine_accuracy@5	0.6891
cosine_accuracy@10	0.7631
cosine_precision@1	0.4021
cosine_precision@3	0.2669
cosine_precision@5	0.2093
cosine_precision@10	0.1471
cosine_recall@1	0.2475
cosine_recall@3	0.3893
cosine_recall@5	0.4577
cosine_recall@10	0.5287
cosine_ndcg@10	0.4705
cosine_mrr@10	0.5195
cosine_map@100	0.3993

Training Details

Training Dataset

gooaq

Dataset: gooaq at b089f72
Size: 3,012,496 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.86 tokens
max: 21 tokens

min: 14 tokens
mean: 60.48 tokens
max: 138 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.86 tokens max: 21 tokens	min: 14 tokens mean: 60.48 tokens max: 138 tokens

Samples:

question	answer
`what is the difference between broilers and layers?`	`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.`
`what is the difference between chronological order and spatial order?`	`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.`
`is kamagra same as viagra?`	`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.`

Loss: MatryoshkaLoss with these parameters:

{
    "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 at b089f72
Size: 3,012,496 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.88 tokens
max: 22 tokens

min: 14 tokens
mean: 61.03 tokens
max: 127 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.88 tokens max: 22 tokens	min: 14 tokens mean: 61.03 tokens max: 127 tokens

Samples:

question	answer
`how do i program my directv remote with my tv?`	`['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.']`
`are rodrigues fruit bats nocturnal?`	`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.`
`why does your heart rate increase during exercise bbc bitesize?`	`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.`

Loss: MatryoshkaLoss with these parameters:

{
    "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

Click to expand

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

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	28.4479	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0256	25	27.0904	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0512	50	19.016	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0768	75	12.2306	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1024	100	9.0613	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1279	125	7.393	3.7497	0.2787	0.4840	0.7029	0.2589	0.5208	0.4094	0.2117	0.4526	0.9042	0.2503	0.5280	0.4922	0.4132	0.4544
0.1535	150	6.6613	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1791	175	6.1911	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2047	200	5.9305	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2303	225	5.6825	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2559	250	5.5326	2.8771	0.2867	0.4619	0.7333	0.2835	0.5549	0.4056	0.2281	0.4883	0.9137	0.2555	0.5114	0.5220	0.4298	0.4673
0.2815	275	5.1671	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3071	300	5.2006	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3327	325	5.0447	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3582	350	4.9647	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3838	375	4.8521	2.5709	0.2881	0.4577	0.7438	0.2909	0.5712	0.4093	0.2273	0.5141	0.9008	0.2668	0.5117	0.5253	0.4331	0.4723
0.4094	400	4.8423	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4350	425	4.7472	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4606	450	4.6527	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4862	475	4.61	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5118	500	4.5451	2.4136	0.2786	0.4464	0.7485	0.2961	0.5638	0.4368	0.2269	0.5125	0.8998	0.2680	0.4938	0.5341	0.4383	0.4726
0.5374	525	4.5357	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5629	550	4.481	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5885	575	4.4669	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6141	600	4.3886	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6397	625	4.2929	2.3091	0.2639	0.4475	0.7521	0.3095	0.5619	0.4448	0.2244	0.5178	0.9102	0.2655	0.4809	0.5253	0.4351	0.4722
0.6653	650	4.2558	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6909	675	4.3228	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7165	700	4.2496	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7421	725	4.2304	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7677	750	4.224	2.2440	0.2628	0.4514	0.7387	0.3028	0.5522	0.4313	0.2253	0.5266	0.9211	0.2675	0.4929	0.5232	0.4351	0.4716
0.7932	775	4.2821	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8188	800	4.2686	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8444	825	4.1657	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8700	850	4.2297	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8956	875	4.1709	2.2142	0.2685	0.4520	0.7569	0.2930	0.5625	0.4486	0.2229	0.5280	0.9153	0.2601	0.4862	0.5199	0.4334	0.4729
0.9212	900	4.0771	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9468	925	4.1492	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9724	950	4.2074	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9980	975	4.0993	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0	977	-	-	0.2613	0.4507	0.7556	0.2964	0.5584	0.4416	0.2241	0.5271	0.9154	0.2646	0.4714	0.5211	0.4291	0.4705

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.643 kWh
Carbon Emitted: 0.250 kg of CO2
Hours Used: 1.727 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

@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

@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

@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}
}