checkpoint-856/README.md

---
base_model: bobox/DeBERTa-small-ST-v1-test-step3
datasets:
- tals/vitaminc
- allenai/scitail
- allenai/sciq
- allenai/qasc
- sentence-transformers/msmarco-msmarco-distilbert-base-v3
- sentence-transformers/natural-questions
- sentence-transformers/trivia-qa
- sentence-transformers/gooaq
- google-research-datasets/paws
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:163205
- loss:CachedGISTEmbedLoss
widget:
- source_sentence: who is the president of chil
  sentences:
  - An endoscopic retrograde cholangiopancreatogram (ERCP) is usually done by a gastroenterologist.
    This is a doctor who has special training in diseases of the digestive system.
    The doctor must be trained in endoscopy. A thin, flexible fiber-optic endoscope
    (scope) is used.
  - "No. Cleveland Cavaliers guard, Kyrie Irving, is not the son of former NBA great\
    \ Julius Erving. Kyrie Irving's father is former Australian professional basketball\
    \ player Drederick Irving. Kyrie's mother, Elizabeth (who was a standout athlete\
    \ herself), died when Kyrie was just four. Since then, dad Drederick raised him\
    \ as a single parent and served as Kyrieâ\x80\x99s first basketball coach."
  - Veronica Verónica Michelle Bachelet jeria is The president Of. Chile she was
    the first Female president Of chile from 2006 march - 11 2010 march, 11 and was-re
    elected for the-2014 2018. Term'chile s presidents are not-re electable. immediatelyhe
    was the first female President of Chile from 2006 March 11 - 2010 March 11, and
    was re-elected for the 2014-2018 term.
- source_sentence: More than 273 people have died from the 2019-20 coronavirus outside
    mainland China .
  sentences:
  - 'More than 3,700 people have died : around 3,100 in mainland China and around
    550 in all other countries combined .'
  - 'More than 3,200 people have died : almost 3,000 in mainland China and around
    275 in other countries .'
  - more than 4,900 deaths have been attributed to COVID-19 .
- source_sentence: The movement of an air mass over earth's surface causes local weather
    changes.
  sentences:
  - The movement of an air mass over Earth's surface causes
  - Which best explains why children resemble their parents?
  - What ensures that seeds germinate only when conditions for seedling survival are
    optimal?
- source_sentence: Heirloom seeds come from plants that were traditionally grown in
    human populations, as opposed to the seeds used for large-scale agricultural production.
  sentences:
  - What do you call a substance that is not an acid or a base?
  - What type of seeds come from plants that were traditionally grown in human populations,
    as opposed to the seeds used for large-scale agricultural production?
  - High consumption of saturated fats is linked to an increased risk of what disease?
- source_sentence: 2012 was the 300th anniversary of the world's first industrial
    steam-powered machine, which was a?
  sentences:
  - Armed Forces Radio Vietnam | Video | C-SPAN.org Supreme Court November 11, 2006
    Armed Forces Radio in Vietnam Adrian Cronauer talked about being a disc jockey
    in Vietnam and the movie based on his experiences, “Good Morning, Vietnam!” The
    ninth… read more Armed Forces Radio in Vietnam Adrian Cronauer talked about being
    a disc jockey in Vietnam and the movie based on his experiences, “Good Morning,
    Vietnam!” The ninth annual conference of the World War II Veterans Committee for
    the first time expanded to include the Vietnam War under the new umbrella organization,
    the American Veterans Center. close Transcript type
  - The Worlds First Steam Engine 300th Anniversary - YouTube The Worlds First Steam
    Engine 300th Anniversary Want to watch this again later? Sign in to add this video
    to a playlist. Need to report the video? Sign in to report inappropriate content.
    Rating is available when the video has been rented. This feature is not available
    right now. Please try again later. Published on Nov 14, 2012 The replica Newcomen
    Pumping Engine at the Black Country Living Museum in Dudley has been brought back
    to life for the 300th anniversary of the first recorded practical application
    of steam power. The self-acting valve gear is a clanking cacophony of joy! The
    restoration team were helped by Guy Martin and the engine was featured in the
    Channel 4 documentary series 'How Britain Worked' (as was my own miniature Newcomen
    engine model, used by Guy to demonstrate how the engine works!) See my other vids
    for some footage of my little 'un in action! Category
  - YouTube | Logopedia | Fandom powered by Wikia 2015–present 2005–2011 The logo
    consists of the black word "You" and a red rounded rectangle with the word "Tube"
    in it next to it. This logo is still being used on some other pages. Logo with
    the slogan "Broadcast Yourself". Notice that the red square looks different in
    this variation. Add a photo to this gallery 2011–2013 This modification of the
    YouTube logo was introduced in July 2011 as a part of the Cosmic Panda experiment.
    It officially became the new logo a few months later. It has the red square in
    a darker color this time. Also, starting in 2012, the slogan "Broadcast Yourself"
    was retired. 2013–2015 On December 19, 2013, the red rectangle was made lighter
    in color. Also, the word "You" was made more black and the shadow behind the word
    "Tube" was removed. This is still used as a secondary logo. Alternate Version,
    only for social media.
model-index:
- name: SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test-step3
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.8846526004146165
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9069678627017289
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9046900352481024
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.9004067012640901
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.903648547377558
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8991392112877015
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8694917588757636
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8661496554202353
      name: Spearman Dot
    - type: pearson_max
      value: 0.9046900352481024
      name: Pearson Max
    - type: spearman_max
      value: 0.9069678627017289
      name: Spearman Max
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: allNLI dev
      type: allNLI-dev
    metrics:
    - type: cosine_accuracy
      value: 0.73828125
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7839766144752502
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.6371681415929203
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.5921227931976318
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.5161290322580645
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8323699421965318
      name: Cosine Recall
    - type: cosine_ap
      value: 0.6377040389504978
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.72265625
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 287.0504150390625
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.615
      name: Dot F1
    - type: dot_f1_threshold
      value: 242.96902465820312
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.5418502202643172
      name: Dot Precision
    - type: dot_recall
      value: 0.7109826589595376
      name: Dot Recall
    - type: dot_ap
      value: 0.5976338107788919
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.73828125
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 287.6244812011719
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.6461538461538462
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 341.6584167480469
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.5806451612903226
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.7283236994219653
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.6359733229236403
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.740234375
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 13.528583526611328
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.6419753086419753
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 16.527751922607422
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.5603448275862069
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.7514450867052023
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.6364974679625237
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.740234375
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 287.6244812011719
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.6461538461538462
      name: Max F1
    - type: max_f1_threshold
      value: 341.6584167480469
      name: Max F1 Threshold
    - type: max_precision
      value: 0.5806451612903226
      name: Max Precision
    - type: max_recall
      value: 0.8323699421965318
      name: Max Recall
    - type: max_ap
      value: 0.6377040389504978
      name: Max Ap
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Qnli dev
      type: Qnli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.69140625
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.657210111618042
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.6783439490445861
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.48659271001815796
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.5433673469387755
      name: Cosine Precision
    - type: cosine_recall
      value: 0.902542372881356
      name: Cosine Recall
    - type: cosine_ap
      value: 0.714041144664227
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.669921875
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 256.53155517578125
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.678125
      name: Dot F1
    - type: dot_f1_threshold
      value: 178.63389587402344
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.5371287128712872
      name: Dot Precision
    - type: dot_recall
      value: 0.9194915254237288
      name: Dot Recall
    - type: dot_ap
      value: 0.6648303273464243
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.697265625
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 350.3825988769531
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.6733466933867736
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 364.5922546386719
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.6387832699619772
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.711864406779661
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7225583351616828
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.6953125
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 16.298263549804688
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.6771929824561403
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 18.734228134155273
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.5778443113772455
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.8177966101694916
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.7198419743004867
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.697265625
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 350.3825988769531
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.6783439490445861
      name: Max F1
    - type: max_f1_threshold
      value: 364.5922546386719
      name: Max F1 Threshold
    - type: max_precision
      value: 0.6387832699619772
      name: Max Precision
    - type: max_recall
      value: 0.9194915254237288
      name: Max Recall
    - type: max_ap
      value: 0.7225583351616828
      name: Max Ap
---

# SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test-step3

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bobox/DeBERTa-small-ST-v1-test-step3](https://huggingface.co/bobox/DeBERTa-small-ST-v1-test-step3) on the negation-triplets, [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), xsum-pairs, [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), openbookqa_pairs, [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq), [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) and global_dataset datasets. 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:** [bobox/DeBERTa-small-ST-v1-test-step3](https://huggingface.co/bobox/DeBERTa-small-ST-v1-test-step3) <!-- at revision df9aaa75fe0c2791e5ed35ff33de1689d9a5f5ff -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
    - negation-triplets
    - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc)
    - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail)
    - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail)
    - xsum-pairs
    - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq)
    - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc)
    - openbookqa_pairs
    - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3)
    - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions)
    - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa)
    - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq)
    - [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws)
    - global_dataset
- **Language:** en
<!-- - **License:** Unknown -->

### 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: DebertaV2Model 
  (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("bobox/DeBERTa2-0.9B-ST-v2-checkpoints-tmp")
# Run inference
sentences = [
    "2012 was the 300th anniversary of the world's first industrial steam-powered machine, which was a?",
    "The Worlds First Steam Engine 300th Anniversary - YouTube The Worlds First Steam Engine 300th Anniversary Want to watch this again later? Sign in to add this video to a playlist. Need to report the video? Sign in to report inappropriate content. Rating is available when the video has been rented. This feature is not available right now. Please try again later. Published on Nov 14, 2012 The replica Newcomen Pumping Engine at the Black Country Living Museum in Dudley has been brought back to life for the 300th anniversary of the first recorded practical application of steam power. The self-acting valve gear is a clanking cacophony of joy! The restoration team were helped by Guy Martin and the engine was featured in the Channel 4 documentary series 'How Britain Worked' (as was my own miniature Newcomen engine model, used by Guy to demonstrate how the engine works!) See my other vids for some footage of my little 'un in action! Category",
    'YouTube | Logopedia | Fandom powered by Wikia 2015–present 2005–2011 The logo consists of the black word "You" and a red rounded rectangle with the word "Tube" in it next to it. This logo is still being used on some other pages. Logo with the slogan "Broadcast Yourself". Notice that the red square looks different in this variation. Add a photo to this gallery 2011–2013 This modification of the YouTube logo was introduced in July 2011 as a part of the Cosmic Panda experiment. It officially became the new logo a few months later. It has the red square in a darker color this time. Also, starting in 2012, the slogan "Broadcast Yourself" was retired. 2013–2015 On December 19, 2013, the red rectangle was made lighter in color. Also, the word "You" was made more black and the shadow behind the word "Tube" was removed. This is still used as a secondary logo. Alternate Version, only for social media.',
]
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]
```

<!--
### Direct Usage (Transformers)

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

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.8847    |
| **spearman_cosine** | **0.907** |
| pearson_manhattan   | 0.9047    |
| spearman_manhattan  | 0.9004    |
| pearson_euclidean   | 0.9036    |
| spearman_euclidean  | 0.8991    |
| pearson_dot         | 0.8695    |
| spearman_dot        | 0.8661    |
| pearson_max         | 0.9047    |
| spearman_max        | 0.907     |

#### Binary Classification
* Dataset: `allNLI-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.7383     |
| cosine_accuracy_threshold    | 0.784      |
| cosine_f1                    | 0.6372     |
| cosine_f1_threshold          | 0.5921     |
| cosine_precision             | 0.5161     |
| cosine_recall                | 0.8324     |
| cosine_ap                    | 0.6377     |
| dot_accuracy                 | 0.7227     |
| dot_accuracy_threshold       | 287.0504   |
| dot_f1                       | 0.615      |
| dot_f1_threshold             | 242.969    |
| dot_precision                | 0.5419     |
| dot_recall                   | 0.711      |
| dot_ap                       | 0.5976     |
| manhattan_accuracy           | 0.7383     |
| manhattan_accuracy_threshold | 287.6245   |
| manhattan_f1                 | 0.6462     |
| manhattan_f1_threshold       | 341.6584   |
| manhattan_precision          | 0.5806     |
| manhattan_recall             | 0.7283     |
| manhattan_ap                 | 0.636      |
| euclidean_accuracy           | 0.7402     |
| euclidean_accuracy_threshold | 13.5286    |
| euclidean_f1                 | 0.642      |
| euclidean_f1_threshold       | 16.5278    |
| euclidean_precision          | 0.5603     |
| euclidean_recall             | 0.7514     |
| euclidean_ap                 | 0.6365     |
| max_accuracy                 | 0.7402     |
| max_accuracy_threshold       | 287.6245   |
| max_f1                       | 0.6462     |
| max_f1_threshold             | 341.6584   |
| max_precision                | 0.5806     |
| max_recall                   | 0.8324     |
| **max_ap**                   | **0.6377** |

#### Binary Classification
* Dataset: `Qnli-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.6914     |
| cosine_accuracy_threshold    | 0.6572     |
| cosine_f1                    | 0.6783     |
| cosine_f1_threshold          | 0.4866     |
| cosine_precision             | 0.5434     |
| cosine_recall                | 0.9025     |
| cosine_ap                    | 0.714      |
| dot_accuracy                 | 0.6699     |
| dot_accuracy_threshold       | 256.5316   |
| dot_f1                       | 0.6781     |
| dot_f1_threshold             | 178.6339   |
| dot_precision                | 0.5371     |
| dot_recall                   | 0.9195     |
| dot_ap                       | 0.6648     |
| manhattan_accuracy           | 0.6973     |
| manhattan_accuracy_threshold | 350.3826   |
| manhattan_f1                 | 0.6733     |
| manhattan_f1_threshold       | 364.5923   |
| manhattan_precision          | 0.6388     |
| manhattan_recall             | 0.7119     |
| manhattan_ap                 | 0.7226     |
| euclidean_accuracy           | 0.6953     |
| euclidean_accuracy_threshold | 16.2983    |
| euclidean_f1                 | 0.6772     |
| euclidean_f1_threshold       | 18.7342    |
| euclidean_precision          | 0.5778     |
| euclidean_recall             | 0.8178     |
| euclidean_ap                 | 0.7198     |
| max_accuracy                 | 0.6973     |
| max_accuracy_threshold       | 350.3826   |
| max_f1                       | 0.6783     |
| max_f1_threshold             | 364.5923   |
| max_precision                | 0.6388     |
| max_recall                   | 0.9195     |
| **max_ap**                   | **0.7226** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Datasets

#### negation-triplets

* Dataset: negation-triplets
* Size: 7,500 training samples
* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | entailment                                                                        | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 21.77 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.89 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.24 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
  | anchor                                                                                          | entailment                                                                                                                                                      | negative                                                                                                                                                              |
  |:------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Exploring inland to the north is difficult on foot, The streets become very steep.</code> | <code>It is difficult to explore inland on foot.</code>                                                                                                         | <code>It is easy to explore inland by foot.</code>                                                                                                                    |
  | <code>Michele is the lead singer in 14 of the top 20 selling Glee songs as of 2010 .</code>     | <code>Michele is featured lead singer in 14 of the top 20 selling Glee songs as of 2010 .</code>                                                                | <code>Michele is featured lead singer in none of the top 20 selling Glee songs as of 2010.</code>                                                                     |
  | <code>Its theme is show business , based on Hollywood in the 1930s and 1940s .</code>           | <code>Spanning 135 acres ( 546,000 m ) in size , its theme is show business , drawing inspiration from the heyday of Hollywood in the 1930s and 1940s .</code> | <code>Covering 25 acres ( 101,000 m ) in size , its theme is agriculture , drawing inspiration from the modern era of Silicon Valley in the 2000s and 2010s .</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### vitaminc-pairs

* Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0)
* Size: 7,500 training samples
* Columns: <code>claim</code> and <code>evidence</code>
* Approximate statistics based on the first 1000 samples:
  |         | claim                                                                             | evidence                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 7 tokens</li><li>mean: 17.12 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 38.83 tokens</li><li>max: 187 tokens</li></ul> |
* Samples:
  | claim                                                                                                                      | evidence                                                                                                                                                                                                                                                                                          |
  |:---------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Prokhorov offered Joseph Tsai , the executive vice-chairman of the Alibaba Group , a stake in Brooklyn Nets .</code> | <code>In late 2017 , there were multiple reports of an agreement for Prokhorov to sell a 49 % stake in the team to Joseph Tsai , the executive vice chairman of the Alibaba Group , with an option for Tsai to become the majority owner .</code>                                                 |
  | <code>Vartan 's father was of Armenian descent .</code>                                                                    | <code>Vartan 's father was Bulgarian-born and of part Armenian and Hungarian descent and Vartan 's mother is an American Jew originally from Poland ; Vartan has said about his French background that “ The funny thing is I ’ m actually a Polish Jew who happens to be born in France .</code> |
  | <code>Big Sean was born on 25 March 1987 .</code>                                                                          | <code>Sean Michael Leonard Anderson ( born March 25 , 1987 ) , known professionally as Big Sean , is an American hip hop recording artist , comedian , singer-songwriter .</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### scitail-pairs-qa

* Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 7,118 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 15.71 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.76 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | sentence1                                                                               | sentence2                                                           |
  |:----------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
  | <code>The hair cells sense(s) the movement of liquid in ear canals.</code>              | <code>What senses the movement of liquid in ear canals?</code>      |
  | <code>Evidence allows theories to be widely accepted.</code>                            | <code>What allows theories to be widely accepted?</code>            |
  | <code>The field of study known as mathematics is called the language of science.</code> | <code>What field of study is called the language of science?</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### scitail-pairs-pos

* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 4,300 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 23.62 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.55 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                | sentence2                                                                    |
  |:---------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|
  | <code>Plants convert carbon dioxide into oxygen as a byproduct of photosynthesis.</code>                 | <code>Oxygen is made by trees and other plants during photosynthesis.</code> |
  | <code>Kinetic energy is the energy of bodies in motion.</code>                                           | <code>Kinetic engergy is the energy of anything in motion.</code>            |
  | <code>Digestion begins in the mouth where salivary amylase starts the breakdown of carbohydrates.</code> | <code>Carbohydrate digestion begins in the mouth.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### xsum-pairs

* Dataset: xsum-pairs
* Size: 7,500 training samples
* Columns: <code>summary</code> and <code>document</code>
* Approximate statistics based on the first 1000 samples:
  |         | summary                                                                           | document                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               |
  | details | <ul><li>min: 8 tokens</li><li>mean: 25.65 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 49 tokens</li><li>mean: 221.16 tokens</li><li>max: 421 tokens</li></ul> |
* Samples:
  | summary                                                                                                                                          | document                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>A council has received complaints about people urinating and defecating outdoors after a village's public toilets were closed down.</code> | <code>The toilets at Staffin on Skye were among a number of Highland Council-run sites shut this year to save money.<br>The council has been in talks with Staffin Community Trust about alternative facilities.<br>In the meantime, two complaints have been received public urination and defecation in and around the village.<br>The nearest public toilet is in Kilmuir about 11 miles (17km) from Staffin.<br>Residents have told BBC Alba of people using ditches, a nearby quarry and the rear of the closed toilet block.<br>Highland Council it had cost £6,700 a year to run Staffin's loos.<br>A spokeswoman said: "We cannot confirm that people have been urinating and defecating in the area - we have found no evidence to support these claims.<br>"There have been two complaints made, one to environmental health and one to roads and community works.<br>"In addition to this we have had three enquiries about Staffin toilets."<br>She added: "The council has been in discussions with Staffin Community Trust and have agreed in principle to operate a seasonal - April to October - Highland Comfort Scheme within the community hall."</code>                            |
  | <code>More than 70 workers have been taken off a North Sea oil platform after it suffered a loss of power.</code>                                | <code>The coastguard was first alerted to the issue on the Bruce installation, east of Shetland, just after 20:00 on Thursday.<br>A total of 76 people from the 121 on board were taken off the platform and flown to neighbouring installations in coastguard helicopters.<br>Forty-five workers stayed on the platform in a bid to restore power.<br>BP said work to restore power was ongoing.<br>A Coastguard spokesman said: "Following a loss of power on the Bruce platform off Aberdeen late on Thursday 22nd June, HM Coastguard rescue helicopters from Shetland and Inverness were tasked to partially down-man the platform.<br>"Seventy six non-essential personnel were transferred overnight by helicopter to other platforms in the area."</code>                                                                                                                                                                                                                                                                                                                                                                                                                                      |
  | <code>Shaun Hutchinson has agreed a two-year deal with League One club Millwall after his recent release by Fulham.</code>                       | <code>The defender, 25, who spent two years at Craven Cottage, will officially join the Lions when his Fulham contract expires at the end of the month.<br>"Coming to work at a place where you are so wanted is a great feeling," he told the Lions' website.<br>"I can't wait to get going and am looking forward to putting on the Millwall shirt."<br>Newcastle-born Hutchinson made 121 league appearances for Motherwell before Fulham signed him from the Scottish side after a number of clubs - including Millwall - had shown an interest in him.<br>However, he struggled to establish himself as a first-team regular, making 34 league appearances during his time in west London.<br>"Shaun is a player that as a club we have been aware of for quite a period of time," said Millwall manager Neil Harris.<br>"We tracked him diligently when he was at Motherwell and when he was moving down south we bid for him.<br>"We monitored him last season knowing that he was coming out of contract.<br>"He is a no-nonsense defender who likes to head it in both boxes and is very aggressive in his approach."<br>Find all the latest football transfers on our dedicated page.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### sciq_pairs

* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815)
* Size: 5,547 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 7 tokens</li><li>mean: 17.08 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 90.28 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                              | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
  |:-----------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>A rounded hollow carved in the side of a mountain by a glacier is known as?</code>                               | <code>A cirque is a rounded hollow carved in the side of a mountain by a glacier. The highest cliff of a cirque is called the headwall.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
  | <code>What is a group of lions called?</code>                                                                          | <code>Lions live in social groups called prides . Adult females in the pride hunt cooperatively, which is more efficient than hunting alone. Then they share the food with the rest of the pride. For their part, adult males defend the pride’s territory from other predators.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
  | <code>What is a measure of the average amount of energy of motion, or kinetic energy, a system contains called?</code> | <code>There are other units in chemistry that are important, and we will cover others in the course of the entire book. One of the fundamental quantities in science is temperature. Temperature is a measure of the average amount of energy of motion, or kinetic energy, a system contains. Temperatures are expressed using scales that use units called degrees, and there are several temperature scales in use. In the United States, the commonly used temperature scale is the Fahrenheit scale (symbolized by °F and spoken as “degrees Fahrenheit”). On this scale, the freezing point of liquid water (the temperature at which liquid water turns to solid ice) is 32°F, and the boiling point of water (the temperature at which liquid water turns to steam) is 212°F. Science also uses other scales to express temperature. The Celsius scale (symbolized by °C and spoken as “degrees Celsius”) is a temperature scale where 0°C is the freezing point of water and 100°C is the boiling point of water; the scale is divided into 100 divisions between these two landmarks and extended higher and lower. By comparing the Fahrenheit and Celsius scales, a conversion between the two scales can be determined: °C=(°F–32) × 59° Saylor URL: http://www. saylor. org/books.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### qasc_pairs

* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070)
* Size: 3,863 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 11.39 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 33.65 tokens</li><li>max: 74 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                   | sentence2                                                                                                                                                                                                                                                                              |
  |:------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Meat, fish and chicken are used to do what by the human body?</code>                                  | <code>protein is used to repair cells by the human body. Meat, fish and chicken are rich sources of protein. <br> Meat, fish and chicken are used to repair cells by the human body.</code>                                                                                            |
  | <code>A mutation in the sex cells of a parent can cause a new trait to appear in the parent 's what?</code> | <code>a mutation in the sex cells of a parent can cause a new trait to appear in the parent 's offspring. Child' is ambiguous between 'offspring' and 'immature offspring'. <br> A mutation in the sex cells of a parent can cause a new trait to appear in the parent's child.</code> |
  | <code>Weathering means breaking down what from larger whole into smaller pieces by weather?</code>          | <code>weathering means breaking down rocks from larger whole into smaller pieces by weather. Igneous rocks are made from lava that hardens. <br> weathering means breaking down something made from lava from larger whole into smaller pieces by weather</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### openbookqa_pairs

* Dataset: openbookqa_pairs
* Size: 2,261 training samples
* Columns: <code>question</code> and <code>fact</code>
* Approximate statistics based on the first 1000 samples:
  |         | question                                                                          | fact                                                                              |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 13.94 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.67 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
  | question                                                                                                                  | fact                                                                     |
  |:--------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|
  | <code>Skills are learned characteristics. To get better at doing something, you must stretch yourself in ways that</code> | <code>skills are learned characteristics</code>                          |
  | <code>the lunar surface contains</code>                                                                                   | <code>the moon 's surface contains flat areas</code>                     |
  | <code>natural disasters can cause animals to</code>                                                                       | <code>natural disasters can cause animals to leave an environment</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### msmarco_pairs

* Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9)
* Size: 7,500 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                           |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              |
  | details | <ul><li>min: 4 tokens</li><li>mean: 8.62 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 76.31 tokens</li><li>max: 195 tokens</li></ul> |
* Samples:
  | sentence1                                            | sentence2                                                                                                                                                                                                                                                                                                                                                             |
  |:-----------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what county is youngsville nc in</code>        | <code>My Home Town. Youngsville is a growing town in southwestern Franklin County, rich in history and full of promise. Its location in the prosperous Triangle region of North Carolina has meant that Youngsville has seen its share of growth, but without losing the rural charm that has made it a desired destination for families and businesses alike.</code> |
  | <code>what college did allan houston play for</code> | <code>Wade Houston. Wade Houston (born October 9, 1944) is an American former college basketball player and coach. He was an assistant coach under Denny Crum at the University of Louisville for 13 years until 1989 when he was named the head coach of the University of Tennessee.</code>                                                                         |
  | <code>what does the rock</code>                      | <code>Webster Dictionary(5.00 / 1 vote)Rate this definition: 1  Rock(noun) see Roc. 2  Rock(noun) a distaff used in spinning; the staff or frame about which flax is arranged, and from which the thread is drawn in spinning. 3  Rock(noun) a large concreted mass of stony material; a large fixed stone or crag.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### nq_pairs

* Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 7,500 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               |
  | details | <ul><li>min: 9 tokens</li><li>mean: 11.66 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 131.66 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence1                                                                              | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
  |:---------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>where did they film last man on earth</code>                                     | <code>The Last Man on Earth (TV series) The main recording location for the series is a 20th Century Fox studio in Chatsworth, California.[35][36][37]</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  | <code>when was the first soviet atomic bomb tested</code>                              | <code>Soviet atomic bomb project On 29 August 1949, the Soviet Union secretly conducted its first successful weapon test (First Lightning), based on the U.S. design at the Semipalatinsk in Kazakhstan.[2]</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
  | <code>which two chambers make the united kingdom a bicameral type of government</code> | <code>Parliament of the United Kingdom The parliament is bicameral, consisting of an upper house (the House of Lords) and a lower house (the House of Commons).[4] The Sovereign forms the third component of the legislature (the Queen-in-Parliament).[5][6] The House of Lords includes two different types of members: the Lords Spiritual, consisting of the most senior bishops of the Church of England, and the Lords Temporal, consisting mainly of life peers, appointed by the Sovereign on the advice of the Prime Minister,[7] and of 92 hereditary peers, sitting either by virtue of holding a royal office, or by being elected by their fellow hereditary peers. Prior to the opening of the Supreme Court in October 2009, the House of Lords also performed a judicial role through the Law Lords.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### trivia_pairs

* Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0)
* Size: 6,012 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                             | answer                                                                               |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               |
  | details | <ul><li>min: 7 tokens</li><li>mean: 17.19 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 203.25 tokens</li><li>max: 499 tokens</li></ul> |
* Samples:
  | query                                                                                        | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
  |:---------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Which resort was advertised in a travel poster as '............ is so bracing'?</code> | <code>Kath - Postcards From the Past - Other Seaside Places                                                                          Kath - Postcards from the Past -                                        Mablethorpe, Skegness, Sutton-on-Sea and Ingoldmells              Here are my postcards of Mablethorpe, Skegness,Sutton-on-Sea and Ingoldmells which together with Cleethorpes (see Cleethorpes separate pages) make up the major seaside resorts of Lincolnshire. These are typical British seaside resorts with all the usual expected amenities. Ingoldmells is a small coastal village and holiday resort three miles to the north of Skegness and is the home to the original Butlin's Holiday Camp built in 1936 which s still very popular today. Mablethorpe is located between Cleethorpes and Skegness and boasts all the usual attractions. It is home to the popular Golden Sands Holiday Park and there is a seal sanctuary situated at North End which has rescued hundreds of seals over the years. Skegness is popular with holiday makers and day-trippers from the Midlands, it was once a small port but from 1877 it was developed into a holiday resort. In 1908 it acquired it's famous "Jolly Fisherman" mascot taken from a Railway travel poster entitled "Skegness is so Bracing". Sutton-on-Sea, a few miles south of Mablethorpe, is one of the smaller seaside resorts being described as a "charming and tranquil village", it has golden sands, the usual attractions and golf can be played at nearby Sandilands. Click on the link for a larger image.</code> |
  | <code>Amman is the capital city of which country?</code>                                     | <code>Jordan Facts on Largest Cities, Populations, Symbols - Worldatlas.com Ethnicity: Arab 98%, Circassian 1%, Armenian 1% GDP total: $38.67 billion (2012 est.) GDP per capita: $6,000 (2012 est.) Language: Arabic (official), English widely understood among upper and middle classes Largest Cities: (by population) Amman, Irbid Name: Aramaic Yarden in origin, meaning "down-flowing," or "one who descends," and is named after the River Jordan National Day: May 25 Religion: Sunni Muslim 92%, Christian 6% (majority Greek Orthodox, but some Greek and Roman Catholics, Syrian Orthodox, Coptic Orthodox, Armenian Orthodox, and Protestant denominations), other 2% (several small Shia Muslim and Druze populations)</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  | <code>A famous sports car named after Argentina's hot dusty North wind, is the?</code>       | <code>Tamerlane's Thoughts: Cars named after winds Cars named after winds Bora (ancient Greek name for north wind) Ghibli (Libyan wind) Khamsin (hot dusty wind in North Africa and Arabian Peninsula) Mistral (wind from the north that blows over the northwest coast of the Mediterranean) Shamal (summer wind over Iraq and Persian Gulf) Pagani: Zonda (Andean wind in Argentina) VW Passat ("trade wind" in German) Santana (Santa Ana wind (subject to debate)) Scirocco (warm wind in Mediterranean) Yugo (it is not derived from the word Yugoslavia; rather, yugo is a southeasterly wind on the Adriatic) 10 comments: Anonymous said... What about the Ford Zephyr and the Zonda and the Austin Maestro or if you want a helicopter try the Chinook for size, wind would seem popular for names kashgar216 said... Thanks for playing! I'll add the Zephyr. The Diablo is a stretch. There is a wind called that but were the Lambo people thinking-- let's name our next supercar after an obscure Northern California wind? I'm also debating the Maestro thing. Check back soon for an update!</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### gooaq_pairs

* Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 7,500 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 11.58 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 57.14 tokens</li><li>max: 132 tokens</li></ul> |
* Samples:
  | sentence1                                              | sentence2                                                                                                                                                                                                                                                                                          |
  |:-------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>are starkist tuna cans bpa free?</code>          | <code>The StarKist Chunk Light canned tuna we tested averaged 3 ppb of BPA, but BPA levels in the same brand in a plastic pouch weren't measurable. ... We tested two products that their manufacturers claimed were packaged in BPA-free cans and found the chemical in both of the foods.</code> |
  | <code>is a 2gb graphics card enough?</code>            | <code>Generally speaking, for 1080p gaming, 2GB of video memory is an adequate minimum, but 4GB is much better. In cards under $300 nowadays, you'll see graphics memory ranging from 1GB up to 8GB.</code>                                                                                        |
  | <code>how much does it cost to jump your phone?</code> | <code>To get Jump, sign up to pay the full price of your new phone in 24 equal monthly installments. Then sign up for the Jump program, which costs an additional $9 to $12 per month, depending on your phone.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### paws-pos

* Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09)
* Size: 7,500 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           |
  | details | <ul><li>min: 9 tokens</li><li>mean: 25.78 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 25.7 tokens</li><li>max: 57 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                     | sentence2                                                                                                                                       |
  |:----------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Quintus Caecilius Metellus Macedonicus was the second son of Roman politician and general Lucius Caecilius Metellus Diadematus .</code> | <code>Quintus Caecilius Metellus Macedonicus was the second son of the Roman politician , General Lucius Caecilius Metellus Diadematus .</code> |
  | <code>She moved to Switzerland when she was a few months old , then to France , but mostly grew up in Paris .</code>                          | <code>She moved to Switzerland when she was a few months old , then grew up to France , but largely in Paris .</code>                           |
  | <code>The NBA season from 1979 to 80 was the 34th season of the National Basketball Association .</code>                                      | <code>The 1979 -- 80 National Basketball Association season was the 34th season of the NBA .</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### global_dataset

* Dataset: global_dataset
* Size: 81,604 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 35.5 tokens</li><li>max: 335 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 59.34 tokens</li><li>max: 415 tokens</li></ul> |
* Samples:
  | sentence1                                                           | sentence2                                                                                                                                                                                                                                                                                                                  |
  |:--------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Great egrets get food by doing what?</code>                   | <code>Heterotrophs get food by eating other living things.. Great egrets are heterotrophs . <br> Great egrets get food by eating other livings things.</code>                                                                                                                                                              |
  | <code>is oil price going to increase</code>                         | <code>The current downward swing in oil prices has raised a similar specter of low oil prices for a prolonged period. So far, this year oil prices have dropped by more than 57% from last year's prices. However, the decline may be temporary. According to analysts, oil prices will rise back up again in 2017.</code> |
  | <code>Vascular plants have a dominant sporophyte generation.</code> | <code>Do nonvascular or vascular plants have a dominant sporophyte generation?</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

### Evaluation Datasets

#### vitaminc-pairs

* Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0)
* Size: 128 evaluation samples
* Columns: <code>claim</code> and <code>evidence</code>
* Approximate statistics based on the first 1000 samples:
  |         | claim                                                                             | evidence                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 21.42 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 35.55 tokens</li><li>max: 79 tokens</li></ul> |
* Samples:
  | claim                                                                               | evidence                                                                                                                                                                                                                                                                                                                                               |
  |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Dragon Con had over 5000 guests .</code>                                      | <code>Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell .</code>                                                                                                          |
  | <code>COVID-19 has reached more than 185 countries .</code>                         | <code>As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths .</code>                                                                                                                                                                                                   |
  | <code>In March , Italy had 3.6x times more cases of coronavirus than China .</code> | <code>As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China .</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### negation-triplets

* Dataset: negation-triplets
* Size: 128 evaluation samples
* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | entailment                                                                        | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                           |
  | details | <ul><li>min: 8 tokens</li><li>mean: 13.98 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.47 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.7 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
  | anchor                                                                    | entailment                                                                         | negative                                                                                |
  |:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
  | <code>A bathroom with a toilet, sink, and shower.</code>                  | <code>A full bathroom with a wicker laundry basket.</code>                         | <code>An empty bathroom with no laundry basket.</code>                                  |
  | <code>A person in a helmet standing by their motorcycle.</code>           | <code>A motorcyclist stands next to a motorcycle at a lookout over a beach.</code> | <code>A motorcyclist stands nowhere near a motorcycle at a lookout over a beach.</code> |
  | <code>A bathroom with a poster of an ugly face above the toilette.</code> | <code>A bathroom with a white toilet and sink.</code>                              | <code>A bathroom with a black toilet and sink.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### scitail-pairs-pos

* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 9 tokens</li><li>mean: 20.28 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.48 tokens</li><li>max: 23 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                     | sentence2                                                                              |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
  | <code>humans normally have 23 pairs of chromosomes.</code>                                                                                                                                    | <code>Humans typically have 23 pairs pairs of chromosomes.</code>                      |
  | <code>A solution is a homogenous mixture of two or more substances that exist in a single phase.</code>                                                                                       | <code>Solution is the term for a homogeneous mixture of two or more substances.</code> |
  | <code>Upwelling The physical process in near-shore ocean systems of rising of nutrients and colder bottom waters to the surface because of constant wind patterns along the shoreline.</code> | <code>Upwelling is the term for when deep ocean water rises to the surface.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### scitail-pairs-qa

* Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 16.12 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.41 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                   | sentence2                                                                                        |
  |:------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
  | <code>The hair cells sense(s) the movement of liquid in ear canals.</code>                                  | <code>What senses the movement of liquid in ear canals?</code>                                   |
  | <code>A metallic bond is the force of attraction between a positive metal ion and valence electrons.</code> | <code>What is the force of attraction between a positive metal ion and valence electrons?</code> |
  | <code>High consumption of saturated fats is linked to an increased risk of cardiovascular disease.</code>   | <code>High consumption of saturated fats is linked to an increased risk of what disease?</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### xsum-pairs

* Dataset: xsum-pairs
* Size: 128 evaluation samples
* Columns: <code>summary</code> and <code>document</code>
* Approximate statistics based on the first 1000 samples:
  |         | summary                                                                            | document                                                                             |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 13 tokens</li><li>mean: 25.59 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 70 tokens</li><li>mean: 217.06 tokens</li><li>max: 347 tokens</li></ul> |
* Samples:
  | summary                                                                                                                                                    | document                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>A charity which has supported fishermen for more than 100 years has sold its base in Cornwall.</code>                                                | <code>The Fishermen's Mission in Newlyn opened in 1903, but the charity said the building was no longer cost-effective to run.<br>It sold the centre to a local businessman, and said it hoped to use the proceeds to benefit the area.<br>The mission provides practical and spiritual support for fishermen and their families.<br>Julian Waring, from the charity, said that in the 1960s and 1970s there was a demand to house fishermen, feed them fresh meals and provide clothing in emergencies.<br>He said: "Those demands have changed. We're still here in Newlyn and a memorial room will remain, but there's no need for accommodation or a canteen. If accommodation is needed we'll house them in a B&B.<br>"To manage a building comes at a huge cost and if it's not used to its full potential it needs to be reassessed."<br>Mr Waring said 88p of every £1 donated to the charity was spent helping fishermen and the move would allow the charity to "better serve the county as a whole" rather than just Newlyn.<br>In 2014 the mission provided emergency grants of £500 to fishermen in Cornwall who were unable to work due to prolonged winter storms.<br>The mission has had a presence in Cornwall since 1896. It was initially based in Penzance and moved to Newlyn in 1903.</code> |
  | <code>The recovery in Scotland's labour market has continued into the second half of 2015, according to the latest Bank of Scotland report on jobs.</code> | <code>But, it said there were signs of the upturn slowing.<br>July saw a rise in demand for staff and an increase in average starting salaries, the report said.<br>However, in each case the rates of improvement were well below the highs reached one year ago.<br>The Bank of Scotland Labour Market Barometer for July was measured at 58.2.<br>That is well above the 50 "no-change" level, pointing to a further improvement in overall labour market conditions north of the border.<br>But, the latest reading was the lowest since May 2013, and well below last July's survey-record high of 67.3.<br>The equivalent UK index recorded a six-month low of 61.1 at the start of the third quarter.<br>Donald MacRae, chief Economist at Bank of Scotland, said: "Scotland's labour market continued to improve in July.<br>"The number of people appointed to both permanent and temporary jobs rose in the month but the number of vacancies for permanent jobs increased at the slowest pace in just over two years.<br>"Salary inflation remained solid although slowing to a five-month low.<br>"These results show an economy demonstrating both confidence and growth in the second half of 2015."<br>The report also said:</code>                                                                 |
  | <code>A man arrested after a man died in a shooting at a pool party in Surrey has been released on bail.</code>                                            | <code>Ricardo Hunter from Coulsdon in south London, died from a single gunshot wound at the private event in Headley, near Epsom.<br>Two others were injured in the shooting just after 02:30 BST on Monday.<br>The 38-year-old man from London, arrested on suspicion of murder and attempted murder, was bailed until 8 September, Surrey Police said.<br>A 36-year-old woman was shot in the leg and taken to hospital while another man was treated for minor shoulder wounds.<br>On Wednesday a woman, 30, arrested on suspicion of assisting an offender was released on conditional bail until September.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### sciq_pairs

* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 8 tokens</li><li>mean: 16.67 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 84.85 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence1                                                                        | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
  |:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Psip can be positive or negative relative to what kind of pressure?</code> | <code></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
  | <code>How many pathways do plants have for carbon fixation?</code>               | <code>Plants have evolved three pathways for carbon fixation. The most common pathway combines one molecule of CO 2 with a 5-carbon sugar called ribulose biphosphate (RuBP). The enzyme which catalyzes this reaction, ribulose-1,5-bisphosphate carboxylase oxygenase (nicknamed RuBisCo ), is the most abundant enzyme on earth! The resulting 6-carbon molecule is unstable, so it immediately splits into two much more stable 3-carbon phosphoglycerate molecules. The 3 carbons in the first stable molecule of this pathway give this largest group of plants the name “C-3. ”.</code> |
  | <code>What is the amount of force pushing against a given area?</code>           | <code>Pressure is defined as the amount of force pushing against a given area. How much pressure a gas exerts depends on the amount of gas. The more gas particles there are, the greater the pressure.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### qasc_pairs

* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 11.69 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 34.49 tokens</li><li>max: 59 tokens</li></ul> |
* Samples:
  | sentence1                                                                          | sentence2                                                                                                                                                                                                                 |
  |:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Where are the structures where proteins are made located on?</code>          | <code>Ribosomes are structures in the cytoplasm where proteins are made.. Rough endoplasmic reticulum has ribosomes attached d. <br> structures where proteins are made are located on rough endoplasmic reticulum</code> |
  | <code>What is something that can be used to impede electrical transference?</code> | <code>an electrical insulator slows the transfer of electricity. Sulfur is a good electrical insulator. <br> Sulfur slows the transfer of electricity.</code>                                                             |
  | <code>What are lakes formed by?</code>                                             | <code>lakes are formed by precipitation and runoff. Clouds form and precipitation occurs. <br> lakes are formed by clouds</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### openbookqa_pairs

* Dataset: openbookqa_pairs
* Size: 128 evaluation samples
* Columns: <code>question</code> and <code>fact</code>
* Approximate statistics based on the first 1000 samples:
  |         | question                                                                          | fact                                                                              |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 13.98 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.78 tokens</li><li>max: 28 tokens</li></ul> |
* Samples:
  | question                                                               | fact                                                                         |
  |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------|
  | <code>The thermal production of a stove is generically used for</code> | <code>a stove generates heat for cooking usually</code>                      |
  | <code>What creates a valley?</code>                                    | <code>a valley is formed by a river flowing</code>                           |
  | <code>when it turns day and night on a planet, what cause this?</code> | <code>a planet rotating causes cycles of day and night on that planet</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### msmarco_pairs

* Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 8.79 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 78.0 tokens</li><li>max: 179 tokens</li></ul> |
* Samples:
  | sentence1                                                                    | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
  |:-----------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>how much american dollars does it cost to join weight watchers?</code> | <code>Beyond that, many cable services offer free, on-demand workout videos, as do various websites, like YouTube. If you join a weight-loss program: If you opt for Weight Watchers, what you spend will depend on whether you're attending in-person meetings ($42.95 a month) or joining the organization online ($18.95 a month).</code>                                                                                                                                                                     |
  | <code>what causes the leaves of an oak tree to curl</code>                   | <code>A. Leaves of oak trees can be attacked by erophytid (pronounced Arrow-Fie-Tid) mites which cause a curling reaction. You can spray with diazinon insecticide or just let nature take its course. The curled leaves are still functional and will support tree growth. All leaves will not be affected. 2. Q. We have a spectacular oak tree in our yard that had an infestation of oak galls last year.</code>                                                                                             |
  | <code>what is tramadol hcl</code>                                            | <code>Tramadol hydrochloride is a centrally acting synthetic analgesic in an extended-release formulation. The chemical name is (±) cis-2-[(dimethylamino)methyl]­-1-(3-methoxyphenyl) cyclohexanol hydrochloride. Its structural formula is: The molecular weight of tramadol hydrochloride is 299.8. It is a white, bitter, crystalline and odorless powder that is readily soluble in water and ethanol and has a pKa of 9.41. The n-octanol/water log partition coefficient (logP) is 1.35 at pH 7.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### nq_pairs

* Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 10 tokens</li><li>mean: 11.39 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 41 tokens</li><li>mean: 146.72 tokens</li><li>max: 392 tokens</li></ul> |
* Samples:
  | sentence1                                                                                            | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
  |:-----------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>where does the queen mary 2 sail to</code>                                                     | <code>RMS Queen Mary 2 Like her predecessor Queen Elizabeth 2 she is built for crossing the Atlantic Ocean, though she is regularly used for cruising; in the winter season she cruises from New York to the Caribbean on twelve- or thirteen-day tours. Queen Mary 2's 30-knot (56 km/h; 35 mph) open ocean speed sets the ship apart from cruise ships, such as MS Oasis of the Seas, which has a service speed of 22.6 knots (41.9 km/h; 26.0 mph); QM2's normal service speed is 26 knots (48 km/h; 30 mph).[14] While the hull of a cruise ship will typically have a block coefficient of 0.73 (1.0 would represent a rectangular block) Queen Mary 2 is more fine-lined, with a block coefficient of 0.61.[15]</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
  | <code>who sings lean with it rock with it</code>                                                     | <code>Lean wit It, Rock wit It "Lean wit It, Rock wit It" is a song by Atlanta rap group Dem Franchize Boyz from their album On Top of Our Game. The recording features Peanut and Charlay and was produced by Maurice "Parlae" Gleaton.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
  | <code>which division of the nervous system consists of the brain spinal cord and optic nerves</code> | <code>Central nervous system The central nervous system (CNS) is the part of the nervous system consisting of the brain and spinal cord. The central nervous system is so named because it integrates information it receives from, and coordinates and influences the activity of, all parts of the bodies of bilaterally symmetric animals—that is, all multicellular animals except sponges and radially symmetric animals such as jellyfish—and it contains the majority of the nervous system. Many consider the retina[2] and the optic nerve (cranial nerve II),[3][4] as well as the olfactory nerves (cranial nerve I) and olfactory epithelium[5] as parts of the CNS, synapsing directly on brain tissue without intermediate ganglia. As such, the olfactory epithelium is the only central nervous tissue in direct contact with the environment, which opens up for therapeutic treatments. [5] The CNS is contained within the dorsal body cavity, with the brain housed in the cranial cavity and the spinal cord in the spinal canal. In vertebrates, the brain is protected by the skull, while the spinal cord is protected by the vertebrae.[6] The brain and spinal cord are both enclosed in the meninges.[6] In central nervous system, the interneuronal space is filled with large amount of supporting non nervous cells called neuroglial cells.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### trivia_pairs

* Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0)
* Size: 128 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                            | answer                                                                               |
  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                               |
  | details | <ul><li>min: 9 tokens</li><li>mean: 16.9 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 200.84 tokens</li><li>max: 379 tokens</li></ul> |
* Samples:
  | query                                                                                                                   | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
  |:------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>"Which actor spoke the line ""I love the smell of napalm in the morning* in the 1979 film Apocalypse Now?"</code> | <code>Smell of Napalm Scene - Apocalypse Now - YouTube Smell of Napalm Scene - Apocalypse Now Want to watch this again later? Sign in to add this video to a playlist. Need to report the video? Sign in to report inappropriate content. Rating is available when the video has been rented. This feature is not available right now. Please try again later. Uploaded on Nov 8, 2008 This is my favorite scene from one of my favorite movies, "Apocalypse Now" in which Kilgore talks about the smell of napalm. This scene gives birth to one of the most famous movie quotes of all time, "I love the smell of napalm in the morning." Lt. Col. Bill Kilgore is played by the actor Robert Duvall. Category</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
  | <code>After winning £152,000 in 1961, whose autobiography was entitled 'Spend, Spend, Spend'?</code>                    | <code>Viv Nicholson goes from 'spend, spend, spend' to 'sell, sell, sell': Sorry tale of pools winner who's auctioning off her memorabilia now she's in a care home | Daily Mail Online From 'spend, spend, spend' to 'sell, sell, sell': Sorry tale of pools winner who's auctioning off her memorabilia now she's in a care home Viv Nicholson, 77, won £152,000 in 1961 with her husband Keith She famously declared that she would 'spend, spend, spend' after the win But she soon lost all the money, which would be worth £2,870,000 today Mrs Nicholson, who now lives in a care home, will auction old possessions kept by her brother to raise money for charity The proceeds will be spent on buying iPads for the elderly</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  | <code>In which 'New England' state is Harvard University situated?</code>                                               | <code>What state are the New England Patriots from? | Reference.com What state are the New England Patriots from? A: Quick Answer The New England Patriots are from the state of Massachusetts. The team plays all of their home games at Gillette Stadium in Foxborough, Mass. Full Answer The team formed in 1959 and in 1960, the first head coach, Lou Saban, took his place with the team. Originally called the Boston Patriots, the team first played at Boston University Field and at Harvard University because they did not have a permanent home stadium. It was at Boston University Field that the Boston Patriots opened their first training camp on July 4, 1960. The team's first game, however, took place at Harvard University on August 14, 1960, where the team lost to the Dallas Texans. After the NFL and AFL merger in 1971, the Patriots took their place in the AFC East Division, and within a year, they had a new stadium in Foxborough called Foxboro Stadium. As they made the new stadium their home, they attempted to change their name to the Bay State Patriots. An overseeing NFL committee refused the name change, however. On March 23, 1981, the team officially became known as the New England Patriots. In 2002, Gillette Stadium was built and opened and it became the new home field for the team.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### gooaq_pairs

* Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 11.62 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 55.83 tokens</li><li>max: 107 tokens</li></ul> |
* Samples:
  | sentence1                                                     | sentence2                                                                                                                                                                                                                                                                                                                      |
  |:--------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>we are never ever getting back together country?</code> | <code>"We Are Never Ever Getting Back Together" is a song recorded by American singer-songwriter Taylor Swift for her fourth studio album, Red (2012). Swift co-wrote the song with its producers, Max Martin and Shellback. ... It also topped the US Hot Country Songs for ten weeks, Swift's longest reign to date.</code>  |
  | <code>can an optometrist treat lazy eye?</code>               | <code>Your eye doctor may recommend eye patch therapy in addition to corrective lenses. Strabismus surgery is usually required if the amblyopia is due to a large eye turn. This type of surgery aligns the eyes and corrects the problem within the eye muscles. After the surgery the eyes will able to focus better.</code> |
  | <code>ok google are the everly brothers still alive?</code>   | <code>Don Everly is now 80, while Phil Everly died in January 2014 at age 74.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### paws-pos

* Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 25.72 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 25.55 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                                      | sentence2                                                                                                                                                      |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>They were there to enjoy us and they were there to pray for us .</code>                                                                                  | <code>They were there for us to enjoy and they were there for us to pray .</code>                                                                              |
  | <code>After the end of the war in June 1902 , Higgins left Southampton in the `` SSBavarian '' in August , returning to Cape Town the following month .</code> | <code>In August , after the end of the war in June 1902 , Higgins Southampton left the `` SSBavarian '' and returned to Cape Town the following month .</code> |
  | <code>From the merger of the Four Rivers Council and the Audubon Council , the Shawnee Trails Council was born .</code>                                        | <code>Shawnee Trails Council was formed from the merger of the Four Rivers Council and the Audubon Council .</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

#### global_dataset

* Dataset: global_dataset
* Size: 663 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 31.01 tokens</li><li>max: 347 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 58.72 tokens</li><li>max: 392 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | sentence2                                                                                                                                        |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Seeds can be dispersed through all sorts of creative ways, such as</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | <code>seed dispersal is when the seeds of a plant are moved from the plant to a new environment</code>                                           |
  | <code>It was part of the Hanover Township , then Chatham Township , before being recorded in 1899 as Florham Park .</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>It was part of Hanover Township , then Chatham Township before being incorporated as Florham Park in 1899 .</code>                         |
  | <code>Pivac joined the Scarlets as assistant coach in July before Easterby announced his decision to move.<br>He takes over the head coach role with immediate effect, though former Ireland international Easterby will remain at the Scarlets until October.<br>"Joining the Scarlets was an exciting challenge for me," said Pivac.<br>"I am honoured that the Scarlets have the faith and belief in me to take the squad forward and build on the good work and solid foundations that Simon and his team have put in place."<br>Former Auckland coach Pivac said Easterby played a key role in his recruitment and he was also influenced by Llanelli's famous 9-3 win over the All Blacks in the 1970s.<br>"There were two factors why I chose Scarlets, the fact that Simon Easterby jumped on a plane and came to New Zealand rather than a lot of talks going on for a long period of time.<br>"[And] As a young boy growing up listening to the All Blacks play Llanelli in 1972 on the radio back home, I've never forgotten that moment.<br>"Knowing the Scarlets has a proud history, like the union I've come from, was important to me and coming to an area where they live and breathe rugby like home."<br>In addition to his role at Auckland, New Zealander Pivac coached Fiji to the Pacific Tri-Nations and was also coach of the side which won the 2005 Rugby World Cup Sevens.</code> | <code>Scarlets have confirmed Wayne Pivac will take over as head coach from Simon Easterby who has been appointed Ireland forwards coach.</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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 0.025}
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 96
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 2
- `learning_rate`: 4.5e-05
- `weight_decay`: 0.001
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 9e-06}
- `warmup_ratio`: 0.33
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa2-0.9B-ST-v2-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `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`: 96
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 4.5e-05
- `weight_decay`: 0.001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 9e-06}
- `warmup_ratio`: 0.33
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `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`: False
- `fp16`: True
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTa2-0.9B-ST-v2-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | negation-triplets loss | vitaminc-pairs loss | scitail-pairs-pos loss | sciq pairs loss | trivia pairs loss | xsum-pairs loss | openbookqa pairs loss | msmarco pairs loss | nq pairs loss | global dataset loss | paws-pos loss | scitail-pairs-qa loss | qasc pairs loss | gooaq pairs loss | Qnli-dev_max_ap | allNLI-dev_max_ap | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:----------------------:|:-------------------:|:----------------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:------------------:|:-------------:|:-------------------:|:-------------:|:---------------------:|:---------------:|:----------------:|:---------------:|:-----------------:|:------------------------:|
| 0.0129 | 11   | 0.1802        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.0257 | 22   | 0.1573        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.0386 | 33   | 0.1184        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.0515 | 44   | 0.1456        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.0643 | 55   | 0.2102        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.0772 | 66   | 0.151         | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.0901 | 77   | 0.1356        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.1029 | 88   | 0.2292        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.1158 | 99   | 0.126         | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.1287 | 110  | 0.1942        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.1415 | 121  | 0.2089        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.1544 | 132  | 0.1225        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.1673 | 143  | 0.2504        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.1801 | 154  | 0.1454        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.1930 | 165  | 0.2052        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.2058 | 176  | 0.1321        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.2187 | 187  | 0.1975        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.2316 | 198  | 0.1615        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.2444 | 209  | 0.2174        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.2503 | 214  | -             | 1.0606                 | 2.7611              | 0.0596                 | 0.0132          | 0.2116            | 0.0044          | 1.3197                | 0.1359             | 0.0413        | 0.2187              | 0.0459        | 0.0000                | 0.0490          | 0.1187           | 0.7328          | 0.6432            | 0.9079                   |
| 0.2573 | 220  | 0.1545        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.2702 | 231  | 0.1201        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.2830 | 242  | 0.1389        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.2959 | 253  | 0.1657        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.3088 | 264  | 0.237         | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.3216 | 275  | 0.1094        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.3345 | 286  | 0.196         | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.3474 | 297  | 0.2164        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.3602 | 308  | 0.1793        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.3731 | 319  | 0.2878        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.3860 | 330  | 0.1189        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.3988 | 341  | 0.1475        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.4117 | 352  | 0.1019        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.4246 | 363  | 0.1587        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.4374 | 374  | 0.2483        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.4503 | 385  | 0.1427        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.4632 | 396  | 0.1199        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.4760 | 407  | 0.2037        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.4889 | 418  | 0.1317        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.5006 | 428  | -             | 1.0632                 | 2.7318              | 0.0605                 | 0.0029          | 0.2094            | 0.0045          | 1.2716                | 0.1622             | 0.0400        | 0.2122              | 0.0461        | 0.0000                | 0.0601          | 0.1271           | 0.7294          | 0.6397            | 0.9063                   |
| 0.5018 | 429  | 0.1293        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.5146 | 440  | 0.1902        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.5275 | 451  | 0.1429        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.5404 | 462  | 0.2446        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.5532 | 473  | 0.1623        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.5661 | 484  | 0.0707        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.5789 | 495  | 0.1557        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.5918 | 506  | 0.2016        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.6047 | 517  | 0.1018        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.6175 | 528  | 0.1821        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.6304 | 539  | 0.1437        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.6433 | 550  | 0.1112        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.6561 | 561  | 0.12          | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.6690 | 572  | 0.0933        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.6819 | 583  | 0.0939        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.6947 | 594  | 0.2064        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.7076 | 605  | 0.131         | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.7205 | 616  | 0.161         | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.7333 | 627  | 0.213         | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.7462 | 638  | 0.1853        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.7509 | 642  | -             | 1.0684                 | 2.7074              | 0.0593                 | 0.0030          | 0.2113            | 0.0052          | 1.2897                | 0.1479             | 0.0423        | 0.2030              | 0.0460        | 0.0000                | 0.0563          | 0.1147           | 0.7257          | 0.6432            | 0.9073                   |
| 0.7591 | 649  | 0.1919        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.7719 | 660  | 0.1395        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.7848 | 671  | 0.2047        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.7977 | 682  | 0.1421        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.8105 | 693  | 0.1227        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.8234 | 704  | 0.1235        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.8363 | 715  | 0.2004        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.8491 | 726  | 0.1568        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.8620 | 737  | 0.1598        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.8749 | 748  | 0.1328        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.8877 | 759  | 0.0999        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.9006 | 770  | 0.1058        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.9135 | 781  | 0.1673        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.9263 | 792  | 0.1905        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.9392 | 803  | 0.1463        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.9520 | 814  | 0.1294        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.9649 | 825  | 0.1312        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.9778 | 836  | 0.1308        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 0.9906 | 847  | 0.1076        | -                      | -                   | -                      | -               | -                 | -               | -                     | -                  | -             | -                   | -             | -                     | -               | -                | -               | -                 | -                        |
| 1.0012 | 856  | -             | 1.0481                 | 2.6587              | 0.0596                 | 0.0031          | 0.2101            | 0.0056          | 1.2992                | 0.1593             | 0.0405        | 0.2027              | 0.0462        | 0.0000                | 0.0542          | 0.1140           | 0.7226          | 0.6377            | 0.9070                   |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.44.0
- PyTorch: 2.4.0
- Accelerate: 0.33.0
- Datasets: 2.21.0
- Tokenizers: 0.19.1

## 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",
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->