metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:3012496
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: how to sign legal documents as power of attorney?
    sentences:
      - >-
        After the principal's name, write “by” and then sign your own name.
        Under or after the signature line, indicate your status as POA by
        including any of the following identifiers: as POA, as Agent, as
        Attorney in Fact or as Power of Attorney.
      - >-
        ['From the Home screen, swipe left to Apps.', 'Tap Transfer my Data.',
        'Tap Menu (...).', 'Tap Export to SD card.']
      - >-
        Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and
        striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted
        to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg
        of THC. ... This is a perfect product for both cannabis and chocolate
        lovers, who appreciate a little twist.
  - source_sentence: how to delete vdom in fortigate?
    sentences:
      - >-
        Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now
        successfully removed from the configuration.
      - >-
        Both combination birth control pills and progestin-only pills may cause
        headaches as a side effect. Additional side effects of birth control
        pills may include: breast tenderness. nausea.
      - >-
        White cheese tends to show imperfections more readily and as consumers
        got more used to yellow-orange cheese, it became an expected option.
        Today, many cheddars are yellow. While most cheesemakers use annatto,
        some use an artificial coloring agent instead, according to Sachs.
  - source_sentence: where are earthquakes most likely to occur on earth?
    sentences:
      - >-
        Zelle in the Bank of the America app is a fast, safe, and easy way to
        send and receive money with family and friends who have a bank account
        in the U.S., all with no fees. Money moves in minutes directly between
        accounts that are already enrolled with Zelle.
      - >-
        It takes about 3 days for a spacecraft to reach the Moon. During that
        time a spacecraft travels at least 240,000 miles (386,400 kilometers)
        which is the distance between Earth and the Moon.
      - >-
        Most earthquakes occur along the edge of the oceanic and continental
        plates. The earth's crust (the outer layer of the planet) is made up of
        several pieces, called plates. The plates under the oceans are called
        oceanic plates and the rest are continental plates.
  - source_sentence: fix iphone is disabled connect to itunes without itunes?
    sentences:
      - >-
        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.
      - >-
        How Māui brought fire to the world. One evening, after eating a hearty
        meal, Māui lay beside his fire staring into the flames. ... In the
        middle of the night, while everyone was sleeping, Māui went from village
        to village and extinguished all the fires until not a single fire burned
        in the world.
      - >-
        Angry Orchard makes a variety of year-round craft cider styles,
        including Angry Orchard Crisp Apple, a fruit-forward hard cider that
        balances the sweetness of culinary apples with dryness and bright
        acidity of bittersweet apples for a complex, refreshing taste.
  - source_sentence: how to reverse a video on tiktok that's not yours?
    sentences:
      - >-
        ['Tap "Effects" at the bottom of your screen — it\'s an icon that looks
        like a clock. Open the Effects menu. ... ', 'At the end of the new list
        that appears, tap "Time." Select "Time" at the end. ... ', 'Select
        "Reverse" — you\'ll then see a preview of your new, reversed video
        appear on the screen.']
      - >-
        Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a
        total initial investment range of $157,800 to $438,000. The initial cost
        of a franchise includes several fees -- Unlock this franchise to better
        understand the costs such as training and territory fees.
      - >-
        Relative age is the age of a rock layer (or the fossils it contains)
        compared to other layers. It can be determined by looking at the
        position of rock layers. Absolute age is the numeric age of a layer of
        rocks or fossils. Absolute age can be determined by using radiometric
        dating.
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: 6.483463467240631
  energy_consumed: 0.01667977902671103
  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: 0.112
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: cosine_accuracy@1
            value: 0.2
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.42
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.58
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.76
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.2
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16666666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.148
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10399999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.10566666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.22233333333333336
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.30566666666666664
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.40399999999999997
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3021857757296797
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.35745238095238085
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.23166090256020686
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: cosine_accuracy@1
            value: 0.52
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.82
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.52
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.5133333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.48
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.43800000000000006
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.04048260039152364
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.10679067052991392
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.16517406885695451
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.29331552217012935
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5008496215473859
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6488571428571429
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3752676117852694
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: cosine_accuracy@1
            value: 0.42
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.68
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.68
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.82
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.42
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.14
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08599999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.3966666666666667
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.6466666666666667
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6466666666666667
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7766666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5890710274148659
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.546047619047619
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5325906780111076
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        metrics:
          - type: cosine_accuracy@1
            value: 0.36
            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: 0.64
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.36
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.22
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.106
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.1734126984126984
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.32126984126984126
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3737936507936508
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.47868253968253976
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.384612736899094
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.44405555555555554
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.32183898737919203
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: cosine_accuracy@1
            value: 0.58
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.74
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.78
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.86
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.58
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3133333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.21600000000000003
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.126
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.29
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.47
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.54
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.63
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5630232180814766
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.675079365079365
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.48992202928149226
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.2
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.52
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.2
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16666666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10400000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06000000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.2
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.52
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.41343867686046815
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3524603174603175
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3712333972436779
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        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: 0.68
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.68
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.38
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.36
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.3440000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.264
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.019665573227317924
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.07420738619382097
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.09536630802985016
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.11619353053313819
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3204704228749859
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.48633333333333334
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.12237170785886863
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.2
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.36
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.48
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.64
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.2
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.12
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.09600000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.068
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.19
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.34
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.45
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.62
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3932776776815765
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.33038888888888884
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.33440957968177043
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoQuoraRetrieval
          type: NanoQuoraRetrieval
        metrics:
          - type: cosine_accuracy@1
            value: 0.9
            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: 0.9
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.38666666666666655
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.23599999999999993
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.13399999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8106666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.922
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9259999999999999
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.99
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9424143419536263
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9395238095238095
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9189180735930736
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: cosine_accuracy@1
            value: 0.28
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.44
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.56
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.76
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.28
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.21333333333333332
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16799999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.13
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.059666666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.13166666666666668
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.17266666666666666
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.26666666666666666
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.24548416934230666
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3969603174603174
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.1751060490177909
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: cosine_accuracy@1
            value: 0.12
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.48
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.64
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.12
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.13333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.09600000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06400000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.12
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.48
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.64
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3703136948358056
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2856587301587301
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2982488157827007
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: cosine_accuracy@1
            value: 0.46
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.46
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1733333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.128
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.076
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.425
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.47
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.58
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.685
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5455895863246394
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5181349206349206
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5100938735556383
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: cosine_accuracy@1
            value: 0.6326530612244898
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9591836734693877
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6326530612244898
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.6326530612244897
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.5877551020408164
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.5163265306122449
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.04221140303473122
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.12126049151597706
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.18889590300402684
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.3304256352667907
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.563482462405376
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7857142857142857
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.42991471736271825
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.4040502354788069
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5922448979591837
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6692307692307693
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7692307692307693
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4040502354788069
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2794348508634223
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2233657770800628
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1671020408163265
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.22103376474868755
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.3635534658597092
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.41878691774496013
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5254577354604563
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.47186257015009897
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5205128205128206
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.39319818639334664
            name: Cosine Map@100

Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs

This is a sentence-transformers model trained on the gooaq dataset. It maps sentences & paragraphs to a 1024-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
Maximum Sequence Length: inf tokens
Output Dimensionality: 1024 tokens
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): StaticEmbedding(
    (embedding): EmbeddingBag(30522, 1024, mode='mean')
  )
)

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/static-bert-uncased-gooaq-beir-4")
# Run inference
sentences = [
    "how to reverse a video on tiktok that's not yours?",
    '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']',
    'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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	0.2	0.52	0.42	0.36	0.58	0.2	0.38	0.2	0.9	0.28	0.12	0.46	0.6327
cosine_accuracy@3	0.42	0.7	0.68	0.48	0.74	0.5	0.54	0.36	0.98	0.44	0.4	0.5	0.9592
cosine_accuracy@5	0.58	0.82	0.68	0.54	0.78	0.52	0.68	0.48	0.98	0.56	0.48	0.6	1.0
cosine_accuracy@10	0.76	0.9	0.82	0.64	0.86	0.6	0.68	0.64	1.0	0.76	0.64	0.7	1.0
cosine_precision@1	0.2	0.52	0.42	0.36	0.58	0.2	0.38	0.2	0.9	0.28	0.12	0.46	0.6327
cosine_precision@3	0.1667	0.5133	0.2333	0.22	0.3133	0.1667	0.36	0.12	0.3867	0.2133	0.1333	0.1733	0.6327
cosine_precision@5	0.148	0.48	0.14	0.16	0.216	0.104	0.344	0.096	0.236	0.168	0.096	0.128	0.5878
cosine_precision@10	0.104	0.438	0.086	0.106	0.126	0.06	0.264	0.068	0.134	0.13	0.064	0.076	0.5163
cosine_recall@1	0.1057	0.0405	0.3967	0.1734	0.29	0.2	0.0197	0.19	0.8107	0.0597	0.12	0.425	0.0422
cosine_recall@3	0.2223	0.1068	0.6467	0.3213	0.47	0.5	0.0742	0.34	0.922	0.1317	0.4	0.47	0.1213
cosine_recall@5	0.3057	0.1652	0.6467	0.3738	0.54	0.52	0.0954	0.45	0.926	0.1727	0.48	0.58	0.1889
cosine_recall@10	0.404	0.2933	0.7767	0.4787	0.63	0.6	0.1162	0.62	0.99	0.2667	0.64	0.685	0.3304
cosine_ndcg@10	0.3022	0.5008	0.5891	0.3846	0.563	0.4134	0.3205	0.3933	0.9424	0.2455	0.3703	0.5456	0.5635
cosine_mrr@10	0.3575	0.6489	0.546	0.4441	0.6751	0.3525	0.4863	0.3304	0.9395	0.397	0.2857	0.5181	0.7857
cosine_map@100	0.2317	0.3753	0.5326	0.3218	0.4899	0.3712	0.1224	0.3344	0.9189	0.1751	0.2982	0.5101	0.4299

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.4041
cosine_accuracy@3	0.5922
cosine_accuracy@5	0.6692
cosine_accuracy@10	0.7692
cosine_precision@1	0.4041
cosine_precision@3	0.2794
cosine_precision@5	0.2234
cosine_precision@10	0.1671
cosine_recall@1	0.221
cosine_recall@3	0.3636
cosine_recall@5	0.4188
cosine_recall@10	0.5255
cosine_ndcg@10	0.4719
cosine_mrr@10	0.5205
cosine_map@100	0.3932

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: 18 characters
mean: 43.23 characters
max: 96 characters

min: 55 characters
mean: 253.36 characters
max: 371 characters

	question	answer
type	string	string
details	min: 18 characters mean: 43.23 characters max: 96 characters	min: 55 characters mean: 253.36 characters max: 371 characters

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": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        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: 18 characters
mean: 43.17 characters
max: 98 characters

min: 51 characters
mean: 254.12 characters
max: 360 characters

	question	answer
type	string	string
details	min: 18 characters mean: 43.17 characters max: 98 characters	min: 51 characters mean: 254.12 characters max: 360 characters

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": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        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: 2048
per_device_eval_batch_size: 2048
learning_rate: 0.2
num_train_epochs: 1
warmup_ratio: 0.1
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: 2048
per_device_eval_batch_size: 2048
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: 0.2
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: 42
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
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
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	-	-	0.0726	0.3715	0.2100	0.1058	0.3196	0.3109	0.2221	0.1401	0.6737	0.1618	0.1183	0.4337	0.1331	0.2518
0.0007	1	35.3437	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0682	100	16.3878	2.4139	0.2927	0.4729	0.5725	0.3235	0.5905	0.3674	0.2994	0.3324	0.9123	0.2326	0.3407	0.5618	0.5352	0.4488
0.1363	200	5.94	1.8298	0.2897	0.4880	0.5624	0.3447	0.5683	0.4311	0.3066	0.3502	0.9129	0.2533	0.3335	0.5696	0.5365	0.4575
0.2045	300	4.8307	1.5955	0.2780	0.4896	0.5746	0.3513	0.5815	0.4040	0.3125	0.3897	0.9190	0.2578	0.3556	0.5461	0.5401	0.4615
0.2727	400	4.33	1.4696	0.3113	0.4909	0.5920	0.3795	0.5836	0.3919	0.3201	0.4023	0.9355	0.2535	0.3419	0.5236	0.5524	0.4676
0.3408	500	4.0423	1.3887	0.3085	0.4966	0.5986	0.3794	0.5914	0.3914	0.3174	0.3590	0.9309	0.2441	0.3537	0.5311	0.5534	0.4658
0.4090	600	3.8422	1.3120	0.3034	0.5052	0.6075	0.3680	0.5834	0.4136	0.3122	0.3725	0.9257	0.2477	0.3583	0.5309	0.5646	0.4687
0.4772	700	3.6795	1.2693	0.2975	0.4988	0.5954	0.3785	0.5811	0.4160	0.3142	0.3908	0.9362	0.2471	0.3479	0.5520	0.5601	0.4704
0.5453	800	3.5367	1.2285	0.3011	0.4947	0.5829	0.3463	0.5689	0.4369	0.3224	0.3791	0.9310	0.2430	0.3663	0.5577	0.5585	0.4684
0.6135	900	3.4279	1.1963	0.3059	0.5027	0.5894	0.3674	0.5758	0.4126	0.3186	0.4066	0.9349	0.2456	0.3672	0.5560	0.5624	0.4727
0.6817	1000	3.3637	1.1652	0.3056	0.5022	0.5849	0.3702	0.5714	0.4238	0.3161	0.4007	0.9373	0.2430	0.3699	0.5618	0.5657	0.4733
0.7498	1100	3.2336	1.1312	0.3006	0.5038	0.5920	0.3884	0.5733	0.4241	0.3247	0.3974	0.9369	0.2431	0.3670	0.5644	0.5608	0.4751
0.8180	1200	3.1952	1.1132	0.3044	0.4987	0.5770	0.3630	0.5735	0.4259	0.3279	0.3955	0.9428	0.2416	0.3798	0.5659	0.5641	0.4739
0.8862	1300	3.1535	1.0926	0.2983	0.4968	0.5753	0.3812	0.5684	0.4108	0.3203	0.3965	0.9421	0.2428	0.3685	0.5608	0.5628	0.4711
0.9543	1400	3.0691	1.0862	0.3109	0.5008	0.5870	0.3761	0.5612	0.4121	0.3204	0.3947	0.9426	0.2414	0.3708	0.5456	0.5588	0.4709
1.0	1467	-	-	0.3022	0.5008	0.5891	0.3846	0.5630	0.4134	0.3205	0.3933	0.9424	0.2455	0.3703	0.5456	0.5635	0.4719

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.017 kWh
Carbon Emitted: 0.006 kg of CO2
Hours Used: 0.112 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: 3.3.0.dev0
Transformers: 4.45.2
PyTorch: 2.5.0.dev20240807+cu121
Accelerate: 1.0.0
Datasets: 2.20.0
Tokenizers: 0.20.1-dev.0

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}
}

MultipleNegativesRankingLoss

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