checkpoint-196/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:124849
- loss:CachedGISTEmbedLoss
widget:
- source_sentence: what is rice vermicelli
  sentences:
  - The blood plasma is filtered through the capillaries of the glomerulus into the
    capsule. The Bowman's capsule empties the filtrate into the proximal tubule that
    is also part of the nephron. A glomerulus receives its blood supply from an afferent
    arteriole of the renal circulation.lomerulus (kidney). From Wikipedia, the free
    encyclopedia. A glomerulus is a network (as a tuft) of capillaries located at
    the beginning of a nephron in the kidney. It serves as the first stage in the
    filtering process of the blood carried out by the nephron in its formation of
    urine.
  - 'Saturn''s Temperature: One Cool Planet. With an average temperature of minus
    288 degrees Fahrenheit (minus 178 degrees Celsius), Saturn is a pretty cool planet.
    Although there are some small differences as one travels from the equator to the
    poles, much of Saturn''s temperature variation is horizontal.'
  - Rice vermicelli are thin rice-flour noodles common in Chinese, Thai, Vietnamese,
    and other Southeast Asian cuisines, where they are often used in stir-fries, soups,
    spring rolls, and salads. Don't confuse rice vermicelli with cellophane noodles,
    another thin Asian noodle that's made with mung bean starch.
- 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: A stream flooding is most likely caused by a thunderstorm.
  sentences:
  - Which change is most likely caused by a thunderstorm?
  - What elements have the highest attraction for electrons?
  - An ether is an organic compound in which two hydrocarbon groups are bonded to
    the same atom of what?
- source_sentence: A(n) exoplanet is often discovered because it causes a star to
    move or to dim.
  sentences:
  - How many dimensions can humans see in?
  - What celestial body is often discovered because it causes a star to move or to
    dim?
  - The majority of solids are what in nature?
- source_sentence: A geis (plural geasa) is a curse or taboo in which national mythology/language?
  sentences:
  - 'BIRMINGHAM SNOW HILL STATION - RAILWAY STATION (PART-TIME STAFF) in Birmingham
    (City Centre), England Geographic Location: Lat: 52.48447;    Long: -1.9006 -
    GeoTag: GB-BIR  Birmingham Snow Hill Station  Birmingham Snow Hill Station is
    a  Railway Station managed by London Midland and is located in or near Birmingham
    (City Centre), England.   Mainland rail tickets from The Trainline.com You can
    obtain timetable information and ticket prices for trains from any UK mainland
    railway    station to any other UK mainland railway station. Just click the banner
    below for further details. Discount on-line train tickets'
  - Meaning of Geis Geis In Irish mythology and folklore, a geis (ʃ; s`; plural geasa)
    is an idiosyncratic taboo, whether of obligation or prohibition, similar to being
    under a vow or spell. The Scottish Gaelic spelling `geas` is also common. ==Geis
    in Irish Mythology== A geis can be compared with a curse or, paradoxically, a
    gift. If someone under a geis violates the......
  - John Constable - The complete works Contact Portrait John Constable John Constable
    (11 June 1776 - 31 March 1837) was an English Romantic painter. Born in Suffolk,
    he is known principally for his landscape paintings of Dedham Vale, the area surrounding
    his home - now known as "Constable Country"- which he invested with an intensity
    of affection. "I should paint my own places best", he wrote to his friend John
    Fisher in 1821, "painting is but another word for feeling". His most famous paintings
    include Dedham Vale of 1802 and The Hay Wain of 1821. Although his paintings are
    now among the most popular and valuable in British art, he was never financially
    successful and did not become a member of the establishment until he was elected
    to the Royal Academy at the age of 52. He sold more paintings in France than in
    his native England.
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: 0.8835670131068663
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9072833143432768
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9119339934951788
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.9080893293121973
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9109918303510968
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.9070972385984113
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8614909192586415
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8645679220863562
      name: Spearman Dot
    - type: pearson_max
      value: 0.9119339934951788
      name: Pearson Max
    - type: spearman_max
      value: 0.9080893293121973
      name: Spearman Max
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: allNLI dev
      type: allNLI-dev
    metrics:
    - type: cosine_accuracy
      value: 0.740234375
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7975447177886963
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.6323185011709601
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.6594541072845459
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.531496062992126
      name: Cosine Precision
    - type: cosine_recall
      value: 0.7803468208092486
      name: Cosine Recall
    - type: cosine_ap
      value: 0.6377387373098401
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.724609375
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 357.22686767578125
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.6168958742632614
      name: Dot F1
    - type: dot_f1_threshold
      value: 255.791015625
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.46726190476190477
      name: Dot Precision
    - type: dot_recall
      value: 0.9075144508670521
      name: Dot Recall
    - type: dot_ap
      value: 0.5991972031824899
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.7421875
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 304.86859130859375
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.6432160804020101
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 354.99481201171875
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.5688888888888889
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.7398843930635838
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.6414859221224841
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.751953125
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 13.69708251953125
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.6431924882629108
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 17.385541915893555
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.541501976284585
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.791907514450867
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.6435648687925655
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.751953125
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 357.22686767578125
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.6432160804020101
      name: Max F1
    - type: max_f1_threshold
      value: 354.99481201171875
      name: Max F1 Threshold
    - type: max_precision
      value: 0.5688888888888889
      name: Max Precision
    - type: max_recall
      value: 0.9075144508670521
      name: Max Recall
    - type: max_ap
      value: 0.6435648687925655
      name: Max Ap
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Qnli dev
      type: Qnli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.693359375
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.6972202062606812
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.6803418803418803
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.5867342948913574
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.5702005730659025
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8432203389830508
      name: Cosine Recall
    - type: cosine_ap
      value: 0.7153827512813624
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.66015625
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 334.04241943359375
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.6741935483870968
      name: Dot F1
    - type: dot_f1_threshold
      value: 255.34799194335938
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.5442708333333334
      name: Dot Precision
    - type: dot_recall
      value: 0.885593220338983
      name: Dot Recall
    - type: dot_ap
      value: 0.6681094375225486
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.705078125
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 351.4493103027344
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.6948853615520282
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 402.3414306640625
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.595166163141994
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.8347457627118644
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7282119097652248
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.70703125
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 16.576934814453125
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.68796992481203
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 18.22495460510254
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.6182432432432432
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.7754237288135594
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.7263623876399564
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.70703125
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 351.4493103027344
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.6948853615520282
      name: Max F1
    - type: max_f1_threshold
      value: 402.3414306640625
      name: Max F1 Threshold
    - type: max_precision
      value: 0.6182432432432432
      name: Max Precision
    - type: max_recall
      value: 0.885593220338983
      name: Max Recall
    - type: max_ap
      value: 0.7282119097652248
      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/DeBERTa-small-ST-v1-test-step3-ft-checkpoints-tmp")
# Run inference
sentences = [
    'A geis (plural geasa) is a curse or taboo in which national mythology/language?',
    'Meaning of Geis Geis In Irish mythology and folklore, a geis (ʃ; s`; plural geasa) is an idiosyncratic taboo, whether of obligation or prohibition, similar to being under a vow or spell. The Scottish Gaelic spelling `geas` is also common. ==Geis in Irish Mythology== A geis can be compared with a curse or, paradoxically, a gift. If someone under a geis violates the......',
    'John Constable - The complete works Contact Portrait John Constable John Constable (11 June 1776 - 31 March 1837) was an English Romantic painter. Born in Suffolk, he is known principally for his landscape paintings of Dedham Vale, the area surrounding his home - now known as "Constable Country"- which he invested with an intensity of affection. "I should paint my own places best", he wrote to his friend John Fisher in 1821, "painting is but another word for feeling". His most famous paintings include Dedham Vale of 1802 and The Hay Wain of 1821. Although his paintings are now among the most popular and valuable in British art, he was never financially successful and did not become a member of the establishment until he was elected to the Royal Academy at the age of 52. He sold more paintings in France than in his native England.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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### Out-of-Scope Use

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## Evaluation

### Metrics

#### 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      | 0.8836     |
| **spearman_cosine** | **0.9073** |
| pearson_manhattan   | 0.9119     |
| spearman_manhattan  | 0.9081     |
| pearson_euclidean   | 0.911      |
| spearman_euclidean  | 0.9071     |
| pearson_dot         | 0.8615     |
| spearman_dot        | 0.8646     |
| pearson_max         | 0.9119     |
| spearman_max        | 0.9081     |

#### 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              | 0.7402     |
| cosine_accuracy_threshold    | 0.7975     |
| cosine_f1                    | 0.6323     |
| cosine_f1_threshold          | 0.6595     |
| cosine_precision             | 0.5315     |
| cosine_recall                | 0.7803     |
| cosine_ap                    | 0.6377     |
| dot_accuracy                 | 0.7246     |
| dot_accuracy_threshold       | 357.2269   |
| dot_f1                       | 0.6169     |
| dot_f1_threshold             | 255.791    |
| dot_precision                | 0.4673     |
| dot_recall                   | 0.9075     |
| dot_ap                       | 0.5992     |
| manhattan_accuracy           | 0.7422     |
| manhattan_accuracy_threshold | 304.8686   |
| manhattan_f1                 | 0.6432     |
| manhattan_f1_threshold       | 354.9948   |
| manhattan_precision          | 0.5689     |
| manhattan_recall             | 0.7399     |
| manhattan_ap                 | 0.6415     |
| euclidean_accuracy           | 0.752      |
| euclidean_accuracy_threshold | 13.6971    |
| euclidean_f1                 | 0.6432     |
| euclidean_f1_threshold       | 17.3855    |
| euclidean_precision          | 0.5415     |
| euclidean_recall             | 0.7919     |
| euclidean_ap                 | 0.6436     |
| max_accuracy                 | 0.752      |
| max_accuracy_threshold       | 357.2269   |
| max_f1                       | 0.6432     |
| max_f1_threshold             | 354.9948   |
| max_precision                | 0.5689     |
| max_recall                   | 0.9075     |
| **max_ap**                   | **0.6436** |

#### 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              | 0.6934     |
| cosine_accuracy_threshold    | 0.6972     |
| cosine_f1                    | 0.6803     |
| cosine_f1_threshold          | 0.5867     |
| cosine_precision             | 0.5702     |
| cosine_recall                | 0.8432     |
| cosine_ap                    | 0.7154     |
| dot_accuracy                 | 0.6602     |
| dot_accuracy_threshold       | 334.0424   |
| dot_f1                       | 0.6742     |
| dot_f1_threshold             | 255.348    |
| dot_precision                | 0.5443     |
| dot_recall                   | 0.8856     |
| dot_ap                       | 0.6681     |
| manhattan_accuracy           | 0.7051     |
| manhattan_accuracy_threshold | 351.4493   |
| manhattan_f1                 | 0.6949     |
| manhattan_f1_threshold       | 402.3414   |
| manhattan_precision          | 0.5952     |
| manhattan_recall             | 0.8347     |
| manhattan_ap                 | 0.7282     |
| euclidean_accuracy           | 0.707      |
| euclidean_accuracy_threshold | 16.5769    |
| euclidean_f1                 | 0.688      |
| euclidean_f1_threshold       | 18.225     |
| euclidean_precision          | 0.6182     |
| euclidean_recall             | 0.7754     |
| euclidean_ap                 | 0.7264     |
| max_accuracy                 | 0.707      |
| max_accuracy_threshold       | 351.4493   |
| max_f1                       | 0.6949     |
| max_f1_threshold             | 402.3414   |
| max_precision                | 0.6182     |
| max_recall                   | 0.8856     |
| **max_ap**                   | **0.7282** |

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### Recommendations

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## Training Details

### Training Datasets

#### negation-triplets

* Dataset: negation-triplets
* Size: 6,000 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: 5 tokens</li><li>mean: 21.8 tokens</li><li>max: 114 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.11 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.42 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                              | entailment                                                                                                                         | negative                                                                                                                         |
  |:------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|
  | <code>People on the roof of a building look down on a crowded street.</code>                                                        | <code>People are outside.</code>                                                                                                   | <code>People are inside.</code>                                                                                                  |
  | <code>Mercedes-Benz is a brand of cars and trucks from the Daimler AG company of Germany .</code>                                   | <code>Mercedes-Benz is a division of its parent company , Daimler AG .</code>                                                      | <code>Mercedes-Benz is an independent company, not affiliated with any parent company.</code>                                    |
  | <code>Jonny Kennedy ( born 16 February 1990 ) is an English rugby union player for Sale Sharks in the Guinness Premiership .</code> | <code>Jonny Kennedy ( born 16 February 1990 in England ) is a rugby union player for Sale Sharks in the Aviva Premiership .</code> | <code>Jonny Kennedy ( born 16 February 1990 in Australia ) is a cricket player for Sale Sharks in the Aviva Premiership .</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: 6,000 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: 17.46 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 38.24 tokens</li><li>max: 182 tokens</li></ul> |
* Samples:
  | claim                                                                                               | evidence                                                                                                                                                                                                                          |
  |:----------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The film , Stuart Little , grossed over $ 455 billion worldwide</code>                        | <code>According to Box Office Mojo , its final gross in the United States and Canada was $ 140 million and it grossed $ 160.1 million at the international box office , for an estimated total of $ 455 billion worldwide.</code> |
  | <code>Under 1,460 of the Airbus A319ceo airliner had been purchased by the end of May 2017 .</code> | <code>A total of 1,457 of the A319ceo model have been delivered , with 28 remaining on order as of 31 May 2017 .</code>                                                                                                           |
  | <code>Frozen II earned over $ 1.033 billion worldwide .</code>                                      | <code>, Frozen II has grossed $ 366.4 million in the United States and Canada , and $ 667.2 million in other territories , for a worldwide total of $ 1.034 billion .</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: 6,000 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: 16.1 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.02 tokens</li><li>max: 34 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                 | sentence2                                                                                                                                      |
  |:------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The process for developing knowledge is called science.</code>                                                                      | <code>What is the process for developing knowledge called?</code>                                                                              |
  | <code>Due to the time it takes for light to reach us, galaxies at the edge of the universe are distant in terms of space and time.</code> | <code>Due to the time it takes for light to reach us, galaxies at the edge of the universe are distant in terms of space and what else?</code> |
  | <code>Present-day plants that have a complex vascular tissue system are called vasuluar plants.</code>                                    | <code>Present-day plants that have a complex vascular tissue system are called what kind of plants?</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: 5,160 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: 6 tokens</li><li>mean: 23.48 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.56 tokens</li><li>max: 39 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                             | sentence2                                                                                              |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|
  | <code>Its energy is in the form of kinetic energy, the energy of motion.</code>                                                                                                                       | <code>Kinetic engergy is the energy of anything in motion.</code>                                      |
  | <code>Exposure to radiation or cancer-causing chemicals may cause DNA  mutations in many organs, but these factors have not been proven to be important causes of mutations in prostate cells.</code> | <code>Cancer is caused by mutations, which can be caused by pathogens, chemicals, or radiation.</code> |
  | <code>One gram of protein provides four calories.</code>                                                                                                                                              | <code>One gram of proteins provides four calories of energy.</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: 6,000 training samples
* Columns: <code>document</code> and <code>summary</code>
* Approximate statistics based on the first 1000 samples:
  |         | document                                                                             | summary                                                                           |
  |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                            |
  | details | <ul><li>min: 46 tokens</li><li>mean: 217.44 tokens</li><li>max: 366 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 25.34 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
  | document                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | summary                                                                                                                                                        |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The body of a man who was aged in his 50s was discovered by a member of the public in Foresterhill Road at about 03:00.<br>Police are treating the death as "unexplained" and said inquiries were ongoing.  A stretch of the road was closed for investigations.<br>A body of a woman was also later found at the mouth of the River Don.<br>The man found in Foresterhill Road is thought to have walked down Foresterhill Road from the Anderson Drive area.<br>He was described as about 5ft 10in tall, of medium build, wearing a dark hooded jacket, a grey horizontal striped jumper, and dark blue jeans.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | <code>Police are investigating after the discovery of two bodies in separate incidents in Aberdeen.</code>                                                     |
  | <code>The full extent of the 25-year-old NFL star's injuries are unclear.<br>Newton was seen smiling before being placed into an ambulance in a neck brace after the accident in Charlotte, North Carolina.<br>The Panthers tweeted: "Cam is in fair condition and undergoing tests. He will remain in hospital for observation."<br>Police said a black pick-up truck driven by Newton had overturned, with debris scattered over the road surface near Carolina's Bank of America Stadium.<br>The roof of the truck was caved in and another car that appeared to be involved in the crash had front-end damage.<br>Newton was taken to the Carolinas Medical Center for treatment.<br>The Panther statement added: "Further testing revealed that Cam has two process fractures in his lower back and no further internal injuries."<br>Newton, the number one overall pick in the 2011 NFL draft, has missed only one game this season.<br>Newton has one year left on his rookie contract after the Panthers picked up a £9.6m ($15m) option for 2015.<br>The Panthers are third in the NFC South, having won four, lost eight and tied one. They are next in action against Tampa Bay on Sunday.</code> | <code>Carolina Panthers quarterback Cam Newton was taken to hospital with fractures to his lower back after a two-vehicle crash.</code>                        |
  | <code>The 37-year-old achieved the feat in his 224th Test innings, during his side's second Test in New Zealand.<br>India's Sachin Tendulkar and Australia's Ricky Ponting both needed 247 innings to surpass the mark.<br>Sangakkara's 33 not out helped his side to 78-5 after the Kiwis were all out for 221 on day one in Wellington.<br>New Zealand lead the series 1-0 after an eight-wicket win in Christchurch.<br>Sangakkara began his innings on 11,995 runs and was given a standing ovation when he became the first Sri Lankan to reach 12,000 by knocking Trent Boult square for two.<br>Tendulkar (15,921), Ponting (13,378), South African Jacques Kallis (13,289) and India's Rahul Dravid (13,288) are the only four players to have scored more Test runs than Sangakkara.</code>                                                                                                                                                                                                                                                                                                                                                                                                          | <code>Sri Lanka batsman Kumar Sangakkara has become the fastest player to reach 12,000 runs in Test cricket - and only the fifth to reach the landmark.</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: 6,000 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: 16.55 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 84.41 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                     | sentence2                                                                                                                                                                                                                                                                            |
  |:--------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Shivering helps the body return to a stable what?</code>                                                | <code>These people may be having fun in the icy water, but their bodies are struggling to react to the cold. For example, they may begin to shiver. Shivering helps the body return to a stable temperature. The body is always working to achieve stability, or homeostasis.</code> |
  | <code>The major cause of outdoor air pollution is the burning of?</code>                                      | <code>The major cause of outdoor air pollution is the burning of fossil fuels.</code>                                                                                                                                                                                                |
  | <code>Resonance has been used in what kind of human-made recreational objects since prehistoric times?</code> | <code>Figure 17.33 Resonance has been used in musical instruments since prehistoric times. This marimba uses gourds as resonance chambers to amplify its sound. (credit: APC Events, Flickr).</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: 4,636 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: 11.46 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 33.87 tokens</li><li>max: 66 tokens</li></ul> |
* Samples:
  | sentence1                                                                            | sentence2                                                                                                                                                                                                                                                                                               |
  |:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>If what are found in permafrost, that plant used to live in that place?</code> | <code>if fossils of an animal or plant are found in a place then that animal or plant used to live in that place. Frozen fossil animals are found in permafrost. <br> If frozen fossils are found in permafrost, that plant used to live in that place</code>                                           |
  | <code>What has electrical current running through it when working properly?</code>   | <code>when an electrical circuit is working properly , electrical current runs through that circuit. Much of the circuit is in fact a comparator made with discrete transistors. <br> When a comparator made with discrete transistors is working properly , electrical current runs through it.</code> |
  | <code>what can be used to feel heat and pressure on the skin?</code>                 | <code>nerves can be used to feel heat and pressure on the skin. Sensory neurons run anteriorly from the nerve ring to the sensory organs in the head. <br> sensory neurons can be used to feel heat and pressure on the skin</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: 2,713 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: 13.89 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.4 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
  | question                                                                                                                                             | fact                                                         |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|
  | <code>An electron microscope can look at</code>                                                                                                      | <code>an electron microscope creates a picture</code>        |
  | <code>Why would it be summer one day and fall another day?</code>                                                                                    | <code>Earth orbiting the Sun causes seasons to change</code> |
  | <code>After a long winter's hibernation, a female bear and her two cubs exit their cave and begin to search for food. One place they check is</code> | <code>bears eat berries</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: 6,000 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: 8.57 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 77.68 tokens</li><li>max: 208 tokens</li></ul> |
* Samples:
  | sentence1                                                | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
  |:---------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>how much does it cost to do house extension</code> | <code>A same level house extension in Australia will cost approximately $60,000 to $200,000+. Adding a room or extending your living area on the ground floor are affordable ways of creating more space.Here are some key points to consider that will help you keep your renovation costs in check.enovation costs for a same level extension are roughly between $60,000 and $200,000 depending on your situation, including material, labour and project management (ready to move in).</code> |
  | <code>school didn't do  moment of silence</code>         | <code>In Wallace v. Jaffree the Supreme Court struck down an Alabama moment of silence law because it was enacted for the express purpose of promoting prayer in public schools.2 At the same time, however, the Court indicated that a moment of silence would be constitutional if it is genuinely neutral.</code>                                                                                                                                                                               |
  | <code>what is aboriginal peoples of canada</code>        | <code>Canada's Multicultural First Peoples. Today Canada is the most multi-cultural country in the world, and the home of immigrants of every ethnic and religious group from every country in the world. But less than 500 years ago, the only people living in Canada were the Aboriginal people of Canada. Aboriginal means the original inhabitants, the people who were here first.</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: 6,000 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: 11.73 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 134.76 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence1                                                                        | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
  |:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>who was irving berlin and what did he do</code>                            | <code>Irving Berlin Irving Berlin (born Israel Beilin; May 11, 1888 – September 22, 1989) was an American composer and lyricist, widely considered one of the greatest songwriters in American history. His music forms a great part of the Great American Songbook. Born in Imperial Russia, Berlin arrived in the United States at the age of five. He published his first song, "Marie from Sunny Italy", in 1907, receiving 33 cents for the publishing rights,[3] and had his first major international hit, "Alexander's Ragtime Band" in 1911. He also was an owner of the Music Box Theatre on Broadway.</code>                                             |
  | <code>which of the following best describes the court of criminal appeals</code> | <code>Supreme Court of the United Kingdom The Supreme Court's focus is on cases that raise points of law of general public importance. As with the former Appellate Committee of the House of Lords, appeals from many fields of law are likely to be selected for hearing, including commercial disputes, family matters, judicial review claims against public authorities and issues under the Human Rights Act 1998. The Supreme Court also hears some criminal appeals, but not from Scotland, as there is no general right of appeal from the High Court of Justiciary, Scotland's highest criminal court, other than with respect to devolution issues.</code> |
  | <code>how many always sunny in philadelphia seasons are there</code>             | <code>It's Always Sunny in Philadelphia The series was renewed for a twelfth season that premiered on January 4, 2017.[2][3] On April 1, 2016, the series was renewed for a thirteenth and fourteenth season, which will tie it with The Adventures of Ozzie and Harriet as the longest running live-action sitcom in American TV history.[4]</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: 6,000 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: 8 tokens</li><li>mean: 17.26 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 208.22 tokens</li><li>max: 484 tokens</li></ul> |
* Samples:
  | query                                                                                       | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
  |:--------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>In which European country is the Caledonian Canal?</code>                             | <code>UK Boating Holidays and Canal Boat Vacations Cruise the UK with Boating Holidays Boating Holidays has the widest range of holidays afloat in the UK. We offer over 1500 boating holiday options - almost all with online availability. We have the biggest choice of self-drive cruisers, narrowboats, and wide-beam narrowboats on the rivers and canals of England, Wales and Scotland. We also offer skippered hotel barges. Enjoy a canal cruise through scenic countryside Locks take your boat up and down hills Explore the Norfolk Broads by boat Moor at a canalside pub See the Falkirk Wheel - a new waterways wonder Cruise along the River Thames Travel through the centre of cities See the setting sun from a peaceful canal Have an energetic day going up or down the Caen Hill locks Tunnels are an exciting part of canal cruises Take your floating hotel to central London Lift bridges are a feature of some canal routes Descend a staircase set of locks Cruise amongst Highland scenery on the Caledonian Canal Sail across valleys on an aqueduct</code>                                              |
  | <code>'Metis', 'Callisto', and 'Sinope' are three of the satellites of which planet?</code> | <code>Jupiter Images Menu Shoemaker - Levy 9 Impacts Jupiter MENU Jupiter's moon Io has many active volcanoes. It's surface is one of the youngest in the Solar System (it rivals only the Earth). This picture taken by Voyager 2 in July 1979, shows two color-enhanced (blue) volcanic plumes erupting to heights of 100 km above the surface. Io is about the same size as Earth's Moon. (Courtesy NASA/JPL) Jupiter on February 13, 1979. Voyager 1 was approaching the giant planet and captured this view showing the Great Red Spot, the satellite Io (orange moon on left), and Europa (brighter moon on right). (Courtesy NASA/JPL). Callisto seen by Voyager 1 in March 1979. Callisto is about the size of the planet Mercury . It is a heavily-cratered moon, which contrasts starkly with Io. Io's surface is as young as Callisto's is old. Callisto probably consists of a mixture of ice and rock. When the Galileo spacecraft begins studying Jupiter's system in 1995-1997, some parts of Callisto are planned to be imaged at much higher resolution (greater detail) than was available from the Voyagers.</code> |
  | <code>What was the subtitle of Terminator 2?</code>                                         | <code>Terminator 2: Judgment Day subtitles | 498 subtitles Movie ID:   Watch movies with subtitles using Open Subtitles MKV Player . It is free and clean, works under Windows. install opensearch for browser Results 1 - 40 >> of 498 (0.136 seconds) You can click on table header to sort results by column name Terminator 2: Judgment Day subtitles Buy at Amazon Movie details "It's nothing personal.". Nearly 10 years have passed since Sarah Connor was targeted for termination by a cyborg from the future. Now her son, John, the future leader of the resistance, is the target for a newer, more deadly terminator. Once again, the resistance has managed to send a protector back to attempt to save John and his mother Sarah. Movie rating: 8.5 / 10 (759156)</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: 6,000 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: 11.55 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 58.2 tokens</li><li>max: 129 tokens</li></ul> |
* Samples:
  | sentence1                                               | sentence2                                                                                                                                                                                                                                                                                                                                |
  |:--------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>how to connect bluetooth on iphone x?</code>      | <code>['From the Home screen, tap Settings > Bluetooth.', 'Tap the Bluetooth slider to ON.', 'Place your Bluetooth device in discovery mode.', 'Tap the Bluetooth device on the list.', 'Tap Pair.', 'Enter the PIN or Passkey if prompted, then tap OK,', 'On the other device, accept the pairing if prompted.']</code>                |
  | <code>how long does it take to charge dead ipad?</code> | <code>Plug It In, Let It Charge — And Wait Connect your iPhone or iPad to a wall charger and let it charge for a little while — give it fifteen minutes, perhaps. If the battery is completely dead, you can't just plug it in and expect it to respond immediately.</code>                                                              |
  | <code>2020 can am x3 improvements?</code>               | <code>front / 11-in. rear), improving rear traction, reduced steering kickback and enriching drivability. The 2020 Can-Am Maverick X3 lineup also includes repricing to increases accessibility without compromising on family DNA or overall quality. With expanded segment growth in mind, Can-Am has unveiled the base, 120hp.</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: 6,000 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: 25.35 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 25.35 tokens</li><li>max: 68 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                            | sentence2                                                                                                                                                                                                 |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Cashel is a village in County Galway , in the province of Connacht , Ireland .</code>                                                                                                          | <code>Cashel is a village in County Galway , Connacht province of Ireland .</code>                                                                                                                        |
  | <code>In March 2017 , Murray McCully criticized former Foreign Minister Peters for endorsing United Nations Security Council Resolution 2334 without consulting his other cabinet ministers .</code> | <code>In March 2017 , Murray McCully criticized the former Foreign Minister Peters for endorsing United Nations Security Council Resolution 2334 without consulting his fellow Cabinet ministers .</code> |
  | <code>The new grammar school became the Upper School , while the old building became the Lower School .</code>                                                                                       | <code>The new high school became the Upper School , while the old building became the Lower School .</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: 52,340 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: 5 tokens</li><li>mean: 34.1 tokens</li><li>max: 343 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 57.42 tokens</li><li>max: 401 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | sentence2                                                                                                                                                     |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The 31-year-old was placed on report by referee Richard Silverwood after catching Giants full-back Scott Grix late during the 24-8 loss on Friday.<br>But the Rugby Football League's review panel judged that the initial contact was with Grix's shoulder.<br>Bowen can now face Sydney Roosters in Sydney on Saturday, 22 February.<br>The former Australia international was making his Super League debut for the Warriors following his winter move from NRL side North Queensland Cowboys.<br>Champions Wigan have taken a squad of 27 players down under and will begin their preparations for the World Club Challenge with a warm-up fixture against New Zealand Warriors on Wednesday.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>Matty Bowen is free to play for Wigan in the World Club Challenge after escaping punishment for a high hit in the defeat by Huddersfield Giants.</code> |
  | <code>The song Firework had over 630 million views by October 2015 .</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | <code>It premiered on October 28 , 2010 on YouTube , and as of October 2015 has amassed more than 630 million views .</code>                                  |
  | <code>John Radcliffe left the bulk of his fortune to charitable causes on his death 300 years ago in 1714.<br>This funded the Radcliffe Camera, which was Britain's first circular library, the Radcliffe Observatory and the Radcliffe Infirmary.<br>Remembering Radcliffe: 300 years of science and philanthropy is at the Bodleian Library until March.<br>"This is a wonderful opportunity for people to learn more about this remarkable physician and philanthropist," said librarian Richard Ovenden.<br>"John Radcliffe's legacy lives on today - not only in Oxford's stunning buildings but through his legacy's investment in scientific research and its support for UK heritage and crafts and classical music performance and composition through the Radcliffe Trust."<br>The exhibition, which is free to attend, includes engravings, watercolours and architectural drawings.<br>Highlights include a 3D scale model of the Radcliffe Camera from 1735, rare and first edition books from the first collection of books housed in the Radcliffe Camera and items from Radcliffe's medical career.<br>"The exhibition explains how an 18th Century doctor became one of Oxford's greatest benefactors," said curator Stephen Hebron.<br>"Visitors can discover the story behind one of Oxford's most famous buildings, the Radcliffe Camera, including its origins, its design, how it was built, and its role as a university library."</code> | <code>The legacy of a physician who gave Oxford some of its landmark buildings is being explored in a new exhibition.</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: 14.29 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.37 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.62 tokens</li><li>max: 21 tokens</li></ul> |
* Samples:
  | anchor                                                                              | entailment                                                             | negative                                                                   |
  |:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:---------------------------------------------------------------------------|
  | <code>Posted signs point the way through a parking garage.</code>                   | <code>Three traffic signs at the entrance of a parking garage. </code> | <code>Three traffic signs not at the entrance of a parking garage. </code> |
  | <code>Two women walk down a sidewalk along a busy street in a downtown area.</code> | <code>The women were walking downtown</code>                           | <code>The women were not walking downtown</code>                           |
  | <code>The kitchen has many grill with pots hanging all around.</code>               | <code>A kitchen with a large stove and hanging pots.</code>            | <code>A kitchen with a small stove and hanging pots.</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: 15.77 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 14.71 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
  | sentence1                                                                                   | sentence2                                                                                        |
  |:--------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
  | <code>Ovulation occurs before the endometrium thickens in estrous cycles.</code>            | <code>What occurs before the endometrium thickens in estrous cycles?</code>                      |
  | <code>A(n) exoplanet is often discovered because it causes a star to move or to dim.</code> | <code>What celestial body is often discovered because it causes a star to move or to dim?</code> |
  | <code>Glaciers deposit their sediment when they melt.</code>                                | <code>When do glaciers deposit their sediment?</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>document</code> and <code>summary</code>
* Approximate statistics based on the first 1000 samples:
  |         | document                                                                             | summary                                                                            |
  |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                             |
  | details | <ul><li>min: 45 tokens</li><li>mean: 223.34 tokens</li><li>max: 339 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 24.97 tokens</li><li>max: 37 tokens</li></ul> |
* Samples:
  | document                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | summary                                                                                                                                              |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>However, North Yorkshire Police said it had passed a "piece of intelligence" to Wiltshire Police, the force leading inquiries into the former prime minister.<br>The force added the intelligence was "not connected to the North Yorkshire area".<br>Wiltshire Police said it "wouldn't discuss any intelligence passed to us".<br>North Yorkshire Police launched the search of its records earlier this month following allegations of child sex abuse levelled against Sir Edward, who was Conservative prime minister between 1970 and 1974.<br>It conducted the investigation after a photograph emerged of the late politician meeting ex-Scarborough mayor and suspected paedophile Peter Jaconelli, who was a close friend of Jimmy Savile.<br>Last year North Yorkshire Police said opportunities to prosecute Savile and Jaconelli over claims of historical sex abuse of children in the resort had been missed.<br>Savile died in 2011 and Jaconelli, who ran an ice cream company in the resort, died in 1999.<br>In a statement, North Yorkshire Police said: "Following a comprehensive search of force systems, North Yorkshire Police can confirm that nothing was found to designate Edward Heath as either a suspect or a victim of crime in North Yorkshire."<br>Former friends and colleagues of Sir Edward have rallied to defend his reputation in the wake of the allegations.<br>The former MP died at his home in Salisbury aged 89 in July 2005.</code> | <code>A police force has said it has found no evidence to link Sir Edward Heath to a crime in its area.</code>                                       |
  | <code>The Argentine striker spent pre-season searching in vain for a new club and has been replaced as skipper by defender Vincent Kompany.<br>Mancini said: "Carlos wanted to leave for family reasons. I respected his opinion but Carlos is still here because we didn't find a solution.<br>"I decided in the summer that Vinnie [Kompany] was the captain."<br>Tevez has made one substitute appearance for City this season, replacing compatriot Sergio Aguero in the 3-2 win at Bolton on 21 August.<br>He has been named in the squad to face Wigan on Saturday, but will not now be captain of the team.<br>Belgium international Kompany joined City from Hamburg in 2008 and made 53 appearances in all competitions last season. He also won the supporters' Player of the Year award as well as the players' Player of the Year honour.<br>Mancini added that Tevez would still be a key part of the squad in the coming season, as City make their debut in the Champions League. He said: "He's a fantastic player for us, someone who can score 20 goals a season."<br>Tevez spoke recently of his "love-hate" relationship with Mancini, and for much of the summer he was linked with a move away from Etihad Stadium.<br>Kompany has led the side in the three Premier League matches so far this season - City have won all of them as they seek a first championship title since 1968.</code>                                                                       | <code>Carlos Tevez has been permanently stripped of the Manchester City captaincy by manager Roberto Mancini.</code>                                 |
  | <code>The event, which has been organised by the All Under One Banner group, set off from the Botanic Gardens.<br>It was followed by a rally in the city's George Square attended by thousands of people.<br>Police Scotland said about 2,500 to 3,000 people had gathered in George Square.<br>Organisers however claimed that more than 5,000 people had taken part in the march and rally.<br>One of the organisers of the event, John McHarg, said the march had been inspired by recent events such as the Brexit vote and the decision at Westminster, opposed by SNP MPs, to renew the Trident submarines carrying the UK's nuclear arsenal.<br>He said: "Scotland did not vote to leave Europe, Scotland did not vote to retain Trident, and we want to show the powers that be that we won't tolerate this.<br>"Independence now for us is very much back on the agenda."<br>Supporters of the Union have played down the significance of the march.<br>Glasgow Conservative MSP Annie Wells said: "At the end of the day, the democracy that really matters is the vote that we cast less than two years ago to say that we wanted to remain part of the United Kingdom.<br>"We need to get out the back of referendums and actually get on with doing the day job."</code>                                                                                                                                                                                                     | <code>Pro-independence campaigners have marched through Glasgow in what organisers describe as a "vibrant and creative people powered event".</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: 7 tokens</li><li>mean: 16.56 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 79.07 tokens</li><li>max: 354 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                            | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
  |:-------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>An inclined plane is a good example of what kind of machine?</code>                                                            | <code>An inclined plane is also a simple machine. The resistance is the weight of the box resting on the inclined plane. In order to lift this box straight up, the effort force would need to be equal to its weight. However, assuming no friction, less effort (a smaller effort force) is required to slide the box up the incline. We know this intuitively; when movng boxes into a truck or onto a platform, we use angled platforms instead of lifting it straight up.</code> |
  | <code>What's the term for a piece of an organism breaking off and forming a new organism?</code>                                     | <code>Fragmentation occurs when a piece breaks off from a parent organism. Then the piece develops into a new organism. Sea stars, like the one in Figure below , can reproduce this way. In fact, a new sea star can form from a single “arm. ”.</code>                                                                                                                                                                                                                              |
  | <code>According to the table, aluminum has an atomic number of 13. therefore, every aluminum atom has 13 protons in its what?</code> | <code>According to the table, aluminum has an atomic number of 13. Therefore, every aluminum atom has 13 protons in its nucleus. Iron has an atomic number of 26. Therefore, every iron atom has 26 protons in its nucleus. Carbon has an atomic number of 6. Therefore, every carbon atom has 6 protons in its nucleus. 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: 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: 5 tokens</li><li>mean: 11.39 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 32.85 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
  | sentence1                                                                    | sentence2                                                                                                                                                                                                                                 |
  |:-----------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What by predators cause porcupines to extend their quills?</code>      | <code>threatening behavior by predators causes a porcupine to extend its quills. Aggression is active, harmful and offensive, threatens all and benefits none. <br> Aggression by predators causes porcupine to extend its quills.</code> |
  | <code>What are born with fur that grows thicker for warmth in winter?</code> | <code>some animals' fur grows thicker for staying warm in the winter. Mammals are animals that are born with fur or hair. <br> Mammals are born with fur that grows thicker for warmth in winter.</code>                                  |
  | <code>What cannot reproduce by itself?</code>                                | <code>Viruses cannot reproduce by themselves.. Influenza Virus Influenza viruses make up the family Orthomyxoviridae. <br> Influenza cannot reproduce by itself</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: 8.55 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 75.18 tokens</li><li>max: 167 tokens</li></ul> |
* Samples:
  | sentence1                                          | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  |:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what is vaporizing rub?</code>               | <code>Vapor rub, also referred to as chest rub or mentholated rub, is a petroleum-based topical gel that is primarily used to treat symptoms of the common cold such as congestion and cough. While it was originally marketed to treat respiratory illnesses, a variety of additional uses have been found for this product.</code>                                                                                                                                                                                                                                                                                                                     |
  | <code>were houdini and doyle really friends</code> | <code>Conan Doyle and Houdini first met in 1920, during the magician's tour of England. The two of them became good friends, despite their opposing views on the supernatural. Houdini was delighted to learn that there was at least one intelligent person who believed in Spiritualism and found that man in his friend Conan Doyle.ome believed they had an explanation for this -- they stated that Houdini s exposure of mediums was simply to cover the fact that he was a medium himself! They claimed that many of his extraordinary escapes were actually done by Houdini dematerializing from the traps that he had placed himself in.</code> |
  | <code>who played merle on walking dead</code>      | <code>'Walking Dead': Michael Rooker talks about the demise of Merle Dixon and life as zombie Merle.</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: 11.83 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 127.77 tokens</li><li>max: 392 tokens</li></ul> |
* Samples:
  | sentence1                                               | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
  |:--------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>who plays regina mills in once upon a time</code> | <code>Lana Parrilla Lana Maria Parrilla (born July 15, 1977) is an American actress. Parrilla is best known for her roles on television and radio. She was a regular cast member in the fifth season of the ABC sitcom Spin City from 2000 to 2001. She later starred in Boomtown (2002-2003), Windfall (2006), Swingtown (2008) and as Doctor Eva Zambrano in the short-lived medical drama Miami Medical (2010). She also played the role of Sarah Gavin during the fourth season of the Fox series 24 in 2005. In 2011, Parrilla began starring as The Evil Queen/Regina Mills in the ABC fantasy drama series, Once Upon a Time. In 2016 Parrilla won a Teen Choice Award for Choice Sci-Fi/Fantasy TV Actress.</code> |
  | <code>who played jason on wanted dead or alive</code>   | <code>Wanted Dead or Alive (TV series) Several episodes in 1960 included a sidekick named Jason Nichols (Wright King), a deputy sheriff turned bounty hunter. He and Randall worked well together on-screen, sharing a chemistry audiences enjoyed. By the start of the third season, Nichols had been dropped. The episode called "The Partners", where Nichols killed three men that Randall felt could have been taken alive, is often considered the episode that broke up the partnership, although that was actually only the second episode with Wright King and long before the last episode he appeared in.[5]</code>                                                                                             |
  | <code>who wrote i don't wanna talk about it</code>      | <code>I Don't Want to Talk About It "I Don't Want to Talk About It" is a song written by Danny Whitten. It was first recorded by Crazy Horse and issued as the final track on side one of their 1971 eponymous album. It was Whitten's signature tune, but gained more fame via its numerous cover versions, especially that by Rod Stewart.</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: 8 tokens</li><li>mean: 17.24 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 206.21 tokens</li><li>max: 389 tokens</li></ul> |
* Samples:
  | query                                                                                                              | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
  |:-------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Which Scottish artist painted the picture 'The Singing Butler'?</code>                                       | <code>Vettriano's Singing Butler in rare exhibition - BBC News BBC News Vettriano's Singing Butler in rare exhibition 4 February 2012 Close share panel Image caption Vettriano's artwork became the most expensive painting by a Scottish artist in 2004 One of the most famous paintings by Fife artist Jack Vettriano has gone on public display for the first time in two decades. The Singing Butler can be viewed at Aberdeen Art Gallery. It is part of an exhibition entitled "From Van Gogh to Vettriano - Hidden Gems from Private Collections". Vettriano's artwork became the most expensive painting by a Scottish artist when it sold for £750,000 eight years ago. The exhibition ends in April. Other artists featured in the exhibition include Monet, Matisse and Lucian Freud and Scottish colourists Peploe and Fergusson. The Singing Butler, which shows a couple dancing on a beach while a butler holds an umbrella over them, has been loaned to the gallery for the exhibition by a private collector in Scotland. Sixty-year-old Vettriano grew up in Methil in Fife. He is reported to sell more posters and postcards than any other artist in the UK. The original canvas of The Singing Butler sold at auction in 2004 for £744,500. In 1992 when Vettriano painted the picture and submitted it for inclusion in the Royal Academy summer show, it was rejected.</code> |
  | <code>A geis (plural geasa) is a curse or taboo in which national mythology/language?</code>                       | <code>Meaning of Geis Geis In Irish mythology and folklore, a geis (ʃ; s`; plural geasa) is an idiosyncratic taboo, whether of obligation or prohibition, similar to being under a vow or spell. The Scottish Gaelic spelling `geas` is also common. ==Geis in Irish Mythology== A geis can be compared with a curse or, paradoxically, a gift. If someone under a geis violates the......</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
  | <code>Which English author wrote The Kate Hannigan series, The Mallen novels and The Tilly Trotter trilogy?</code> | <code>Order of Catherine Cookson Books - OrderOfBooks.com Home | Characters | Authors Order of Catherine Cookson Books Catherine Cookson (1906-1998) was an English author of historical fiction novels and the author of the Mary Ann Shaughnessy series. She grew up the illegitimate daughter of an impoverished woman named Kate, whom she was led to believe was her older sister. Catherine was known as a regional writer, writing specifically for readers from the North of England. Of all of her many accolades, undoubtedly the greatest of all of them is being named a Dame of the British Empire in 1993. Catherine Cookson became a published novelist in 1950 with the novel Kate Hannigan. She continued to write up until her death and her final work was published in 2002. Below is a list of Catherine Cookson’s books in order of when they were first released: Publication Order of Kate Hannigan Books Kate Hannigan</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: 11.44 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 56.05 tokens</li><li>max: 114 tokens</li></ul> |
* Samples:
  | sentence1                                                                                 | sentence2                                                                                                                                                                                                                                                                                                    |
  |:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what stocks are shorted the most?</code>                                            | <code>['Tesla (ticker: TSLA)', 'Apple (AAPL)', 'Amazon.com (AMZN)', 'Microsoft Corp. (MSFT)', 'Alibaba Group Holding (BABA)']</code>                                                                                                                                                                         |
  | <code>how to tell the difference between male and female red eared slider turtles?</code> | <code>Male red-ear sliders have a much longer tail than females, and the cloaca is positioned lower on the tail. In females, the cloaca is close to the body, while males have a cloaca located almost at the tip of the tail.</code>                                                                        |
  | <code>is nied an intentional tort?</code>                                                 | <code>In tort law, the causation of severe emotional distress through negligent action. Abbreviated as NIED. Plaintiffs suing for NIED must have experienced contact as a result of defendant's negligence, or at least been in the zone of danger. See Intentional infliction of emotional distress.</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: 546 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: 5 tokens</li><li>mean: 31.63 tokens</li><li>max: 330 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 55.89 tokens</li><li>max: 392 tokens</li></ul> |
* Samples:
  | sentence1                                              | sentence2                                                                                                                                                                                                                                                                                                                                                            |
  |:-------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>can dht be washed away?</code>                   | <code>DHT Blocking Shampoos A good quality DHT-blocking shampoo will help to gently lift DHT from your scalp. Check the ingredients label and look out for key natural DHT-blocking nutrients including zinc which helps to stop the build up of DHT and Saw Palmetto, which also assists in slowing down DHT production.</code>                                     |
  | <code>at what temperature does grass go dormant</code> | <code>St Augustine grass will go dormant when the soil temperatures fall below 55 degrees. If the soil remains warmer than 60 degrees all year, the grass will stay green. It will, however, slow in growth as the soil drops in temperature. Disadvantages of St Augustine grass. One of the disadvantages of St Augustine grass is its poor wear tolerance.</code> |
  | <code>where are beko products manufactured?</code>     | <code>Beko plc is the UK and Ireland subsidiary of Arçelik A. Ş. Established in 1955, Arçelik A. Ş is one of the largest household appliance producers in Europe with a global workforce of over 30,000 people in more than 30 countries, distributing products and services to 146 countries.</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`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 3e-05
- `weight_decay`: 0.0005
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3e-06}
- `warmup_ratio`: 0.33
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa-small-ST-v1-test-step3-ft-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`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0005
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3e-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/DeBERTa-small-ST-v1-test-step3-ft-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
<details><summary>Click to expand</summary>

| Epoch  | Step | Training Loss | qasc pairs loss | msmarco pairs loss | trivia pairs loss | negation-triplets loss | nq pairs loss | vitaminc-pairs loss | scitail-pairs-qa loss | sciq pairs loss | paws-pos loss | scitail-pairs-pos loss | openbookqa pairs loss | gooaq pairs loss | xsum-pairs loss | global dataset loss | Qnli-dev_max_ap | allNLI-dev_max_ap | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:------------------:|:-----------------:|:----------------------:|:-------------:|:-------------------:|:---------------------:|:---------------:|:-------------:|:----------------------:|:---------------------:|:----------------:|:---------------:|:-------------------:|:---------------:|:-----------------:|:------------------------:|
| 0.0010 | 1    | 0.0203        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0020 | 2    | 0.1339        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0031 | 3    | 0.1229        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0041 | 4    | 0.0947        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0051 | 5    | 0.1468        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0061 | 6    | 0.2783        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0072 | 7    | 0.2316        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0082 | 8    | 0.092         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0092 | 9    | 0.2354        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0102 | 10   | 0.2881        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0112 | 11   | 0.0046        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0123 | 12   | 0.1022        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0133 | 13   | 0.0723        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0143 | 14   | 0.1052        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0153 | 15   | 0.4409        | 0.1410          | 0.1724             | 0.1513            | 0.9542                 | 0.0900        | 1.7411              | 0.0000                | 0.0215          | 0.0220        | 0.0545                 | 0.8675                | 0.1312           | 0.0051          | 0.2532              | 0.7320          | 0.6426            | 0.9078                   |
| 0.0163 | 16   | 0.0001        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0174 | 17   | 0.3306        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0184 | 18   | 0.2889        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0194 | 19   | 0.2016        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0204 | 20   | 0.295         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0215 | 21   | 0.1884        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0225 | 22   | 0.2173        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0235 | 23   | 0.2632        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0245 | 24   | 0.0168        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0255 | 25   | 0.0348        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0266 | 26   | 0.1607        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0276 | 27   | 0.209         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0286 | 28   | 0.0724        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0296 | 29   | 0.345         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0306 | 30   | 0.0812        | 0.1390          | 0.1704             | 0.1523            | 0.9515                 | 0.0899        | 1.7373              | 0.0000                | 0.0215          | 0.0220        | 0.0541                 | 0.8709                | 0.1306           | 0.0050          | 0.2490              | 0.7324          | 0.6429            | 0.9079                   |
| 0.0317 | 31   | 0.2389        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0327 | 32   | 0.0954        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0337 | 33   | 0.0002        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0347 | 34   | 0.2136        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0358 | 35   | 0.2434        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0368 | 36   | 0.0745        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0378 | 37   | 0.3987        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0388 | 38   | 0.0742        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0398 | 39   | 0.0017        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0409 | 40   | 0.2109        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0419 | 41   | 0.1791        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0429 | 42   | 0.1338        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0439 | 43   | 0.275         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0449 | 44   | 0.1818        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0460 | 45   | 0.2393        | 0.1371          | 0.1658             | 0.1534            | 0.9482                 | 0.0912        | 1.7347              | 0.0000                | 0.0211          | 0.0221        | 0.0536                 | 0.8666                | 0.1292           | 0.0051          | 0.2438              | 0.7326          | 0.6433            | 0.9085                   |
| 0.0470 | 46   | 0.2644        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0480 | 47   | 0.0184        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0490 | 48   | 0.4364        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0501 | 49   | 0.3131        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0511 | 50   | 0.2334        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0521 | 51   | 0.094         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0531 | 52   | 0.2048        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0541 | 53   | 0.138         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0552 | 54   | 0.1472        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0562 | 55   | 0.1483        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0572 | 56   | 0.1489        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0582 | 57   | 0.2447        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0592 | 58   | 0.0023        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0603 | 59   | 0.2143        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0613 | 60   | 0.0006        | 0.1321          | 0.1601             | 0.1521            | 0.9489                 | 0.0926        | 1.7312              | 0.0000                | 0.0209          | 0.0222        | 0.0533                 | 0.8544                | 0.1278           | 0.0056          | 0.2372              | 0.7342          | 0.6424            | 0.9087                   |
| 0.0623 | 61   | 0.1404        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0633 | 62   | 0.0407        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0644 | 63   | 0.0295        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0654 | 64   | 0.1194        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0664 | 65   | 0.007         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0674 | 66   | 0.1981        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0684 | 67   | 0.3657        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0695 | 68   | 0.3135        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0705 | 69   | 0.1054        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0715 | 70   | 0.2613        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0725 | 71   | 0.0079        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0735 | 72   | 0.0157        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0746 | 73   | 0.1849        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0756 | 74   | 0.0975        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0766 | 75   | 0.2335        | 0.1271          | 0.1556             | 0.1509            | 0.9486                 | 0.0977        | 1.7180              | 0.0000                | 0.0207          | 0.0222        | 0.0520                 | 0.8392                | 0.1258           | 0.0060          | 0.2331              | 0.7365          | 0.6426            | 0.9085                   |
| 0.0776 | 76   | 0.0662        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0787 | 77   | 0.0915        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0797 | 78   | 0.1385        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0807 | 79   | 0.3163        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0817 | 80   | 0.185         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0827 | 81   | 0.1407        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0838 | 82   | 0.1441        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0848 | 83   | 0.0           | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0858 | 84   | 0.1092        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0868 | 85   | 0.2534        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0878 | 86   | 0.0641        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0889 | 87   | 0.3494        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0899 | 88   | 1.4592        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0909 | 89   | 0.1255        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0919 | 90   | 0.2629        | 0.1169          | 0.1521             | 0.1639            | 0.9536                 | 0.1017        | 1.7091              | 0.0000                | 0.0207          | 0.0223        | 0.0509                 | 0.8204                | 0.1247           | 0.0069          | 0.2274              | 0.7404          | 0.6425            | 0.9082                   |
| 0.0930 | 91   | 0.3228        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0940 | 92   | 0.1874        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0950 | 93   | 0.3143        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0960 | 94   | 0.2283        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0970 | 95   | 0.2747        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0981 | 96   | 0.0362        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.0991 | 97   | 0.0002        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1001 | 98   | 0.3649        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1011 | 99   | 0.3179        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1021 | 100  | 0.0557        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1032 | 101  | 0.3263        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1042 | 102  | 0.231         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1052 | 103  | 0.3294        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1062 | 104  | 0.0975        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1073 | 105  | 0.1257        | 0.1078          | 0.1506             | 0.1725            | 0.9358                 | 0.1025        | 1.6902              | 0.0000                | 0.0204          | 0.0224        | 0.0492                 | 0.8026                | 0.1251           | 0.0082          | 0.2205              | 0.7416          | 0.6429            | 0.9084                   |
| 0.1083 | 106  | 0.0841        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1093 | 107  | 0.3928        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1103 | 108  | 0.1016        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1113 | 109  | 0.2171        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1124 | 110  | 0.1669        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1134 | 111  | 1.3794        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1144 | 112  | 0.1301        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1154 | 113  | 0.1865        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1164 | 114  | 0.1471        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1175 | 115  | 0.1562        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1185 | 116  | 0.5139        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1195 | 117  | 0.1164        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1205 | 118  | 0.1798        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1216 | 119  | 0.1081        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1226 | 120  | 0.1001        | 0.1094          | 0.1537             | 0.1700            | 0.9225                 | 0.1013        | 1.6895              | 0.0000                | 0.0199          | 0.0224        | 0.0494                 | 0.8019                | 0.1260           | 0.0072          | 0.2186              | 0.7442          | 0.6423            | 0.9088                   |
| 0.1236 | 121  | 0.1866        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1246 | 122  | 0.0017        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1256 | 123  | 0.0127        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1267 | 124  | 0.2586        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1277 | 125  | 0.0281        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1287 | 126  | 0.0289        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1297 | 127  | 0.0659        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1307 | 128  | 0.0001        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1318 | 129  | 0.132         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1328 | 130  | 0.0003        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1338 | 131  | 0.2092        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1348 | 132  | 0.5796        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1359 | 133  | 0.4157        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1369 | 134  | 0.2938        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1379 | 135  | 0.0566        | 0.1126          | 0.1621             | 0.1648            | 0.9074                 | 0.1052        | 1.6810              | 0.0000                | 0.0198          | 0.0224        | 0.0508                 | 0.8041                | 0.1283           | 0.0060          | 0.2173              | 0.7419          | 0.6404            | 0.9088                   |
| 0.1389 | 136  | 0.0864        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1399 | 137  | 0.3386        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1410 | 138  | 0.103         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1420 | 139  | 0.4119        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1430 | 140  | 0.1294        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1440 | 141  | 0.1687        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1450 | 142  | 0.167         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1461 | 143  | 1.2691        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1471 | 144  | 0.2091        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1481 | 145  | 0.1221        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1491 | 146  | 0.0           | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1502 | 147  | 0.0484        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1512 | 148  | 0.1199        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1522 | 149  | 0.4259        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1532 | 150  | 0.0532        | 0.1037          | 0.1649             | 0.1605            | 0.8964                 | 0.1105        | 1.6757              | 0.0000                | 0.0189          | 0.0226        | 0.0536                 | 0.7999                | 0.1278           | 0.0051          | 0.2132              | 0.7400          | 0.6419            | 0.9094                   |
| 0.1542 | 151  | 0.3261        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1553 | 152  | 0.1618        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1563 | 153  | 0.2373        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1573 | 154  | 0.0943        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1583 | 155  | 0.1366        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1593 | 156  | 0.0941        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1604 | 157  | 0.0914        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1614 | 158  | 0.1362        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1624 | 159  | 0.1623        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1634 | 160  | 0.167         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1645 | 161  | 0.0605        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1655 | 162  | 0.2929        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1665 | 163  | 0.1709        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1675 | 164  | 0.1022        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1685 | 165  | 0.1399        | 0.0829          | 0.1599             | 0.1472            | 0.8837                 | 0.1189        | 1.6713              | 0.0000                | 0.0182          | 0.0229        | 0.0516                 | 0.7814                | 0.1268           | 0.0045          | 0.2061              | 0.7405          | 0.6439            | 0.9092                   |
| 0.1696 | 166  | 0.0008        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1706 | 167  | 0.0201        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1716 | 168  | 0.1038        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1726 | 169  | 0.1082        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1736 | 170  | 0.2274        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1747 | 171  | 0.211         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1757 | 172  | 0.2527        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1767 | 173  | 0.0           | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1777 | 174  | 0.0565        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1788 | 175  | 0.1456        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1798 | 176  | 0.1498        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1808 | 177  | 0.1689        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1818 | 178  | 0.1241        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1828 | 179  | 0.1143        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1839 | 180  | 0.0823        | 0.0751          | 0.1540             | 0.1511            | 0.8806                 | 0.1188        | 1.6510              | 0.0000                | 0.0182          | 0.0231        | 0.0495                 | 0.7501                | 0.1356           | 0.0055          | 0.1996              | 0.7410          | 0.6448            | 0.9084                   |
| 0.1849 | 181  | 0.1072        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1859 | 182  | 0.2119        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1869 | 183  | 0.2067        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1879 | 184  | 0.0849        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1890 | 185  | 0.1698        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1900 | 186  | 0.0729        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1910 | 187  | 0.0402        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1920 | 188  | 0.0977        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1931 | 189  | 0.0836        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1941 | 190  | 0.264         | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1951 | 191  | 0.1379        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1961 | 192  | 0.1358        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1971 | 193  | 0.0939        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1982 | 194  | 0.4662        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |
| 0.1992 | 195  | 0.0428        | 0.0634          | 0.1615             | 0.1542            | 0.8759                 | 0.1146        | 1.6089              | 0.0000                | 0.0175          | 0.0228        | 0.0482                 | 0.7465                | 0.1595           | 0.0064          | 0.1977              | 0.7282          | 0.6436            | 0.9073                   |
| 0.2002 | 196  | 0.3566        | -               | -                  | -                 | -                      | -             | -                   | -                     | -               | -             | -                      | -                     | -                | -               | -                   | -               | -                 | -                        |

</details>

### 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",
}
```

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