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: microsoft/mpnet-base
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: 901.0176370050929
  energy_consumed: 2.3180164676412596
  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: 5.999
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: MPNet base trained on GooAQ triplets
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        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: 0.5
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.62
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.26
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1733333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11600000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08199999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.12833333333333333
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.23566666666666664
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.2523333333333333
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.3423333333333333
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2832168283343785
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3685714285714285
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.22816684702715823
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: cosine_accuracy@1
            value: 0.56
            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: 0.88
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.56
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.5
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.436
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.37800000000000006
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.05411706752798353
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.12035295895525228
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.15928246254162917
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.23697530489351543
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4605652479922868
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6701666666666667
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.313461519912651
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: cosine_accuracy@1
            value: 0.62
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.82
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.84
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.62
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27999999999999997
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.172
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.092
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5766666666666667
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7866666666666667
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8066666666666668
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8666666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7421816204572005
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7256349206349206
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6984857882513162
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        metrics:
          - type: cosine_accuracy@1
            value: 0.4
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.52
            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: 0.4
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.188
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.11199999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.24385714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.37612698412698414
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.429515873015873
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5025952380952381
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.43956943866243664
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.48483333333333334
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.39610909278538586
            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: 0.72
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.78
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.84
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31999999999999995
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.204
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.11799999999999997
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.3
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.48
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.51
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.59
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5463522282651155
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6749126984126984
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4777656892588857
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.26
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.54
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.82
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.26
            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: 0.08199999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.26
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.54
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.7
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.82
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5254388867327386
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.43241269841269836
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.44192370495002076
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: cosine_accuracy@1
            value: 0.42
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.52
            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: 0.42
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3533333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.29600000000000004
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.22999999999999995
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.024846889440892198
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.050109275117862714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.06353201637623539
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.08853093525637233
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2784279013606366
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.48200000000000004
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.1099281411687893
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.46
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.64
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.68
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.46
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.22666666666666666
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.14400000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08399999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.44
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.63
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.67
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.76
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6103091812374759
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5662380952380953
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5687228298733515
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoQuoraRetrieval
          type: NanoQuoraRetrieval
        metrics:
          - type: cosine_accuracy@1
            value: 0.92
            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: 0.92
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.40666666666666657
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.25599999999999995
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.13399999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7973333333333332
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9453333333333334
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9593333333333334
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9893333333333334
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9468303023215506
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.948888888888889
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9245031746031745
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: cosine_accuracy@1
            value: 0.34
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.54
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.64
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.76
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.34
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.21600000000000003
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.148
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.07
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.16
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.22266666666666668
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.3046666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.29180682575954126
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4679126984126984
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.20981154821773768
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: cosine_accuracy@1
            value: 0.24
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5
            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: 0.24
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16666666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.136
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08199999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.24
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.68
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.82
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5108280876289467
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.413579365079365
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.42352200577200577
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: cosine_accuracy@1
            value: 0.52
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.64
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.72
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.74
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.52
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.22666666666666668
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08399999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.485
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.61
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.705
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.73
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6181538011380482
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5913333333333333
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5833669046006453
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: cosine_accuracy@1
            value: 0.5102040816326531
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8163265306122449
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8571428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9795918367346939
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5102040816326531
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.510204081632653
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.47346938775510194
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.41020408163265304
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.03893285013079613
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.11588553532033441
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.17562928121209787
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.2858043118244373
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4588632608031716
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6822238419177193
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.36126308261178003
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.47001569858712716
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.6520251177394034
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7182417582417582
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8061224489795917
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.47001569858712716
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2971951857666143
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2259591836734694
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.15663108320251176
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.2814682525607806
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4269339553990077
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.48722766408814117
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5643773684668895
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5163495085148867
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5775929206847574
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4413100253102233
            name: Cosine Map@100

MPNet base trained on GooAQ triplets

This is a sentence-transformers model finetuned from microsoft/mpnet-base 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: microsoft/mpnet-base
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: MPNetModel 
  (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/mpnet-base-gooaq-cmnrl-mrl")
# 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, 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	0.26	0.56	0.62	0.4	0.6	0.26	0.42	0.46	0.92	0.34	0.24	0.52	0.5102
cosine_accuracy@3	0.46	0.78	0.82	0.52	0.72	0.54	0.52	0.64	0.98	0.54	0.5	0.64	0.8163
cosine_accuracy@5	0.5	0.82	0.84	0.6	0.78	0.7	0.54	0.68	0.98	0.64	0.68	0.72	0.8571
cosine_accuracy@10	0.62	0.88	0.9	0.68	0.84	0.82	0.64	0.8	1.0	0.76	0.82	0.74	0.9796
cosine_precision@1	0.26	0.56	0.62	0.4	0.6	0.26	0.42	0.46	0.92	0.34	0.24	0.52	0.5102
cosine_precision@3	0.1733	0.5	0.28	0.26	0.32	0.18	0.3533	0.2267	0.4067	0.26	0.1667	0.2267	0.5102
cosine_precision@5	0.116	0.436	0.172	0.188	0.204	0.14	0.296	0.144	0.256	0.216	0.136	0.16	0.4735
cosine_precision@10	0.082	0.378	0.092	0.112	0.118	0.082	0.23	0.084	0.134	0.148	0.082	0.084	0.4102
cosine_recall@1	0.1283	0.0541	0.5767	0.2439	0.3	0.26	0.0248	0.44	0.7973	0.07	0.24	0.485	0.0389
cosine_recall@3	0.2357	0.1204	0.7867	0.3761	0.48	0.54	0.0501	0.63	0.9453	0.16	0.5	0.61	0.1159
cosine_recall@5	0.2523	0.1593	0.8067	0.4295	0.51	0.7	0.0635	0.67	0.9593	0.2227	0.68	0.705	0.1756
cosine_recall@10	0.3423	0.237	0.8667	0.5026	0.59	0.82	0.0885	0.76	0.9893	0.3047	0.82	0.73	0.2858
cosine_ndcg@10	0.2832	0.4606	0.7422	0.4396	0.5464	0.5254	0.2784	0.6103	0.9468	0.2918	0.5108	0.6182	0.4589
cosine_mrr@10	0.3686	0.6702	0.7256	0.4848	0.6749	0.4324	0.482	0.5662	0.9489	0.4679	0.4136	0.5913	0.6822
cosine_map@100	0.2282	0.3135	0.6985	0.3961	0.4778	0.4419	0.1099	0.5687	0.9245	0.2098	0.4235	0.5834	0.3613

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.47
cosine_accuracy@3	0.652
cosine_accuracy@5	0.7182
cosine_accuracy@10	0.8061
cosine_precision@1	0.47
cosine_precision@3	0.2972
cosine_precision@5	0.226
cosine_precision@10	0.1566
cosine_recall@1	0.2815
cosine_recall@3	0.4269
cosine_recall@5	0.4872
cosine_recall@10	0.5644
cosine_ndcg@10	0.5163
cosine_mrr@10	0.5776
cosine_map@100	0.4413

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: 2048
per_device_eval_batch_size: 2048
learning_rate: 8e-05
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: 8e-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: 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
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	-	-	0.0419	0.1123	0.0389	0.0309	0.0746	0.1310	0.0311	0.0397	0.6607	0.0638	0.2616	0.1097	0.1098	0.1312
0.0007	1	41.9671	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0682	100	12.4237	1.0176	0.3022	0.4597	0.7934	0.4621	0.5280	0.4849	0.2517	0.5561	0.8988	0.3144	0.5708	0.5755	0.4514	0.5115
0.1363	200	3.0536	0.6917	0.2883	0.4588	0.7773	0.4272	0.5264	0.5494	0.2538	0.5837	0.9303	0.2945	0.5493	0.5795	0.4547	0.5133
0.2045	300	2.2724	0.5954	0.2944	0.4606	0.7825	0.4522	0.5247	0.5069	0.2554	0.5636	0.9177	0.2861	0.5560	0.5562	0.4667	0.5095
0.2727	400	1.933	0.5171	0.3027	0.4841	0.7050	0.4406	0.4877	0.5406	0.2768	0.6014	0.9463	0.2989	0.5725	0.6151	0.4680	0.5184
0.3408	500	1.7806	0.4745	0.3034	0.4857	0.7537	0.4435	0.5661	0.5529	0.2733	0.5878	0.9470	0.3016	0.5377	0.6073	0.4682	0.5252
0.4090	600	1.6253	0.4392	0.3018	0.4790	0.7502	0.4617	0.5478	0.5411	0.2812	0.6220	0.9443	0.2916	0.5210	0.5900	0.4644	0.5228
0.4772	700	1.5136	0.4312	0.3175	0.4846	0.7481	0.4168	0.5761	0.5222	0.2825	0.6142	0.9415	0.2888	0.5373	0.5754	0.4675	0.5210
0.5453	800	1.4454	0.4022	0.3017	0.4756	0.7307	0.4494	0.5484	0.5184	0.2821	0.6182	0.9440	0.2834	0.5191	0.6071	0.4694	0.5191
0.6135	900	1.3711	0.3886	0.2945	0.4602	0.7463	0.4529	0.5433	0.5457	0.2730	0.5972	0.9449	0.2776	0.5183	0.6018	0.4716	0.5175
0.6817	1000	1.3295	0.3688	0.2811	0.4720	0.7275	0.4342	0.5581	0.5418	0.2809	0.6087	0.9421	0.2823	0.5138	0.5729	0.4662	0.5140
0.7498	1100	1.267	0.3637	0.2815	0.4666	0.7168	0.4346	0.5348	0.5317	0.2789	0.6056	0.9450	0.2775	0.5117	0.6116	0.4583	0.5119
0.8180	1200	1.2542	0.3514	0.2882	0.4659	0.7275	0.4308	0.5585	0.5373	0.2788	0.5950	0.9433	0.2767	0.5241	0.6141	0.4655	0.5158
0.8862	1300	1.2146	0.3427	0.2932	0.4638	0.7118	0.4453	0.5636	0.5363	0.2788	0.6098	0.9481	0.2825	0.5160	0.6238	0.4619	0.5181
0.9543	1400	1.1892	0.3378	0.2809	0.4610	0.7319	0.4353	0.5397	0.5295	0.2828	0.6029	0.9474	0.2931	0.5078	0.6182	0.4602	0.5147
1.0	1467	-	-	0.2832	0.4606	0.7422	0.4396	0.5464	0.5254	0.2784	0.6103	0.9468	0.2918	0.5108	0.6182	0.4589	0.5163

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 2.318 kWh
Carbon Emitted: 0.901 kg of CO2
Hours Used: 5.999 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: 1.1.1
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}
}