sentence-transformers safetensors sentence-similarity feature-extraction generated_from_trainer dataset_size:3012496 loss:MultipleNegativesRankingLoss en dataset:sentence-transformers/gooaq arxiv:1908.10084 arxiv:1705.00652 base_model:sentence-transformers-testing/stsb-bert-tiny-safetensors base_model:finetune:sentence-transformers-testing/stsb-bert-tiny-safetensors license:apache-2.0 model-index co2_eq_emissions autotrain_compatible endpoints_compatible region:us
---
base_model: sentence-transformers-testing/stsb-bert-tiny-safetensors
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:MultipleNegativesRankingLoss
widget:
- source_sentence: how to sign legal documents as power of attorney?
  sentences:
  - 'After the principal''s name, write “by” and then sign your own name. Under or
    after the signature line, indicate your status as POA by including any of the
    following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.'
  - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap
    Menu (...).'', ''Tap Export to SD card.'']'
  - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking
    gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect
    nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect
    product for both cannabis and chocolate lovers, who appreciate a little twist.
- source_sentence: how to delete vdom in fortigate?
  sentences:
  - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully
    removed from the configuration.
  - 'Both combination birth control pills and progestin-only pills may cause headaches
    as a side effect. Additional side effects of birth control pills may include:
    breast tenderness. nausea.'
  - White cheese tends to show imperfections more readily and as consumers got more
    used to yellow-orange cheese, it became an expected option. Today, many cheddars
    are yellow. While most cheesemakers use annatto, some use an artificial coloring
    agent instead, according to Sachs.
- source_sentence: where are earthquakes most likely to occur on earth?
  sentences:
  - Zelle in the Bank of the America app is a fast, safe, and easy way to send and
    receive money with family and friends who have a bank account in the U.S., all
    with no fees. Money moves in minutes directly between accounts that are already
    enrolled with Zelle.
  - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft
    travels at least 240,000 miles (386,400 kilometers) which is the distance between
    Earth and the Moon.
  - Most earthquakes occur along the edge of the oceanic and continental plates. The
    earth's crust (the outer layer of the planet) is made up of several pieces, called
    plates. The plates under the oceans are called oceanic plates and the rest are
    continental plates.
- source_sentence: fix iphone is disabled connect to itunes without itunes?
  sentences:
  - To fix a disabled iPhone or iPad without iTunes, you have to erase your device.
    Click on the "Erase iPhone" option and confirm your selection. Wait for a while
    as the "Find My iPhone" feature will remotely erase your iOS device. Needless
    to say, it will also disable its lock.
  - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui
    lay beside his fire staring into the flames. ... In the middle of the night, while
    everyone was sleeping, Māui went from village to village and extinguished all
    the fires until not a single fire burned in the world.
  - Angry Orchard makes a variety of year-round craft cider styles, including Angry
    Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of
    culinary apples with dryness and bright acidity of bittersweet apples for a complex,
    refreshing taste.
- source_sentence: how to reverse a video on tiktok that's not yours?
  sentences:
  - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like
    a clock. Open the Effects menu. ... '', ''At the end of the new list that appears,
    tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then
    see a preview of your new, reversed video appear on the screen.'']'
  - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial
    investment range of $157,800 to $438,000. The initial cost of a franchise includes
    several fees -- Unlock this franchise to better understand the costs such as training
    and territory fees.
  - Relative age is the age of a rock layer (or the fossils it contains) compared
    to other layers. It can be determined by looking at the position of rock layers.
    Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can
    be determined by using radiometric dating.
co2_eq_emissions:
  emissions: 9.679189270737199
  energy_consumed: 0.024901310697493708
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.15
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: stsb-bert-tiny adapter finetuned on GooAQ pairs
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoClimateFEVER
      type: NanoClimateFEVER
    metrics:
    - type: cosine_accuracy@1
      value: 0.14
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.26
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.38
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07999999999999999
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.05600000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.056666666666666664
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.08666666666666668
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.11166666666666666
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.17833333333333332
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1412311142763055
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.19938095238095235
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.11363345611144926
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoDBPedia
      type: NanoDBPedia
    metrics:
    - type: cosine_accuracy@1
      value: 0.42
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.62
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.72
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.86
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.42
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.34
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.344
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.29
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.02634308391586433
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.06038926804951766
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.10265977040056268
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.19610280190566398
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.34151812101104584
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5504126984126985
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21133731615809154
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoFEVER
      type: NanoFEVER
    metrics:
    - type: cosine_accuracy@1
      value: 0.12
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.18
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.22
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.36
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.12
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05999999999999999
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.044000000000000004
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.036000000000000004
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.18
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.22
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.34
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.21218661613500586
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.17491269841269838
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.18857101300669993
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoFiQA2018
      type: NanoFiQA2018
    metrics:
    - type: cosine_accuracy@1
      value: 0.06
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.1
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.28
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.06
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.04
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04800000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.032
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.044000000000000004
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.06199999999999999
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.12488888888888887
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.15574603174603174
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.10395695406287388
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.10821428571428571
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.08041090092126037
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoHotpotQA
      type: NanoHotpotQA
    metrics:
    - type: cosine_accuracy@1
      value: 0.36
      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: 0.62
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.36
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.20666666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07800000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.31
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.35
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.39
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3504958855767756
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4476349206349205
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.29308037158200173
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoMSMARCO
      type: NanoMSMARCO
    metrics:
    - type: cosine_accuracy@1
      value: 0.06
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.26
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.36
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.06
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.064
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.036000000000000004
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.06
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.26
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.32
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.36
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.21417075898440763
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.16666666666666663
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.19159156983842277
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNFCorpus
      type: NanoNFCorpus
    metrics:
    - type: cosine_accuracy@1
      value: 0.2
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.26
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.44
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.12
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.09600000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.00377949106046741
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.007274949456892388
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.012714784638321257
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.019303285579015287
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.09870502263453415
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2538809523809524
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.018928657854150332
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNQ
      type: NanoNQ
    metrics:
    - type: cosine_accuracy@1
      value: 0.08
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.18
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.42
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.042
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.17
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.19
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2051878697694875
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1506904761904762
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.16101738947158584
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoQuoraRetrieval
      type: NanoQuoraRetrieval
    metrics:
    - type: cosine_accuracy@1
      value: 0.7
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.82
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.88
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.94
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.32
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.22399999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.11799999999999997
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.624
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7719999999999999
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.866
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8993333333333333
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7992844609162323
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7798333333333335
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7635205205527187
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoSCIDOCS
      type: NanoSCIDOCS
    metrics:
    - type: cosine_accuracy@1
      value: 0.18
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.26
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.12
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.09200000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.066
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.036000000000000004
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.07466666666666667
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.09466666666666666
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.13466666666666666
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1348403477257659
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.24209523809523809
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.10255365352032365
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoArguAna
      type: NanoArguAna
    metrics:
    - type: cosine_accuracy@1
      value: 0.08
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.26
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06400000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.26
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.32
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2375425714519515
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1856666666666667
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1985205474177431
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoSciFact
      type: NanoSciFact
    metrics:
    - type: cosine_accuracy@1
      value: 0.08
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.32
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.064
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.034
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.195
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.28
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.19370675821369307
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.16466666666666668
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1653693334513147
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoTouche2020
      type: NanoTouche2020
    metrics:
    - type: cosine_accuracy@1
      value: 0.20408163265306123
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5102040816326531
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7551020408163265
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8775510204081632
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.20408163265306123
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25170068027210885
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.25306122448979596
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.24489795918367346
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.014397370082893721
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.04876234248655414
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.0792610922160282
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.14648888406884147
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2485959675297849
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4082118561710398
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.16376385142142616
      name: Cosine Map@100
  - task:
      type: nano-beir
      name: Nano BEIR
    dataset:
      name: NanoBEIR mean
      type: NanoBEIR_mean
    metrics:
    - type: cosine_accuracy@1
      value: 0.20646781789638935
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.33924646781789636
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.41039246467817886
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5121193092621665
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.20646781789638935
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1419256933542648
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11762009419152278
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08822291993720567
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10809127782506864
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.19128922256356135
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2362967591905488
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.30153648743329886
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.25241711140675877
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2947898009020458
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2040229677928606
      name: Cosine Map@100
---

# stsb-bert-tiny adapter finetuned on GooAQ pairs

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers-testing/stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

This model was trained using [train_script.py](train_script.py).

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers-testing/stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors) <!-- at revision f3cb857cba53019a20df283396bcca179cf051a4 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 128 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **Language:** en
- **License:** apache-2.0

### Model Sources

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

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 128, '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("sentence-transformers-testing/stsb-bert-tiny-lora")
# 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, 128]

# 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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<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   | 0.14             | 0.42        | 0.12       | 0.06         | 0.36         | 0.06        | 0.2          | 0.08       | 0.7                | 0.18        | 0.08        | 0.08        | 0.2041         |
| cosine_accuracy@3   | 0.22             | 0.62        | 0.18       | 0.1          | 0.52         | 0.26        | 0.26         | 0.18       | 0.82               | 0.26        | 0.26        | 0.22        | 0.5102         |
| cosine_accuracy@5   | 0.26             | 0.72        | 0.22       | 0.2          | 0.54         | 0.32        | 0.3          | 0.2        | 0.88               | 0.32        | 0.32        | 0.3         | 0.7551         |
| cosine_accuracy@10  | 0.38             | 0.86        | 0.36       | 0.28         | 0.62         | 0.36        | 0.44         | 0.42       | 0.94               | 0.4         | 0.4         | 0.32        | 0.8776         |
| cosine_precision@1  | 0.14             | 0.42        | 0.12       | 0.06         | 0.36         | 0.06        | 0.2          | 0.08       | 0.7                | 0.18        | 0.08        | 0.08        | 0.2041         |
| cosine_precision@3  | 0.08             | 0.34        | 0.06       | 0.04         | 0.2067       | 0.0867      | 0.12         | 0.06       | 0.32               | 0.12        | 0.0867      | 0.0733      | 0.2517         |
| cosine_precision@5  | 0.056            | 0.344       | 0.044      | 0.048        | 0.14         | 0.064       | 0.096        | 0.04       | 0.224              | 0.092       | 0.064       | 0.064       | 0.2531         |
| cosine_precision@10 | 0.05             | 0.29        | 0.036      | 0.032        | 0.078        | 0.036       | 0.08         | 0.042      | 0.118              | 0.066       | 0.04        | 0.034       | 0.2449         |
| cosine_recall@1     | 0.0567           | 0.0263      | 0.12       | 0.044        | 0.18         | 0.06        | 0.0038       | 0.08       | 0.624              | 0.036       | 0.08        | 0.08        | 0.0144         |
| cosine_recall@3     | 0.0867           | 0.0604      | 0.18       | 0.062        | 0.31         | 0.26        | 0.0073       | 0.17       | 0.772              | 0.0747      | 0.26        | 0.195       | 0.0488         |
| cosine_recall@5     | 0.1117           | 0.1027      | 0.22       | 0.1249       | 0.35         | 0.32        | 0.0127       | 0.19       | 0.866              | 0.0947      | 0.32        | 0.28        | 0.0793         |
| cosine_recall@10    | 0.1783           | 0.1961      | 0.34       | 0.1557       | 0.39         | 0.36        | 0.0193       | 0.4        | 0.8993             | 0.1347      | 0.4         | 0.3         | 0.1465         |
| **cosine_ndcg@10**  | **0.1412**       | **0.3415**  | **0.2122** | **0.104**    | **0.3505**   | **0.2142**  | **0.0987**   | **0.2052** | **0.7993**         | **0.1348**  | **0.2375**  | **0.1937**  | **0.2486**     |
| cosine_mrr@10       | 0.1994           | 0.5504      | 0.1749     | 0.1082       | 0.4476       | 0.1667      | 0.2539       | 0.1507     | 0.7798             | 0.2421      | 0.1857      | 0.1647      | 0.4082         |
| cosine_map@100      | 0.1136           | 0.2113      | 0.1886     | 0.0804       | 0.2931       | 0.1916      | 0.0189       | 0.161      | 0.7635             | 0.1026      | 0.1985      | 0.1654      | 0.1638         |

#### 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   | 0.2065     |
| cosine_accuracy@3   | 0.3392     |
| cosine_accuracy@5   | 0.4104     |
| cosine_accuracy@10  | 0.5121     |
| cosine_precision@1  | 0.2065     |
| cosine_precision@3  | 0.1419     |
| cosine_precision@5  | 0.1176     |
| cosine_precision@10 | 0.0882     |
| cosine_recall@1     | 0.1081     |
| cosine_recall@3     | 0.1913     |
| cosine_recall@5     | 0.2363     |
| cosine_recall@10    | 0.3015     |
| **cosine_ndcg@10**  | **0.2524** |
| cosine_mrr@10       | 0.2948     |
| cosine_map@100      | 0.204      |

<!--
## Bias, Risks and Limitations

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### Recommendations

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

### Training Dataset

#### gooaq

* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | question                                                                          | answer                                                                              |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.48 tokens</li><li>max: 138 tokens</li></ul> |
* Samples:
  | question                                                                           | answer                                                                                                                                                                                                                                                                                                                |
  |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what is the difference between broilers and layers?</code>                   | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code>                |
  | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> |
  | <code>is kamagra same as viagra?</code>                                            | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code>                               |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 1024
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 1024
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 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    | -             | -               | 0.1174                          | 0.3053                     | 0.1405                   | 0.0440                      | 0.2821                      | 0.2297                     | 0.0773                      | 0.1708                | 0.7830                            | 0.1181                     | 0.2017                     | 0.1447                     | 0.1642                        | 0.2138                       |
| 0.0010 | 1    | 3.6449        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0256 | 25   | 3.6146        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0512 | 50   | 3.6074        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0768 | 75   | 3.5997        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1024 | 100  | 3.5737        | 2.0205          | 0.1178                          | 0.3061                     | 0.1477                   | 0.0461                      | 0.2837                      | 0.2291                     | 0.0804                      | 0.1713                | 0.7791                            | 0.1205                     | 0.2049                     | 0.1534                     | 0.1731                        | 0.2164                       |
| 0.1279 | 125  | 3.5644        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1535 | 150  | 3.4792        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1791 | 175  | 3.4743        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2047 | 200  | 3.4169        | 1.9114          | 0.1336                          | 0.3084                     | 0.1446                   | 0.0604                      | 0.2965                      | 0.2350                     | 0.0847                      | 0.1650                | 0.7806                            | 0.1270                     | 0.2141                     | 0.1633                     | 0.1835                        | 0.2228                       |
| 0.2303 | 225  | 3.3535        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2559 | 250  | 3.3336        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2815 | 275  | 3.3038        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3071 | 300  | 3.2576        | 1.8114          | 0.1359                          | 0.3260                     | 0.1733                   | 0.0752                      | 0.3167                      | 0.2323                     | 0.0851                      | 0.1753                | 0.7843                            | 0.1266                     | 0.2218                     | 0.1752                     | 0.2012                        | 0.2330                       |
| 0.3327 | 325  | 3.2304        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3582 | 350  | 3.2133        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3838 | 375  | 3.1369        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4094 | 400  | 3.1412        | 1.7379          | 0.1389                          | 0.3298                     | 0.1930                   | 0.0934                      | 0.3261                      | 0.2310                     | 0.0852                      | 0.1760                | 0.7850                            | 0.1349                     | 0.2235                     | 0.1863                     | 0.2118                        | 0.2396                       |
| 0.4350 | 425  | 3.0782        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4606 | 450  | 3.0948        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4862 | 475  | 3.0696        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5118 | 500  | 3.0641        | 1.6850          | 0.1373                          | 0.3307                     | 0.1945                   | 0.0937                      | 0.3301                      | 0.2365                     | 0.0931                      | 0.1950                | 0.7933                            | 0.1359                     | 0.2231                     | 0.1885                     | 0.2289                        | 0.2447                       |
| 0.5374 | 525  | 3.0224        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5629 | 550  | 2.9927        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5885 | 575  | 2.9796        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6141 | 600  | 2.9624        | 1.6475          | 0.1397                          | 0.3321                     | 0.2058                   | 0.0999                      | 0.3422                      | 0.2276                     | 0.1014                      | 0.1901                | 0.7971                            | 0.1393                     | 0.2258                     | 0.1918                     | 0.2342                        | 0.2482                       |
| 0.6397 | 625  | 2.9508        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6653 | 650  | 2.958         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6909 | 675  | 2.9428        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7165 | 700  | 2.9589        | 1.6209          | 0.1425                          | 0.3344                     | 0.2061                   | 0.1050                      | 0.3427                      | 0.2295                     | 0.1001                      | 0.1868                | 0.7955                            | 0.1342                     | 0.2298                     | 0.1922                     | 0.2343                        | 0.2487                       |
| 0.7421 | 725  | 2.9152        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7677 | 750  | 2.9056        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7932 | 775  | 2.9111        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8188 | 800  | 2.9107        | 1.6037          | 0.1415                          | 0.3401                     | 0.2064                   | 0.1053                      | 0.3523                      | 0.2153                     | 0.1001                      | 0.1934                | 0.7976                            | 0.1340                     | 0.2302                     | 0.1946                     | 0.2461                        | 0.2505                       |
| 0.8444 | 825  | 2.8675        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8700 | 850  | 2.9175        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8956 | 875  | 2.8592        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9212 | 900  | 2.86          | 1.5941          | 0.1411                          | 0.3415                     | 0.2180                   | 0.1048                      | 0.3506                      | 0.2210                     | 0.0987                      | 0.2052                | 0.7988                            | 0.1349                     | 0.2302                     | 0.1946                     | 0.2464                        | 0.2528                       |
| 0.9468 | 925  | 2.8603        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9724 | 950  | 2.8909        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9980 | 975  | 2.8819        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 1.0    | 977  | -             | -               | 0.1412                          | 0.3415                     | 0.2122                   | 0.1040                      | 0.3505                      | 0.2142                     | 0.0987                      | 0.2052                | 0.7993                            | 0.1348                     | 0.2375                     | 0.1937                     | 0.2486                        | 0.2524                       |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.025 kWh
- **Carbon Emitted**: 0.010 kg of CO2
- **Hours Used**: 0.15 hours

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

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.3.0.dev0
- Transformers: 4.46.2
- PyTorch: 2.5.0+cu121
- Accelerate: 1.0.0
- 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",
}
```

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

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