sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:3012496 loss:MatryoshkaLoss loss:CachedMultipleNegativesRankingLoss en dataset:sentence-transformers/gooaq arxiv:1908.10084 arxiv:2205.13147 arxiv:2101.06983 base_model:microsoft/mpnet-base base_model:finetune:microsoft/mpnet-base license:apache-2.0 model-index co2_eq_emissions autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/mpnet-base
datasets:
- sentence-transformers/gooaq
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3012496
- loss:MatryoshkaLoss
- loss:CachedMultipleNegativesRankingLoss
widget:
- source_sentence: 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.
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: 1.0
      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](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: 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:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/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]
```

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</details>
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You can finetune this model on your own dataset.

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

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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval

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

| Metric              | NanoClimateFEVER | NanoDBPedia | NanoFEVER  | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ     | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| cosine_accuracy@1   | 0.26             | 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 [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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     |

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### Recommendations

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## Training Details

### Training Dataset

#### gooaq

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

### Evaluation Dataset

#### gooaq

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

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 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
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 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

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0      | 0    | -             | -               | 0.0419                          | 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](https://github.com/mlco2/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
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

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

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

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