sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:50000 loss:CachedGISTEmbedLoss en dataset:tomaarsen/gooaq-hard-negatives arxiv:1908.10084 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:
- tomaarsen/gooaq-hard-negatives
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:50000
- loss:CachedGISTEmbedLoss
widget:
- source_sentence: what does the accounts receivable turnover measure?
  sentences:
  - The accounts receivable turnover ratio is an accounting measure used to quantify
    a company's effectiveness in collecting its receivables or money owed by clients.
    The ratio shows how well a company uses and manages the credit it extends to customers
    and how quickly that short-term debt is collected or is paid.
  - Capital budgeting, and investment appraisal, is the planning process used to determine
    whether an organization's long term investments such as new machinery, replacement
    of machinery, new plants, new products, and research development projects are
    worth the funding of cash through the firm's capitalization structure ( ...
  - The accounts receivable turnover ratio is an accounting measure used to quantify
    a company's effectiveness in collecting its receivables or money owed by clients.
    The ratio shows how well a company uses and manages the credit it extends to customers
    and how quickly that short-term debt is collected or is paid.
- source_sentence: does gabapentin cause liver problems?
  sentences:
  - Gabapentin has no appreciable liver metabolism, yet, suspected cases of gabapentin-induced
    hepatotoxicity have been reported. Per literature review, two cases of possible
    gabapentin-induced liver injury have been reported.
  - Strongholds are a type of story mission which only unlocks after enough progression
    through the game. There are three Stronghold's during the first section of progression
    through The Division 2. You'll need to complete the first two and have reached
    level 30 before being able to unlock the final Stronghold.
  - The most-common side effects attributed to Gabapentin include mild sedation, ataxia,
    and occasional diarrhea. Sedation can be minimized by tapering from a smaller
    starting dose to the desired dose. When treating seizures, it is ideal to wean
    off the drug to reduce the risk of withdrawal seizures.
- source_sentence: how long should you wait to give blood after eating?
  sentences:
  - Until the bleeding has stopped it is natural to taste blood or to see traces of
    blood in your saliva. You may stop using gauze after the flow stops – usually
    around 8 hours after surgery.
  - Before donation The first and most important rule—never donate blood on an empty
    stomach. “Eat a wholesome meal about 2-3 hours before donating to keep your blood
    sugar stable," says Dr Chaturvedi. The timing of the meal is important too. You
    need to allow the food to be digested properly before the blood is drawn.
  - While grid computing involves virtualizing computing resources to store massive
    amounts of data, whereas cloud computing is where an application doesn't access
    resources directly, rather it accesses them through a service over the internet.
    ...
- source_sentence: what is the difference between chicken francese and chicken marsala?
  sentences:
  - Chicken is the species name, equivalent to our “human.” Rooster is an adult male,
    equivalent to “man.” Hen is an adult female, equivalent to “woman.” Cockerel is
    a juvenile male, equivalent to “boy/young man.”
  - What is 99 kg in pounds? - 99 kg is equal to 218.26 pounds.
  - The difference between the two is for Francese, the chicken breast is first dipped
    in flour, then into a beaten egg mixture, before being cooked. For piccata, the
    chicken is first dipped in egg and then in flour. Both are then simmered in a
    lemony butter sauce, but the piccata sauce includes capers.”
- source_sentence: what energy is released when coal is burned?
  sentences:
  - When coal is burned, it reacts with the oxygen in the air. This chemical reaction
    converts the stored solar energy into thermal energy, which is released as heat.
    But it also produces carbon dioxide and methane.
  - When coal is burned it releases a number of airborne toxins and pollutants. They
    include mercury, lead, sulfur dioxide, nitrogen oxides, particulates, and various
    other heavy metals.
  - Squad Building Challenges allow you to exchange sets of players for coins, packs,
    and special items in FUT 20. Each of these challenges come with specific requirements,
    such as including players from certain teams. ... Live SBCs are time-limited challenges
    which often give out unique, high-rated versions of players.
co2_eq_emissions:
  emissions: 40.86214567359107
  energy_consumed: 0.1051246087583575
  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: 0.3
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: MPNet base trained on Natural Questions pairs
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoClimateFEVER
      type: NanoClimateFEVER
    metrics:
    - type: cosine_accuracy@1
      value: 0.22
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.44
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.52
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.72
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.22
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16666666666666663
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.12
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09399999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.09333333333333332
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.195
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2333333333333333
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.37233333333333335
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2744024872493329
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3594365079365079
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.20181676147957636
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoDBPedia
      type: NanoDBPedia
    metrics:
    - type: cosine_accuracy@1
      value: 0.46
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.62
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.76
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.82
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.46
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.38666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.38799999999999996
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.344
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.03065300183409328
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.07730098142643593
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.14588470319900892
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.22159653924772912
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3920743245484332
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.567
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.28153419189397744
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoFEVER
      type: NanoFEVER
    metrics:
    - type: cosine_accuracy@1
      value: 0.38
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.54
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.58
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.68
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.38
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.12
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.37
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.52
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.57
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.66
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5156585003907987
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4756666666666666
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.47620972127897226
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoFiQA2018
      type: NanoFiQA2018
    metrics:
    - type: cosine_accuracy@1
      value: 0.28
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.52
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.58
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.28
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.22
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16399999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09799999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1371904761904762
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3226904761904762
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3682142857142857
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.43073809523809525
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3420135901424927
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.38405555555555554
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2826394452885763
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoHotpotQA
      type: NanoHotpotQA
    metrics:
    - type: cosine_accuracy@1
      value: 0.34
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.52
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.62
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.72
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.34
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.19333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14400000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09200000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.29
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.36
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.46
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3723049657456267
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4570793650793651
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2995175868330484
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoMSMARCO
      type: NanoMSMARCO
    metrics:
    - type: cosine_accuracy@1
      value: 0.1
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.28
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.52
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.68
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09333333333333332
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10400000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.068
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.28
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.52
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.68
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.36083481845261806
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.26157142857142857
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.27215692684924997
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNFCorpus
      type: NanoNFCorpus
    metrics:
    - type: cosine_accuracy@1
      value: 0.26
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.38
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.44
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.26
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.21333333333333332
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19599999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.13799999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.01122167476431692
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.02047531859468654
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.03079316493603994
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.0422192068561938
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1654539374427929
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3367460317460317
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.04901233559063261
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNQ
      type: NanoNQ
    metrics:
    - type: cosine_accuracy@1
      value: 0.14
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.36
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.44
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.58
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.11999999999999998
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08800000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06000000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.13
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.34
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.41
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.55
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.33223439819785083
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2734365079365079
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2764557370904448
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoQuoraRetrieval
      type: NanoQuoraRetrieval
    metrics:
    - type: cosine_accuracy@1
      value: 0.82
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.92
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.96
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.82
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.244
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.13399999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7206666666666667
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8553333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8993333333333333
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9566666666666666
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8807317086981499
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8616666666666666
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8525831566094724
      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: 0.48
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.54
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.66
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.34
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.24666666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.212
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.14800000000000002
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.07066666666666668
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.15366666666666667
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.21866666666666668
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.30466666666666664
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.28968259227673265
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4286349206349206
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22985309744949503
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoArguAna
      type: NanoArguAna
    metrics:
    - type: cosine_accuracy@1
      value: 0.18
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.56
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.62
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.84
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18666666666666668
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.124
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08399999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.56
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.62
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.84
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.49726259302609505
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.389079365079365
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3967117258845785
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoSciFact
      type: NanoSciFact
    metrics:
    - type: cosine_accuracy@1
      value: 0.38
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.46
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.48
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.62
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.38
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16666666666666663
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10400000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.068
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.345
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.44
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.46
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.605
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.47012843706683605
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4409285714285714
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.43840522432574647
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoTouche2020
      type: NanoTouche2020
    metrics:
    - type: cosine_accuracy@1
      value: 0.5306122448979592
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7551020408163265
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8571428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9387755102040817
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5306122448979592
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.45578231292517
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.4040816326530612
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.336734693877551
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.03881638827876476
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.10008002766114979
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.13975964122053652
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.22966349775526734
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.39339080810676896
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6553206997084549
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.31344772891929434
      name: Cosine Map@100
  - task:
      type: nano-beir
      name: Nano BEIR
    dataset:
      name: NanoBEIR mean
      type: NanoBEIR_mean
    metrics:
    - type: cosine_accuracy@1
      value: 0.3408163265306122
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5227001569858712
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6013186813186814
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7152904238618524
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3408163265306122
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.23044479330193612
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1855447409733124
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.13344113029827318
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18442678521033212
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.31958052337482684
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3827680868002465
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4886833850587655
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4066287047188099
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4531247913084647
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.33618027996100497
      name: Cosine Map@100
---

# MPNet base trained on Natural Questions pairs

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [gooaq-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives) 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-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives)
- **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-nq-cgist-triplet-3-gte")
# Run inference
sentences = [
    'what energy is released when coal is burned?',
    'When coal is burned, it reacts with the oxygen in the air. This chemical reaction converts the stored solar energy into thermal energy, which is released as heat. But it also produces carbon dioxide and methane.',
    'When coal is burned it releases a number of airborne toxins and pollutants. They include mercury, lead, sulfur dioxide, nitrogen oxides, particulates, and various other heavy metals.',
]
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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### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
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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   | 0.22             | 0.46        | 0.38       | 0.28         | 0.34         | 0.1         | 0.26         | 0.14       | 0.82               | 0.34        | 0.18        | 0.38        | 0.5306         |
| cosine_accuracy@3   | 0.44             | 0.62        | 0.54       | 0.5          | 0.52         | 0.28        | 0.38         | 0.36       | 0.9                | 0.48        | 0.56        | 0.46        | 0.7551         |
| cosine_accuracy@5   | 0.52             | 0.76        | 0.58       | 0.52         | 0.62         | 0.52        | 0.44         | 0.44       | 0.92               | 0.54        | 0.62        | 0.48        | 0.8571         |
| cosine_accuracy@10  | 0.72             | 0.82        | 0.68       | 0.58         | 0.72         | 0.68        | 0.5          | 0.58       | 0.96               | 0.66        | 0.84        | 0.62        | 0.9388         |
| cosine_precision@1  | 0.22             | 0.46        | 0.38       | 0.28         | 0.34         | 0.1         | 0.26         | 0.14       | 0.82               | 0.34        | 0.18        | 0.38        | 0.5306         |
| cosine_precision@3  | 0.1667           | 0.3867      | 0.18       | 0.22         | 0.1933       | 0.0933      | 0.2133       | 0.12       | 0.3667             | 0.2467      | 0.1867      | 0.1667      | 0.4558         |
| cosine_precision@5  | 0.12             | 0.388       | 0.12       | 0.164        | 0.144        | 0.104       | 0.196        | 0.088      | 0.244              | 0.212       | 0.124       | 0.104       | 0.4041         |
| cosine_precision@10 | 0.094            | 0.344       | 0.07       | 0.098        | 0.092        | 0.068       | 0.138        | 0.06       | 0.134              | 0.148       | 0.084       | 0.068       | 0.3367         |
| cosine_recall@1     | 0.0933           | 0.0307      | 0.37       | 0.1372       | 0.17         | 0.1         | 0.0112       | 0.13       | 0.7207             | 0.0707      | 0.18        | 0.345       | 0.0388         |
| cosine_recall@3     | 0.195            | 0.0773      | 0.52       | 0.3227       | 0.29         | 0.28        | 0.0205       | 0.34       | 0.8553             | 0.1537      | 0.56        | 0.44        | 0.1001         |
| cosine_recall@5     | 0.2333           | 0.1459      | 0.57       | 0.3682       | 0.36         | 0.52        | 0.0308       | 0.41       | 0.8993             | 0.2187      | 0.62        | 0.46        | 0.1398         |
| cosine_recall@10    | 0.3723           | 0.2216      | 0.66       | 0.4307       | 0.46         | 0.68        | 0.0422       | 0.55       | 0.9567             | 0.3047      | 0.84        | 0.605       | 0.2297         |
| **cosine_ndcg@10**  | **0.2744**       | **0.3921**  | **0.5157** | **0.342**    | **0.3723**   | **0.3608**  | **0.1655**   | **0.3322** | **0.8807**         | **0.2897**  | **0.4973**  | **0.4701**  | **0.3934**     |
| cosine_mrr@10       | 0.3594           | 0.567       | 0.4757     | 0.3841       | 0.4571       | 0.2616      | 0.3367       | 0.2734     | 0.8617             | 0.4286      | 0.3891      | 0.4409      | 0.6553         |
| cosine_map@100      | 0.2018           | 0.2815      | 0.4762     | 0.2826       | 0.2995       | 0.2722      | 0.049        | 0.2765     | 0.8526             | 0.2299      | 0.3967      | 0.4384      | 0.3134         |

#### 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   | 0.3408     |
| cosine_accuracy@3   | 0.5227     |
| cosine_accuracy@5   | 0.6013     |
| cosine_accuracy@10  | 0.7153     |
| cosine_precision@1  | 0.3408     |
| cosine_precision@3  | 0.2304     |
| cosine_precision@5  | 0.1855     |
| cosine_precision@10 | 0.1334     |
| cosine_recall@1     | 0.1844     |
| cosine_recall@3     | 0.3196     |
| cosine_recall@5     | 0.3828     |
| cosine_recall@10    | 0.4887     |
| **cosine_ndcg@10**  | **0.4066** |
| cosine_mrr@10       | 0.4531     |
| cosine_map@100      | 0.3362     |

<!--
## 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-hard-negatives

* Dataset: [gooaq-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives) at [87594a1](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives/tree/87594a1e6c58e88b5843afa9da3a97ffd75d01c2)
* Size: 50,000 training samples
* Columns: <code>question</code>, <code>answer</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | question                                                                          | answer                                                                              | negative                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 11.53 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 59.79 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 58.76 tokens</li><li>max: 143 tokens</li></ul> |
* Samples:
  | question                                                                              | answer                                                                                                                                                                                                                                                                                                                                      | negative                                                                                                                                                                                                                                                                                                                             |
  |:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what is the difference between calories from fat and total fat?</code>          | <code>Fat has more than twice as many calories per gram as carbohydrates and proteins. A gram of fat has about 9 calories, while a gram of carbohydrate or protein has about 4 calories. In other words, you could eat twice as much carbohydrates or proteins as fat for the same amount of calories.</code>                               | <code>Fat has more than twice as many calories per gram as carbohydrates and proteins. A gram of fat has about 9 calories, while a gram of carbohydrate or protein has about 4 calories. In other words, you could eat twice as much carbohydrates or proteins as fat for the same amount of calories.</code>                        |
  | <code>what is the difference between return transcript and account transcript?</code> | <code>A tax return transcript usually meets the needs of lending institutions offering mortgages and student loans. ... Tax Account Transcript - shows basic data such as return type, marital status, adjusted gross income, taxable income and all payment types. It also shows changes made after you filed your original return.</code> | <code>Trial balance is not a financial statement whereas a balance sheet is a financial statement. Trial balance is solely used for internal purposes whereas a balance sheet is used for purposes other than internal i.e. external. In a trial balance, each and every account is divided into debit (dr.) and credit (cr.)</code> |
  | <code>how long does my dog need to fast before sedation?</code>                       | <code>Now, guidelines are aimed towards 6-8 hours before surgery. This pre-op fasting time is much more beneficial for your pets because you have enough food in there to neutralize the stomach acid, preventing it from coming up the esophagus that causes regurgitation under anesthetic.</code>                                        | <code>Try not to let your pooch rapidly wolf down his/her food! Do not let the dog play or exercise (e.g. go for a walk) for at least two hours after having a meal. Ensure continuous fresh water is available to avoid your pet gulping down a large amount after eating.</code>                                                   |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, '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})
    (2): Normalize()
  ), 'temperature': 0.01}
  ```

### Evaluation Dataset

#### gooaq-hard-negatives

* Dataset: [gooaq-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives) at [87594a1](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives/tree/87594a1e6c58e88b5843afa9da3a97ffd75d01c2)
* Size: 10,048,700 evaluation samples
* Columns: <code>question</code>, <code>answer</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | question                                                                          | answer                                                                              | negative                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 11.61 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 58.16 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 57.98 tokens</li><li>max: 157 tokens</li></ul> |
* Samples:
  | question                                                            | answer                                                                                                                                                                                                                                                                            | negative                                                                                                                                                                                                                                                                                                                                    |
  |:--------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>how is height width and length written?</code>                | <code>The Graphics' industry standard is width by height (width x height). Meaning that when you write your measurements, you write them from your point of view, beginning with the width.</code>                                                                                | <code>The Graphics' industry standard is width by height (width x height). Meaning that when you write your measurements, you write them from your point of view, beginning with the width. That's important.</code>                                                                                                                        |
  | <code>what is the difference between pork shoulder and loin?</code> | <code>All the recipes I've found for pulled pork recommends a shoulder/butt. Shoulders take longer to cook than a loin, because they're tougher. Loins are lean, while shoulders have marbled fat inside.</code>                                                                  | <code>They are extracted from the loin, which runs from the hip to the shoulder, and it has a small strip of meat called the tenderloin. Unlike other pork, this pork chop is cut from four major sections, which are the shoulder, also known as the blade chops, ribs chops, loin chops, and the last, which is the sirloin chops.</code> |
  | <code>is the yin yang symbol religious?</code>                      | <code>The ubiquitous yin-yang symbol holds its roots in Taoism/Daoism, a Chinese religion and philosophy. The yin, the dark swirl, is associated with shadows, femininity, and the trough of a wave; the yang, the light swirl, represents brightness, passion and growth.</code> | <code>Yin energy is in the calm colors around you, in the soft music, in the soothing sound of a water fountain, or the relaxing images of water. Yang (active energy) is the feng shui energy expressed in strong, vibrant sounds and colors, bright lights, upward moving energy, tall plants, etc.</code>                                |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, '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})
    (2): Normalize()
  ), 'temperature': 0.01}
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 2048
- `per_device_eval_batch_size`: 2048
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True

#### 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`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 12
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `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 |
|:-----:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0.04  | 1    | 11.5141       | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2   | 5    | 9.4407        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4   | 10   | 5.6005        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6   | 15   | 3.7323        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8   | 20   | 2.7976        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 1.0   | 25   | 2.1899        | 1.3429          | 0.2744                          | 0.3921                     | 0.5157                   | 0.3420                      | 0.3723                      | 0.3608                     | 0.1655                      | 0.3322                | 0.8807                            | 0.2897                     | 0.4973                     | 0.4701                     | 0.3934                        | 0.4066                       |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.105 kWh
- **Carbon Emitted**: 0.041 kg of CO2
- **Hours Used**: 0.3 hours

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

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.4.0.dev0
- Transformers: 4.46.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.35.0.dev0
- Datasets: 2.20.0
- Tokenizers: 0.20.3

## Citation

### BibTeX

#### Sentence Transformers
```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",
}
```

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