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---
base_model: microsoft/deberta-v2-xlarge
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:99470
- loss:CachedGISTEmbedLoss
widget:
- source_sentence: what is the wisconsin idea
sentences:
- The Netherlands Time is in the Central European Time Zone . Central European Standard
Time ( CET ) is 1 hours ahead of Greenwich Mean Time ( GMT+1 ). Like most states
in Europe, Summer (Daylight-Saving) Time is observed in The Netherlands Time,
where the time is shifted forward by 1 hour; 2 hours ahead of Greenwich Mean Time
( GMT+2 ). After the Summer months the time in The Netherlands Time is shifted
back by 1 hour to Central European Time (CET) or ( GMT+1 )
- "Unless stated otherwise, these amounts are the total that is recommended for\
\ your dog over a 24 hour period. Most adult dogs should eat two meals a day (puppies\
\ often require three or more feedings), so youâ\x80\x99ll need to divide the\
\ amount in the table by the number of meals you are offering."
- McCarthy's book, The Wisconsin Idea, published in 1912, describes the major problems
facing the country, some of the Progressive reforms already passed, and his guiding
vision for the future. Library-Archives.
- source_sentence: More than 273 people have died from the 2019-20 coronavirus outside
mainland China .
sentences:
- 'More than 3,700 people have died : around 3,100 in mainland China and around
550 in all other countries combined .'
- 'More than 3,200 people have died : almost 3,000 in mainland China and around
275 in other countries .'
- more than 4,900 deaths have been attributed to COVID-19 .
- source_sentence: The planets orbit around the sun.
sentences:
- The planets orbit around what celestial body?
- What are the high points in a transverse wave called?
- After fertilization, how many cells does a zygote form into?
- source_sentence: Cells are small biological structures that make up all living things,
including the human body.
sentences:
- Of the three basic types of radioactive emissions, what particle is the most penetrating?
- What small biological structures make up all living things, including the human
body?
- What cellular structure is used during endocytosis to allow molecules to enter
the cell?
- source_sentence: '"""Music of the Night"" and ""All I Ask of You"" are songs from
which stage musical?"'
sentences:
- 'Electric resistance unit conversion - SI derived quantity Electric resistance
unit conversion - Discussion Forum ›› SI derived quantity: electric resistance
This category of measurement units is defined by the "electric resistance" type,
which is an SI derived quantity. ›› SI unit: ohm The SI derived unit for electric
resistance is the ohm. ›› Convert ohm to another unit Convert ohm to I''m feeling
lucky, show me some random units ›› Convert between two electric resistance units
Convert ›› Definition: Ohm The ohm (symbol: Ω) is the SI unit of electrical
impedance or, in the direct current case, electrical resistance, named after Georg
Ohm. It is defined as the resistance between two points of a conductor when a
constant potential difference of 1 volt, applied to these points, produces in
the conductor a current of 1 ampere, the conductor not being the seat of any electromotive
force.'
- 'All I Ask of You - YouTube All I Ask of You Want to watch this again later? Sign
in to add this video to a playlist. Need to report the video? Sign in to report
inappropriate content. Rating is available when the video has been rented. This
feature is not available right now. Please try again later. Published on Jan 6,
2012 Twenty-second in a series of clips from the 2004 film version of The Phantom
of the Opera. Performed by Patrick Wilson as Raoul and Emmy Rossum as Christine.
For more Phantom, find us on Facebook: http://www.facebook.com/ThePhantomOfT...
Or follow us on Twitter: http://www.twitter.com/TheOperaGhosts Or add us to your
Google+ Circles: https://plus.google.com/1019282273838... Category'
- Beatles US Labels - Variations THE CAPITOL � APPLE LABEL DISCOGRAPHY In
early 1964, Capitol Records became the primary U.S. manufacturer of The Beatles
major releases. When Apple Records was formed by the Beatles in 1968, Capitol
still maintained production and distribution control over the Beatles records.
When Apple was dissolved in 1975, the entire Beatles catalog, including solo releases,
reverted back to Capitol. In the early 1990�s, all The Beatles/solo Capitol
product was reissued on the Apple label. The Beatles, collectively and individually,
have also appeared on several other labels over the years. However, most of
these releases failed to stay in production long enough to experience significant
label design changes. The relatively few that did, are thoroughly identified
in their respective sections.
model-index:
- name: SentenceTransformer based on microsoft/deberta-v2-xlarge
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.9043388760494557
name: Pearson Cosine
- type: spearman_cosine
value: 0.919076121993877
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9234022512717295
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9199692676687267
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9241059779670555
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9203842013643476
name: Spearman Euclidean
- type: pearson_dot
value: 0.8886027908855041
name: Pearson Dot
- type: spearman_dot
value: 0.8892642189440298
name: Spearman Dot
- type: pearson_max
value: 0.9241059779670555
name: Pearson Max
- type: spearman_max
value: 0.9203842013643476
name: Spearman Max
- task:
type: binary-classification
name: Binary Classification
dataset:
name: allNLI dev
type: allNLI-dev
metrics:
- type: cosine_accuracy
value: 0.716796875
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.744063138961792
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.6287128712871288
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7192724943161011
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5497835497835498
name: Cosine Precision
- type: cosine_recall
value: 0.7341040462427746
name: Cosine Recall
- type: cosine_ap
value: 0.5872524450594222
name: Cosine Ap
- type: dot_accuracy
value: 0.716796875
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 740.0152587890625
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6201923076923077
name: Dot F1
- type: dot_f1_threshold
value: 690.6895751953125
name: Dot F1 Threshold
- type: dot_precision
value: 0.5308641975308642
name: Dot Precision
- type: dot_recall
value: 0.7456647398843931
name: Dot Recall
- type: dot_ap
value: 0.5749750816372319
name: Dot Ap
- type: manhattan_accuracy
value: 0.71484375
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 710.319091796875
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.6294416243654821
name: Manhattan F1
- type: manhattan_f1_threshold
value: 713.067138671875
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.5610859728506787
name: Manhattan Precision
- type: manhattan_recall
value: 0.7167630057803468
name: Manhattan Recall
- type: manhattan_ap
value: 0.588942704782977
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.71484375
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 22.877256393432617
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.6275510204081632
name: Euclidean F1
- type: euclidean_f1_threshold
value: 22.877256393432617
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.5616438356164384
name: Euclidean Precision
- type: euclidean_recall
value: 0.7109826589595376
name: Euclidean Recall
- type: euclidean_ap
value: 0.587161106665968
name: Euclidean Ap
- type: max_accuracy
value: 0.716796875
name: Max Accuracy
- type: max_accuracy_threshold
value: 740.0152587890625
name: Max Accuracy Threshold
- type: max_f1
value: 0.6294416243654821
name: Max F1
- type: max_f1_threshold
value: 713.067138671875
name: Max F1 Threshold
- type: max_precision
value: 0.5616438356164384
name: Max Precision
- type: max_recall
value: 0.7456647398843931
name: Max Recall
- type: max_ap
value: 0.588942704782977
name: Max Ap
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Qnli dev
type: Qnli-dev
metrics:
- type: cosine_accuracy
value: 0.69921875
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7296463251113892
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.700525394045534
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6390261650085449
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5970149253731343
name: Cosine Precision
- type: cosine_recall
value: 0.847457627118644
name: Cosine Recall
- type: cosine_ap
value: 0.7499128775998081
name: Cosine Ap
- type: dot_accuracy
value: 0.685546875
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 729.7808837890625
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6901408450704225
name: Dot F1
- type: dot_f1_threshold
value: 627.7941284179688
name: Dot F1 Threshold
- type: dot_precision
value: 0.5903614457831325
name: Dot Precision
- type: dot_recall
value: 0.8305084745762712
name: Dot Recall
- type: dot_ap
value: 0.7267806921631229
name: Dot Ap
- type: manhattan_accuracy
value: 0.703125
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 719.592529296875
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.6963696369636965
name: Manhattan F1
- type: manhattan_f1_threshold
value: 864.5909423828125
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.5702702702702702
name: Manhattan Precision
- type: manhattan_recall
value: 0.8940677966101694
name: Manhattan Recall
- type: manhattan_ap
value: 0.7518755537519403
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.703125
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 23.62594223022461
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.6930693069306931
name: Euclidean F1
- type: euclidean_f1_threshold
value: 27.77108383178711
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.5675675675675675
name: Euclidean Precision
- type: euclidean_recall
value: 0.8898305084745762
name: Euclidean Recall
- type: euclidean_ap
value: 0.7513960328625653
name: Euclidean Ap
- type: max_accuracy
value: 0.703125
name: Max Accuracy
- type: max_accuracy_threshold
value: 729.7808837890625
name: Max Accuracy Threshold
- type: max_f1
value: 0.700525394045534
name: Max F1
- type: max_f1_threshold
value: 864.5909423828125
name: Max F1 Threshold
- type: max_precision
value: 0.5970149253731343
name: Max Precision
- type: max_recall
value: 0.8940677966101694
name: Max Recall
- type: max_ap
value: 0.7518755537519403
name: Max Ap
---
# SentenceTransformer based on microsoft/deberta-v2-xlarge
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) 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 1536-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) <!-- at revision 1d134961d4db8e7e8eb1bc1ab81cb370244c57f7 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1536 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': 1536, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("bobox/DeBERTa2-0.9B-ST-v2-checkpoints-tmp")
# Run inference
sentences = [
'"""Music of the Night"" and ""All I Ask of You"" are songs from which stage musical?"',
'All I Ask of You - YouTube All I Ask of You Want to watch this again later? Sign in to add this video to a playlist. Need to report the video? Sign in to report inappropriate content. Rating is available when the video has been rented. This feature is not available right now. Please try again later. Published on Jan 6, 2012 Twenty-second in a series of clips from the 2004 film version of The Phantom of the Opera. Performed by Patrick Wilson as Raoul and Emmy Rossum as Christine. For more Phantom, find us on Facebook: http://www.facebook.com/ThePhantomOfT... Or follow us on Twitter: http://www.twitter.com/TheOperaGhosts Or add us to your Google+ Circles: https://plus.google.com/1019282273838... Category',
'Beatles US Labels - Variations \xa0 THE CAPITOL � APPLE LABEL DISCOGRAPHY\xa0 In early 1964, Capitol Records became the primary U.S. manufacturer of The Beatles major releases.\xa0 When Apple Records was formed by the Beatles in 1968, Capitol still maintained production and distribution control over the Beatles records.\xa0 When Apple was dissolved in 1975, the entire Beatles catalog, including solo releases, reverted back to Capitol. In the early 1990�s, all The Beatles/solo Capitol product was reissued on the Apple label.\xa0 The Beatles, collectively and individually, have also appeared on several other labels over the years.\xa0 However, most of these releases failed to stay in production long enough to experience significant label design changes.\xa0 The relatively few that did, are thoroughly identified in their respective sections.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1536]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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### Direct Usage (Transformers)
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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 | 0.9043 |
| **spearman_cosine** | **0.9191** |
| pearson_manhattan | 0.9234 |
| spearman_manhattan | 0.92 |
| pearson_euclidean | 0.9241 |
| spearman_euclidean | 0.9204 |
| pearson_dot | 0.8886 |
| spearman_dot | 0.8893 |
| pearson_max | 0.9241 |
| spearman_max | 0.9204 |
#### 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 | 0.7168 |
| cosine_accuracy_threshold | 0.7441 |
| cosine_f1 | 0.6287 |
| cosine_f1_threshold | 0.7193 |
| cosine_precision | 0.5498 |
| cosine_recall | 0.7341 |
| cosine_ap | 0.5873 |
| dot_accuracy | 0.7168 |
| dot_accuracy_threshold | 740.0153 |
| dot_f1 | 0.6202 |
| dot_f1_threshold | 690.6896 |
| dot_precision | 0.5309 |
| dot_recall | 0.7457 |
| dot_ap | 0.575 |
| manhattan_accuracy | 0.7148 |
| manhattan_accuracy_threshold | 710.3191 |
| manhattan_f1 | 0.6294 |
| manhattan_f1_threshold | 713.0671 |
| manhattan_precision | 0.5611 |
| manhattan_recall | 0.7168 |
| manhattan_ap | 0.5889 |
| euclidean_accuracy | 0.7148 |
| euclidean_accuracy_threshold | 22.8773 |
| euclidean_f1 | 0.6276 |
| euclidean_f1_threshold | 22.8773 |
| euclidean_precision | 0.5616 |
| euclidean_recall | 0.711 |
| euclidean_ap | 0.5872 |
| max_accuracy | 0.7168 |
| max_accuracy_threshold | 740.0153 |
| max_f1 | 0.6294 |
| max_f1_threshold | 713.0671 |
| max_precision | 0.5616 |
| max_recall | 0.7457 |
| **max_ap** | **0.5889** |
#### 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 | 0.6992 |
| cosine_accuracy_threshold | 0.7296 |
| cosine_f1 | 0.7005 |
| cosine_f1_threshold | 0.639 |
| cosine_precision | 0.597 |
| cosine_recall | 0.8475 |
| cosine_ap | 0.7499 |
| dot_accuracy | 0.6855 |
| dot_accuracy_threshold | 729.7809 |
| dot_f1 | 0.6901 |
| dot_f1_threshold | 627.7941 |
| dot_precision | 0.5904 |
| dot_recall | 0.8305 |
| dot_ap | 0.7268 |
| manhattan_accuracy | 0.7031 |
| manhattan_accuracy_threshold | 719.5925 |
| manhattan_f1 | 0.6964 |
| manhattan_f1_threshold | 864.5909 |
| manhattan_precision | 0.5703 |
| manhattan_recall | 0.8941 |
| manhattan_ap | 0.7519 |
| euclidean_accuracy | 0.7031 |
| euclidean_accuracy_threshold | 23.6259 |
| euclidean_f1 | 0.6931 |
| euclidean_f1_threshold | 27.7711 |
| euclidean_precision | 0.5676 |
| euclidean_recall | 0.8898 |
| euclidean_ap | 0.7514 |
| max_accuracy | 0.7031 |
| max_accuracy_threshold | 729.7809 |
| max_f1 | 0.7005 |
| max_f1_threshold | 864.5909 |
| max_precision | 0.597 |
| max_recall | 0.8941 |
| **max_ap** | **0.7519** |
<!--
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## Training Details
### Training Datasets
#### negation-triplets
* Dataset: negation-triplets
* Size: 4,987 training samples
* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | entailment | negative |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 21.87 tokens</li><li>max: 144 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.72 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.94 tokens</li><li>max: 39 tokens</li></ul> |
* Samples:
| anchor | entailment | negative |
|:--------------------------------------------------------------|:---------------------------------------------------------------------------|:----------------------------------------------------------------------------|
| <code>a very dirty toilet in a tiled bathroom</code> | <code>A dirty toilet in a dirty bathroom with a octagon tile floor.</code> | <code>A clean toilet in a dirty bathroom with an octagon tile floor.</code> |
| <code>enjoy the wildlife</code> | <code>Enjoy the animals.</code> | <code>Ignore the animals.</code> |
| <code>A man looking inside of birdcages on a sidewalk.</code> | <code>A man, holding his hat, is looking into the bird cage. </code> | <code>A man, holding his hat, is looking away from the bird cage. </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: 4,987 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: 6 tokens</li><li>mean: 16.62 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 36.79 tokens</li><li>max: 133 tokens</li></ul> |
* Samples:
| claim | evidence |
|:-------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Mase was born before 1976 .</code> | <code>Mase was born Mason Drell Betha in Jacksonville , Florida , on August 27 , 1975 , as a fraternal twin born almost two months premature .</code> |
| <code>On Rotten Tomatoes , Going in Style received more than 105 reviews and a rating of under 47 % .</code> | <code>On Rotten Tomatoes , the film has an approval rating of 46 % based on 106 reviews , with an average rating of 5.3/10 .</code> |
| <code>Aaron Charles Donald is an American football defensive end for Los Angeles Rams .</code> | <code>Aaron Charles Donald ( born May 23 , 1991 ) is an American football defensive end for the Los Angeles Rams of the National Football League ( NFL ) .</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: 4,987 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 15.84 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 14.96 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| <code>A negative enthalpy change is observed in an exothermic reaction.</code> | <code>What enthalpy change is observed in an exothermic reaction?</code> |
| <code>Fungus-like protists such as slime molds reproduce with spores.</code> | <code>How do fungus-like protists such as slime molds reproduce?</code> |
| <code>Unlike energy, matter doesn’t need to be constantly added to ecosystems because it is recycled through ecosystems.</code> | <code>Unlike energy, what doesn’t need to be constantly added to ecosystems because it is recycled through ecosystems?</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### scitail-pairs-pos
* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 4,987 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 23.21 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.66 tokens</li><li>max: 39 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|
| <code>During equinox times the Sun's vertical ray is shining directly on the Earth's equator and neither hemisphere is tilted toward or away from the Sun.</code> | <code>The sun is directly over the equator during.</code> |
| <code>All the baby s major organs begin to develop in the first 6 to 8 weeks of pregnancy, so tight control from the moment of conception is critical.</code> | <code>By 8 weeks, all major organs start developing.</code> |
| <code>Nobody disputes that all modern humans belong to one species, Homo sapiens .</code> | <code>Humans belong to the species homo sapiens.</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: 4,987 training samples
* Columns: <code>summary</code> and <code>document</code>
* Approximate statistics based on the first 1000 samples:
| | summary | document |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 25.19 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 217.32 tokens</li><li>max: 422 tokens</li></ul> |
* Samples:
| summary | document |
|:------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Chester's Elliott Durrell salvaged a 1-1 draw at Boreham Wood with a late wonder strike.</code> | <code>Morgan Ferrier's fifth-minute effort looked like it would be enough for the hosts after the 24-year-old evaded Blaine Hudson to get in behind and tap the ball home.<br>After that the game became scrappy, with neither side creating many clear-cut chances, although Chester's Ryan Astles was forced into a goal-line clearance after 70 minutes to block a Matthew Paine drive.<br>Just when it looked like Chester boss Jon McCarthy was staring at a fourth defeat in his first five games, Durrell picked the ball up around halfway, spotted Grant Smith off his line and struck from distance to level things up with seven minutes remaining.<br>Report supplied by the Press Association<br>Match ends, Boreham Wood 1, Chester FC 1.<br>Second Half ends, Boreham Wood 1, Chester FC 1.<br>Substitution, Chester FC. Sam Hughes replaces Kane Richards.<br>Goal! Boreham Wood 1, Chester FC 1. Elliott Durrell (Chester FC).<br>Substitution, Boreham Wood. Jordan Chiedozie replaces Morgan Ferrier.<br>Substitution, Boreham Wood. Aaron Kuhl replaces Kenny Davis.<br>Substitution, Chester FC. Elliott Durrell replaces Jordan Chapell.<br>Evan Horwood (Chester FC) is shown the yellow card for a bad foul.<br>Second Half begins Boreham Wood 1, Chester FC 0.<br>First Half ends, Boreham Wood 1, Chester FC 0.<br>Goal! Boreham Wood 1, Chester FC 0. Morgan Ferrier (Boreham Wood).<br>First Half begins.<br>Lineups are announced and players are warming up.</code> |
| <code>A major trauma centre in Stoke-on-Trent has been rated the best in the country for saving the lives of patients.</code> | <code>The University Hospitals of North Midlands Major Trauma Centre has the best total rolling survival rates of any adult major trauma single site centre since 2013.<br>Latest statistics show that for every 1,000 people treated in the last four years, 13 more survived than expected.<br>The centre is based at Royal Stoke University Hospital.<br>Medical director Dr John Oxtoby, described it as a "huge accomplishment".<br>See more stories from across Stoke and Staffordshire here<br>The figures come from the Trauma Audit and Research Network, an independent monitor of trauma care in England and Wales.<br>The data shows the centre also had the best survival rates for adult major trauma in 2015-16, when there were 15 extra survivors per 1,000 patients than expected.<br>"To have the best survival rates over four years of any major trauma centre is a phenomenal achievement," Dr Oxtoby added.<br>The centre treats patients from as far away as north Wales and the Peak District.<br>Those treated include people seriously injured in incidents such as vehicle crashes, falls, or assaults.</code> |
| <code>Sunderland defender Paddy McNair will miss the rest of the season because of a cruciate knee ligament injury.</code> | <code>The 21-year-old, signed from Manchester United in August, has made 12 appearances for a Black Cats side that are 19th in the Premier League.<br>He was injured during his side's 3-0 win over Hull City on Saturday.<br>"We won't see him again this season and all we can hope is getting him right for the start of next season," said Sunderland boss David Moyes.<br>"I think he'd just started to find his way in the Premier League - even though he had experience at Manchester United - and the games he was having were bringing him on and giving him confidence."<br>The injury means McNair is unlikely to feature in Northern Ireland's World Cup 2018 qualifier against Norway in March.</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: 4,987 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: 16.93 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 86.84 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The procedure known as angioplasty is used when what part of the circulatory system is clogged?</code> | <code>When a blood vessel gets clogged, there is no medical equivalent of "Drano" that will clear it out. There is, however, a procedure known as angioplasty. A thin tube with a balloon is threaded through the blood vessels. Once in place, the balloon is inflated to compress the clog against the artery wall.</code> |
| <code>Mollusks such as squid and octopi, which must hunt to survive, possess what complex organs containing millions of neurons?</code> | <code>Figure 35.2 Nervous systems vary in structure and complexity. In (a) cnidarians, nerve cells form a decentralized nerve net. In (b) echinoderms, nerve cells are bundled into fibers called nerves. In animals exhibiting bilateral symmetry such as (c) planarians, neurons cluster into an anterior brain that processes information. In addition to a brain, (d) arthropods have clusters of nerve cell bodies, called peripheral ganglia, located along the ventral nerve cord. Mollusks such as squid and (e) octopi, which must hunt to survive, have complex brains containing millions of neurons. In (f) vertebrates, the brain and spinal cord comprise the central nervous system, while neurons extending into the rest of the body comprise the peripheral nervous system. (credit e: modification of work by Michael Vecchione, Clyde F. Roper, and Michael J. Sweeney, NOAA; credit f: modification of work by NIH).</code> |
| <code>Combining nonpolar olive oil and polar vinegar yields what type of mixture?</code> | <code>Another familiar example is the mixing of vinegar and olive oil. Olive oil is a nonpolar substance, while vinegar (which is mostly water and acetic acid) is polar. The result is a heterogeneous mixture that exhibits a bilayer.</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: 4,987 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: 11.33 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 33.52 tokens</li><li>max: 67 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What happens before the rigid structure that surrounds the plant cell dilates?</code> | <code>Dilation occurs when cell walls relax.. Cell wall is the rigid structure that surrounds the plant cell. <br> Dilation occurs when the rigid structure that surrounds the plant cell relaxes.</code> |
| <code>Reusing plastic bags has a positive impact on what?</code> | <code>recycling has a positive impact on the environment. Plastic bags are recyclable and they are reusable. <br> Reusing plastic bags has a positive impact on the environment</code> |
| <code>What protects the body from harmful substances?</code> | <code>skin is used for protecting the body from harmful substances. Skin is a protective organ. <br> organs protect the body from harmful substances</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: 3,007 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: 13.75 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.34 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
| question | fact |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|
| <code>Heat exposure at higher temperatures without ability to regulate internal body temperatures will result in the expiration of which of these?</code> | <code>if an organism becomes too hot then that organism may die</code> |
| <code>Which of the following would be part of the water cycle?</code> | <code>evaporation is a stage in the water cycle process</code> |
| <code>polar bears are white due to an inherited</code> | <code>the color of fur is an inherited characteristic</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: 4,987 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: 8.68 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 75.36 tokens</li><li>max: 238 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how many days is it in a month</code> | <code>Each month has either 28, 30, or 31 days during a common year, which has 365 days. During leap years, which occur nearly every 4 years, we add an extra (intercalary) day, Leap Day, on 29 February, making leap years 366 days long.</code> |
| <code>who were the peloponnesian wars between? who won them?</code> | <code>The Greek city-state of Sparta won the war against Athens. The war, known as the Peloponnesian War, raged for 27 years between the Athenian realm and the Peloponnesian coalition commanded by the Spartans. The Peloponnesian War began in 431 B.C.C. and ended in 404 B.C.E. when Athens conceded defeat to Sparta.</code> |
| <code>average nurse practitioner salary ny</code> | <code>Nurse Practitioner New York, NY Salary. Nurse Practitioner New York, NY average salary is $91,897, median salary is $79,060 with a salary range from $20,530 to $1,926,393. Nurse Practitioner New York, NY salaries are collected from government agencies and companies. Each salary is associated with a real job position.</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: 4,987 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: 10 tokens</li><li>mean: 11.79 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 131.1 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>parks and rec episode where ron gets a hernia</code> | <code>The Stakeout (Parks and Recreation) Back at city hall, Ron (Nick Offerman) remains immobile in his chair all day due to a hernia which causes excruciating pain if he moves. Ron remains immobile in his seat well into the night, until the janitors turn the lights off on him. The intern, April (Aubrey Plaza), returns to check on him, and wheels Ron out to the car on his office chair to bring him to the hospital.</code> |
| <code>when did jane beale come back to eastenders</code> | <code>Jane Beale Jane Beale (also Collins and Clarke) is a fictional character from the BBC soap opera, EastEnders, played by Laurie Brett. She made her first appearance on 21 June 2004. Brett took maternity leave in 2011[1] and departed on 19 May.[2] She returned on 8 November[3] and departed again on 27 January 2012.[4][5] Jane made temporary a return to the show on 6 January 2014 until 20 May and permanently from 24 November 2014.[6][7] Her major storylines have included her relationship and later marriages to Ian Beale (Adam Woodyatt); the first ended due to his affair with Glenda Mitchell (Glynis Barber), an affair with Grant Mitchell (Ross Kemp), accidentally shot by Ian's stepson Steven Beale (Aaron Sidwell) which results her desire to have a child of her own following a hysterectomy, a relationship with Masood Ahmed (Nitin Ganatra), her popular friendship with Tanya Branning (Jo Joyner), covering up her adopted son Bobby's (Eliot Carrington) role in the murder of her stepdaughter Lucy Beale (Hetti Bywater), being paralysed after brutally attacked with a hockey stick by Bobby, caught up in the fire at Beale's restaurant, which was started by Steven before she was then left in the blaze by Max Branning (Jake Wood) and being forced by Max to leave Walford which led to a second departure on 23 October 2017.[8] She made a voiceover appearance on 8 December.</code> |
| <code>who sings what lovers do with maroon 5</code> | <code>What Lovers Do "What Lovers Do" is a song by American pop rock band Maroon 5 featuring American R&B singer SZA. It was released on August 30, 2017, as the lead single from the band's sixth studio album Red Pill Blues (2017).[4] The song contains an interpolation of the 2016 song "Sexual" by Neiked featuring Dyo, therefore Victor Rådström, Dyo and Elina Stridh are credited as songwriters.</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: 4,987 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: 64 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 205.76 tokens</li><li>max: 402 tokens</li></ul> |
* Samples:
| query | answer |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What word is the most common synonym of the word “corsair”?</code> | <code>Corsair Synonyms, Corsair Antonyms | Thesaurus.com Cite This Source Word Origin & History corsair 1549, from Fr. corsaire, from Prov. cursar, It. corsaro, from M.L. cursarius "pirate," from L. cursus "course, a running," from currere "to run" (see current). Meaning evolved in M.L. from "course" to "journey" to "expedition" to an expedition specifically for plunder. Example Sentences for corsair I have been a mercenary soldier, a corsair, a kozak, and a hundred other things. The words were out and the thing was done before Asad had realized the corsair's intent. He left a corsair's name to other times, Linked with one virtue, and a thousand crimes. If it were a Corsair, the rowers would all be Christian prisoners. After the failure of her attempt to board us, the corsair hauled aft her sheets and shot ahead of the Good Hope. I have read the Corsair, mended my petticoat, and have nothing else to do. He had also a mania for travelling, and when he was only two-and-twenty was captured by an Algerian corsair and enslaved. You are wrong there,” said the corsair, “for we would have attacked you all the same. To Moore he dedicated his "Corsair," and to read the preface is to see how sincerely attached Byron was to his friend. The corsair was standing by the side of Mr Tompkins, close by the taffrail.</code> |
| <code>"What scale initially related wind conditions to their effects on the sails of a man of war, from ""sufficient for steerage"" to ""which canvas sails couldn't withstand."""</code> | <code>Oil droplets transport due to irregular waves: Development of large-scale spreading coefficients "The sea state can be defined in a variety of ways. For example, one may use the Beaufort scale (Singleton, 2008) that describes the sea state based on qualitative terms. It is an empirical yet expedient scale. " [Show abstract] [Hide abstract] ABSTRACT: The movement of oil droplets due to waves and buoyancy was investigated by assuming an irregular sea state following a JONSWAP spectrum and four buoyancy values. A technique known as Wheeler stretching was used to model the movement of particles under the moving water surface. In each simulation, 500 particles were released and were tracked for a real time of 4.0 h. A Monte Carlo approach was used to obtain ensemble properties. It was found that small eddy diffusivities that decrease rapidly with depth generated the largest horizontal spreading of the plume. It was also found that large eddy diffusivities that decrease slowly with depth generated the smallest horizontal spreading coefficient of the plume. The increase in buoyancy resulted in a decrease in the horizontal spreading coefficient, which suggests that two-dimensional (horizontal) models that predict the transport of surface oil could be overestimating the spreading of oil. Full-text · Article · Jan 2016</code> |
| <code>What is the name given to the collection of Welsh legends?</code> | <code>1000+ images about Welsh - Legends & Folklore on Pinterest | Alan lee, Welsh and King arthur When looking for some illustrations on the Mabinogion I found this amazing looking book- The Golden Cockerel Sir Gawain so now I'm on the hunt for the artist. See More</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: 4,987 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: 11.52 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 56.25 tokens</li><li>max: 143 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how long does it take to get used to getting up earlier?</code> | <code>The most efficient method for changing the time you wake up is to do it gradually — 10–15 minutes earlier for 1–3 days until you feel used to it, and then lower it down. If you get up at 8 a.m. generally, don't suddenly change it to 6 a.m. Try 7:45 a.m. first.</code> |
| <code>what are the differences among standards goals and objectives?</code> | <code>Standard: Written expectations of what students are expected to know at a specific stage of education. These are based off of learning objectives. Goal: Unique to an individual student. May stem from a standard, but also relates to a student's academic habits and organization.</code> |
| <code>how do u work out the surface area of a triangular prism?</code> | <code>A triangular prism has three rectangular sides and two triangular faces. To find the area of the rectangular sides, use the formula A = lw, where A = area, l = length, and h = height. To find the area of the triangular faces, use the formula A = 1/2bh, where A = area, b = base, and h = height.</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: 4,987 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: 25.62 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 25.61 tokens</li><li>max: 52 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|
| <code>Primidone also causes exfoliative dermatitis , Johnson -- Stevens syndrome , and toxic epidermal necrolysis .</code> | <code>Primidone also causes exfoliative dermatitis , Johnson - Stevens -- Syndrome and toxic epidermal necrolysis .</code> |
| <code>The agency was founded in 1976 in Chicago , and it entered the New York market in 1998 and Milwaukee in 2009 .</code> | <code>The agency was founded in Chicago in 1976 and entered New York in 1998 and Milwaukee in 2009 .</code> |
| <code>After his death , the widow of Kellow Mary Hope Kellow with her daughter Mary moved to Sydney .</code> | <code>After his death , Kellow 's widow Mary Hope Kellow moved to Sydney with her daughter Mary .</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: 36,619 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: 28.68 tokens</li><li>max: 368 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 55.88 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The Turks , Tibetans , Muslim Arabs , and Tang competed for control of Central Asia until the tang 's collapse in the 10th century .</code> | <code>The Turks , Tang , Muslim Arabs and the Tibetans competed for control over Central Asia until the collapse of the Tang in the 10th century .</code> |
| <code>What do animals use to reproduce?</code> | <code>an animal needs to attract a mate to reproduce. Animals mate because of smells. <br> animals attract with smells</code> |
| <code>Some touch receptors sense a difference in pain or what?</code> | <code>Some touch receptors sense differences in temperature or pain.. Heat and temperature are the same. <br> Some touch receptors sense differences in heat or pain.</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: 19.71 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 32.5 tokens</li><li>max: 78 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: 14.54 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.12 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.43 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| anchor | entailment | negative |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------|:----------------------------------------------------------|
| <code>A Marine that is looking at his cell phone.</code> | <code>A marine in uniform using a smart phone.</code> | <code>Not a marine in uniform using a smart phone.</code> |
| <code>A snowboarder on a wide plain of snow</code> | <code>A snow field with a snowboarder on it</code> | <code>An empty field with no snowboarder on it</code> |
| <code>Three men, one holding pipes, another holding a large object above his head, and one resting against the pipe bed on the truck, are looking at the camera.</code> | <code>three men look at the camera</code> | <code>three men ignore the camera</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: 20.13 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.48 tokens</li><li>max: 23 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| <code>humans normally have 23 pairs of chromosomes.</code> | <code>Humans typically have 23 pairs pairs of chromosomes.</code> |
| <code>A solution is a homogenous mixture of two or more substances that exist in a single phase.</code> | <code>Solution is the term for a homogeneous mixture of two or more substances.</code> |
| <code>Upwelling The physical process in near-shore ocean systems of rising of nutrients and colder bottom waters to the surface because of constant wind patterns along the shoreline.</code> | <code>Upwelling is the term for when deep ocean water rises to the surface.</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### scitail-pairs-qa
* Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 16.6 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.67 tokens</li><li>max: 33 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|
| <code>Magma comes toward earth's crust through mantle plumes.</code> | <code>What substance comes toward earth's crust through mantle plumes?</code> |
| <code>The understory of the rainforest commonly has ferns and other ground plants.</code> | <code>What part of the rainforest commonly has ferns and other ground plants?</code> |
| <code>Because trees add water vapor to air, cutting down forests leads to longer periods of drought.</code> | <code>Because trees add water vapor to air, cutting down forests leads to longer periods of what?</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### xsum-pairs
* Dataset: xsum-pairs
* Size: 128 evaluation samples
* Columns: <code>summary</code> and <code>document</code>
* Approximate statistics based on the first 1000 samples:
| | summary | document |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 13 tokens</li><li>mean: 25.42 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 67 tokens</li><li>mean: 213.0 tokens</li><li>max: 354 tokens</li></ul> |
* Samples:
| summary | document |
|:-----------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>An exam paper for Scottish secondary pupils is being re-issued after a mistake was spotted.</code> | <code>The National 5 Modern Studies exam will take place on Friday afternoon.<br>The Scottish Qualification Authority (SQA) said the paper has been reprinted because of a typographical error.<br>The exams body said there was no suggestion of a security breach. A spokesman added that it had procedures in place to deal with situations like this.<br>The printing mistake in the original paper was in a diagram, not a question.<br>The SQA said it acted quickly after the error was spotted.<br>The reprinted paper - with the correct text in the diagram but otherwise identical - will be issued to exam centres across Scotland.<br>Last year the Higher English paper was replaced amid fears of a security breach.<br>The SQA also faced criticism over mistakes in the National 5 computing paper.<br>Earlier this month, the SQA announced that teachers would not have access to exam papers until the day after each test takes place.<br>It said the change was in order to improve security and confidentiality. The largest teachers' union, the EIS, has condemned the move.</code> |
| <code>US regulators have told seven carmakers the recall of airbags made by Japanese firm Takata is likely to expand.</code> | <code>The National Highway Traffic Safety Administration (NHTSA) has written to firms including Mercedes-Benz, Jaguar-Land Rover and Tesla to ask which of their models use the Takata parts.<br>About 23.4 million Takata airbag inflators have been recalled in the US.<br>The airbags have been linked to eight deaths and more than 100 injuries around the world.<br>It was found they can inflate with excessive force, spraying metal shrapnel at the drivers.<br>The driver and passenger airbags were in more than 19 million cars sold by 11 different companies such as Honda in the US.<br>In the letters sent last week, the NHTSA said the recall "will likely grow to include vehicles that are outside the scope of the current recalls".<br>The agency will attend a public meeting in Washington on 22 October to discuss the Takata investigation and whether it will take over management of the recalls to speed up the repairs.<br>Carmakers are struggling to get parts with only 4.4 million airbag inflators replaced since the start of this month.<br>The other automakers that received the letters include Suzuki, Volvo Trucks, Volkswagen and Spartan Motors.<br>So far Mercedes, Jaguar-Land Rover and Tesla have all said the air bags they used from Takata are not part of current recalls, according to the Associated Press.</code> |
| <code>A salt lake in Melbourne has turned pink due to a combination of sunlight, warm temperatures and low rainfall.</code> | <code>Wildlife officers said algae growing in the salt crust at the bottom of Westgate Park's lake produce a red pigment.<br>"Enjoy the views, but we recommend you don't come into contact with the water," Parks Victoria said.<br>The phenomenon also occurs in Spain's Salina de Torrevieja, Canada's Dusty Rose Lake and Senegal's Lake Retba.<br>In Australia, the natural occurring sight can be seen in Victoria's Murray-Sunset National Park and Western Australia's Lake Hillier.<br>You might also be interested in:<br>Dr Mark Norman, Parks Victoria chief conservation scientist, said the colouration was caused by a harmless, single-cell alga known as Dunalliela.<br>"It's completely natural," he said. "We often get comments that it looks like an industrial accident of pink paint."<br>Dr Norman said that even though the water is not dangerous, he would not recommend taking a swim.<br>"It's so salty and muddy on the bottom that you would come out looking like a frosted rum ball, especially when you dried," he said.<br>Parks Victoria said the lake is expected to return to blue when the weather cooled and the rainfall increased.</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: 9 tokens</li><li>mean: 16.98 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 86.18 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What structures receive blood from the atria and pump it out of the heart?</code> | <code>The bottom two chambers of the heart are called the left and right ventricles. The ventricles receive blood from the atria and pump it out of the heart, either to the lungs or to the rest of the body.</code> |
| <code>Amphibians have permeable skin which allows for the exchange of oxygen and carbon dioxide, what is this "breathing called?"</code> | <code>Characteristics of Amphibians As tetrapods, most amphibians are characterized by four well-developed limbs. Some species of salamanders and all caecilians are functionally limbless; their limbs are vestigial. An important characteristic of extant amphibians is a moist, permeable skin that is achieved via mucus glands that keep the skin moist; thus, exchange of oxygen and carbon dioxide with the environment can take place through it ( cutaneous respiration). Additional characteristics of amphibians include pedicellate teeth—teeth in which the root and crown are calcified, separated by a zone of noncalcified tissue—and a papilla amphibiorum and papilla basilaris, structures of the inner ear that are sensitive to frequencies below and above 10,00 hertz, respectively. Amphibians also have an auricular operculum, which is an extra bone in the ear that transmits sounds to the inner ear. All extant adult amphibians are carnivorous, and some terrestrial amphibians have a sticky tongue that is used to capture prey.</code> |
| <code>What form do alkali metals take at room temperature?</code> | <code>Alkali metals are all solids at room temperature.</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: 11.25 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 34.65 tokens</li><li>max: 66 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Warm body temperature requires what?</code> | <code>an animal usually requires a warm body temperature for survival. Most animals require water regularly. <br> a warm body temperature requires water</code> |
| <code>what rotates causing cycles of day and night?</code> | <code>a planet rotating causes cycles of day and night on that planet. Of all the planets, Mars is most like Earth. <br> Mars rotating causes cycles of day and night</code> |
| <code>What being tilted on its rotating axis causes spring, summer, autumn, and winter.</code> | <code>the Earth being tilted on its rotating axis causes seasons. Spring, summer, autumn, and winter are the seasons of the year. <br> Earth being tilted on its rotating axis causes spring, summer, autumn, and winter.</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: 13.96 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: 9.1 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 74.29 tokens</li><li>max: 200 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>tcf routing number illinois</code> | <code>Routing Number 271972572. Tcf National Bank Illinois Routing Number. TCF NATIONAL BANK ILLINOIS ROUTING ABA NUMBER. 271972572 routing number is a 9-digit number designed and assigned to Tcf National Bank Illinois by The American Bankers Association (ABA) to identify the financial institution upon which a payment was drawn.</code> |
| <code>why was jamestown so important other then being the first permanent english settlement</code> | <code>Credit: National Park Service. View full size image. Jamestown, founded in 1607, was the first successful permanent English settlement in what would become the United States. It was located on Jamestown Island, in Virginia, about 30 miles (47 kilometers) up the James River from the Atlantic coast.amestown, founded in 1607, was the first successful permanent English settlement in what would become the United States.</code> |
| <code>when was the town of farragut tn incorporated</code> | <code>In January of 1980, residents decided to incorporate by an overwhelming margin. The Town of Farragut was incorporated on January 16, 1980, with the first board of Mayor and Alderman elected on April 1, 1980.</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: 9 tokens</li><li>mean: 12.24 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 128.73 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>when did rodan and fields start direct sales</code> | <code>Rodan + Fields In 2002, Drs. Fields and Rodan launched Rodan + Fields. Products were sold in department stores. In 2003, Rodan + Fields was purchased by Estée Lauder.[1] In 2007, Drs. Fields and Rodan reacquired the brand[4] and transitioned the company from department stores to multi-level marketing, where consultants are paid a commission for their own sales and for the sales of people they recruit.[1]</code> |
| <code>what are the house names in harry potter</code> | <code>Hogwarts Hogwarts is divided into four houses, each bearing the last name of its founder: Godric Gryffindor, Salazar Slytherin, Rowena Ravenclaw and Helga Hufflepuff. Throughout the school year, the houses compete for the House Cup, gaining and losing points based on actions such as performance in class and rule violations. The house with the highest end-of-year total wins and has its colours displayed in the Great Hall for the following school year. Each house also has its own Quidditch team that competes for the Quidditch Cup. These two competitions breed rivalries between the houses. Houses at Hogwarts are living and learning communities for their students. Each house is under the authority of one of the Hogwarts staff members. The Heads of the houses, as they are called, are in charge of giving their students important information, dealing with matters of severe punishment, and responding to emergencies in their houses, among other things. Each year, year level groups of every separate house share the same dormitory and classes. The dormitory and common room of a House are, barring rare exceptions, inaccessible to students belonging to other Houses.</code> |
| <code>when was calibri font made available for use</code> | <code>Calibri Calibri (/kəˈliːbri/) is a sans-serif typeface family designed by Luc(as) de Groot in 2002–2004 and released to the general public in 2007, with Microsoft Office 2007 and Windows Vista.[2][3] In Office 2007, it replaced Times New Roman as the default typeface in Word[4] and replaced Arial as the default in PowerPoint, Excel, Outlook, and WordPad. Creator de Groot described its subtly rounded design as having "a warm and soft character".[3]</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: 16.59 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 199.75 tokens</li><li>max: 373 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Theodore Francis international airport is in which US state?</code> | <code>Theodore Francis Green State Airport, Providence RI Hotels Near the Airport T. F. Green Airport (sometimes called T. F. Green International Airport) (IATA: PVD, ICAO: KPVD, FAA LID: PVD) , also known as Theodore Francis Green State Airport and Providence International Airport is located in Warwick, six miles (10 km) south of Providence, in Kent County, Rhode Island, USA. Completely rebuilt in 1996, it was the first state-owned airport in the United States. Providence International Airport is a popular alternative to Boston, Massachusetts' often busy Logan International Airport, as delays and wait time are minimal. There are two terminals with two concourses, North and South. The South Concourse has eight gates, and the North Concourse has 14 gates. Gate 8 is designed for international arrivals for use by Air Canada and SATA International flights; it is directly connected to customs, which is on the lower level of the concourse. The terminal contains a number of stores and restaurants, and a central food court. Local Time: 17-Jan-2017 12:02 AM © Copyright 2017, Providence-Airport.com, not the official airport website</code> |
| <code>What is the English for ‘Duirt me leat go raibh me breoite’?</code> | <code>Duirt mé leat go raibh mé breoite – Susan Hated Literature That is the enscription on Spike Milligan’s headstone . For those of you without Irish it is a translation of what he wanted: “I told you I was ill.” Duirt mé (I told) pronounced durtch may (like the month) leat (you) pronounced lat go raibh mé (that I was) pronounced not like the english go, but the o is sorta like “uh”; raibh can have a variety of pronunciations, I’d usually say row (as in to fight, not to do anything in a boat), but you could say rev, or rav, and mé is may (again) breoite (ill) pronounced bro-tcha Here endeth the very bad Irish lesson, from which you are probably more confuddled than you were before? On a secondary issue, why is the Irish language constantly described as Gaelic? It’s Irish, or Gaeilge. The latter only if you are speaking as Gaeilge. I mean I don’t say that some German person was speaking in Deutsch unless I’m attempting to say it in German. This is a pet peeve of mine, feel free to ignore :)</code> |
| <code>Which group sung the 1997 Eurovision Song Contest winning Love Shine A Light?</code> | <code>Eurovision 1997 - Katrina & The Waves - Love shine a light - YouTube Eurovision 1997 - Katrina & The Waves - Love shine a light Want to watch this again later? Sign in to add this video to a playlist. Need to report the video? Sign in to report inappropriate content. Rating is available when the video has been rented. This feature is not available right now. Please try again later. Uploaded on Sep 14, 2008 Eurovision 1997 - Katrina & The Waves - Love shine a light Category</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: 11.01 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 57.03 tokens</li><li>max: 96 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>can i use full synthetic oil in my snowblower?</code> | <code>So, while synthetic will do better in temperature extremes, (extremes not seen in a properly running snow blower) it will get dirty at the same rate as regular oil. Therefore we will need to replace it at the same intervals as regular oil, but at a greater cost.</code> |
| <code>what is the difference between primary and foreign key in sql?</code> | <code>Difference between Primary Key and Foreign Key. Primary key uniquely identify a record in the table. Foreign key is a field in the table that is primary key in another table. ... By default, Primary key is clustered index and data in the database table is physically organized in the sequence of clustered index.</code> |
| <code>how to change administrator windows 10?</code> | <code>['Under Settings > Accounts > Family & other users, select the account owner name, then select Change account type.', 'Under Account type, select Administrator and OK.', 'Sign in with the new administrator account.']</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: 25.58 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 25.4 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: 325 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: 31.88 tokens</li><li>max: 344 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 55.72 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>when was the world cup</code> | <code>In the first World Cup final, held on July 30, 1930, 93,000 spectators looked on as Uruguay defeated Argentina 4â2 in a rematch of the 1928 Olympic gold medal game. Uruguay went on to win its second World Cup in 1950 with a 2-1 win over Brazil in Rio de Janeiro.</code> |
| <code>Highlands-based Neil Anderson set up the camera in a part of Strathspey in the Cairngorms.<br>He said: "I knew the cat used a path fairly regularly and seeing that snow was forecast I rigged up my camera trap.<br>"I went away for a couple of weeks so when I finally checked the camera it was a great surprise to come back to."<br>The wildcat was photographed on 13 January.<br>Mr Anderson, whose credits include commissions for the BBC's Springwatch wildlife programmes, also photographs wildlife in other parts of the world.<br>The Scottish wildcat is one of the world's most endangered animals.<br>Habitat loss and breeding with domestic and feral cats are factors behind a severe decline in the mammals.</code> | <code>A rare Scottish wildcat has been photographed on a camera trap rigged up by a wildlife cameraman.</code> |
| <code>can phi be disclosed for marketing purposes?</code> | <code>The Privacy Rule addresses the use and disclosure of protected health information for marketing purposes by: ... Requiring individual authorization for all uses or disclosures of protected health information for marketing purposes with limited exceptions.</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`: 64
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 3
- `learning_rate`: 1.5e-05
- `weight_decay`: 0.001
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1.5e-06}
- `warmup_ratio`: 0.25
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa2-0.9B-ST-v2-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 3
- `eval_accumulation_steps`: None
- `learning_rate`: 1.5e-05
- `weight_decay`: 0.001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1.5e-06}
- `warmup_ratio`: 0.25
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTa2-0.9B-ST-v2-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | xsum-pairs loss | global dataset loss | scitail-pairs-pos loss | nq pairs loss | qasc pairs loss | trivia pairs loss | scitail-pairs-qa loss | vitaminc-pairs loss | sciq pairs loss | negation-triplets loss | gooaq pairs loss | msmarco pairs loss | openbookqa pairs loss | paws-pos loss | Qnli-dev_max_ap | allNLI-dev_max_ap | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:-------------------:|:----------------------:|:-------------:|:---------------:|:-----------------:|:---------------------:|:-------------------:|:---------------:|:----------------------:|:----------------:|:------------------:|:---------------------:|:-------------:|:---------------:|:-----------------:|:------------------------:|
| 0.0058 | 3 | 7.8503 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0116 | 6 | 8.4022 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0174 | 9 | 11.1776 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0231 | 12 | 9.7845 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0289 | 15 | 8.9224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0347 | 18 | 11.1202 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0405 | 21 | 7.413 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0463 | 24 | 7.7803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0501 | 26 | - | 5.3947 | 6.3484 | 2.5062 | 10.4474 | 6.6809 | 6.1073 | 2.9786 | 4.1224 | 0.8978 | 3.9176 | 8.0953 | 15.7827 | 6.4026 | 1.6077 | 0.6094 | 0.3453 | 0.3223 |
| 0.0521 | 27 | 7.9729 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0578 | 30 | 6.0587 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0636 | 33 | 5.6742 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0694 | 36 | 6.5406 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0752 | 39 | 5.4429 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0810 | 42 | 6.7855 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0868 | 45 | 5.3403 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0925 | 48 | 4.2282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0983 | 51 | 4.7411 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1003 | 52 | - | 4.4624 | 3.8914 | 1.1342 | 6.2954 | 4.8895 | 5.7900 | 2.0086 | 3.9298 | 0.7183 | 3.2670 | 5.7852 | 7.5325 | 3.4273 | 0.4783 | 0.5957 | 0.4051 | 0.5257 |
| 0.1041 | 54 | 3.9082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1099 | 57 | 4.3922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1157 | 60 | 3.2655 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1215 | 63 | 3.1043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1272 | 66 | 2.2074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1330 | 69 | 1.4414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1388 | 72 | 1.5937 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1446 | 75 | 1.0306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1504 | 78 | 1.0784 | 0.4360 | 0.6714 | 0.1109 | 1.2714 | 1.3059 | 1.0828 | 0.1282 | 3.7639 | 0.2034 | 1.4773 | 0.7032 | 1.3856 | 1.1711 | 0.0449 | 0.6293 | 0.4747 | 0.8291 |
| 0.1562 | 81 | 0.9674 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1620 | 84 | 0.9335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1677 | 87 | 0.8806 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1735 | 90 | 0.631 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1793 | 93 | 0.3384 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1851 | 96 | 0.404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1909 | 99 | 0.6488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1967 | 102 | 0.4728 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2005 | 104 | - | 0.1409 | 0.3990 | 0.0726 | 0.5425 | 0.4244 | 0.6438 | 0.0485 | 3.3142 | 0.1505 | 1.3401 | 0.2238 | 0.5883 | 0.5924 | 0.0358 | 0.7154 | 0.5430 | 0.8863 |
| 0.2024 | 105 | 0.5094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2082 | 108 | 0.8002 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2140 | 111 | 0.3886 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2198 | 114 | 0.6937 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2256 | 117 | 0.2909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2314 | 120 | 0.3885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2371 | 123 | 0.29 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2429 | 126 | 0.3485 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2487 | 129 | 0.3931 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2506 | 130 | - | 0.0513 | 0.3591 | 0.0457 | 0.2678 | 0.2206 | 0.1540 | 0.0365 | 3.8795 | 0.1231 | 1.1135 | 0.1206 | 0.2162 | 0.4568 | 0.0261 | 0.6993 | 0.5638 | 0.9082 |
| 0.2545 | 132 | 0.3394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2603 | 135 | 0.1276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2661 | 138 | 0.3569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2719 | 141 | 0.1231 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2776 | 144 | 0.3086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2834 | 147 | 0.3541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2892 | 150 | 0.2597 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2950 | 153 | 0.1585 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3008 | 156 | 0.1436 | 0.0503 | 0.2959 | 0.0376 | 0.2004 | 0.1717 | 0.0823 | 0.0410 | 3.8346 | 0.0871 | 0.9441 | 0.0767 | 0.1350 | 0.4304 | 0.0213 | 0.7239 | 0.5807 | 0.9177 |
| 0.3066 | 159 | 0.1941 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3123 | 162 | 0.3041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3181 | 165 | 0.2358 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3239 | 168 | 0.2148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3297 | 171 | 0.8567 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3355 | 174 | 0.3668 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3413 | 177 | 0.3278 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3470 | 180 | 0.474 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3509 | 182 | - | 0.0382 | 0.3900 | 0.0550 | 0.2525 | 0.1511 | 0.0624 | 0.1251 | 3.8444 | 0.0825 | 0.8706 | 0.1212 | 0.4176 | 0.7894 | 0.0209 | 0.7187 | 0.5555 | 0.9103 |
| 0.3528 | 183 | 0.5365 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3586 | 186 | 0.6902 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3644 | 189 | 0.4105 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3702 | 192 | 0.2434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3760 | 195 | 0.1521 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3817 | 198 | 0.1878 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3875 | 201 | 0.3544 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3933 | 204 | 0.1397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3991 | 207 | 0.2982 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4010 | 208 | - | 0.0290 | 0.2504 | 0.0209 | 0.2429 | 0.1335 | 0.0537 | 0.0243 | 3.2105 | 0.0851 | 0.6793 | 0.0728 | 0.0954 | 0.4361 | 0.0224 | 0.7266 | 0.5853 | 0.9212 |
| 0.4049 | 210 | 0.1875 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4107 | 213 | 0.169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4165 | 216 | 0.2341 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4222 | 219 | 0.1806 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4280 | 222 | 0.2736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4338 | 225 | 0.1772 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4396 | 228 | 0.131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4454 | 231 | 0.0825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4512 | 234 | 0.2745 | 0.0315 | 0.2459 | 0.0264 | 0.2080 | 0.1237 | 0.0534 | 0.0236 | 3.4479 | 0.0854 | 0.6366 | 0.0623 | 0.0320 | 0.4140 | 0.0188 | 0.7252 | 0.5810 | 0.9186 |
| 0.4569 | 237 | 0.131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4627 | 240 | 0.2012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4685 | 243 | 0.0855 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4743 | 246 | 0.1181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4801 | 249 | 0.0992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4859 | 252 | 0.0375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4916 | 255 | 0.1503 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4974 | 258 | 0.1239 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5013 | 260 | - | 0.0130 | 0.2468 | 0.0173 | 0.1420 | 0.1050 | 0.0554 | 0.0256 | 2.7884 | 0.0917 | 0.6254 | 0.0432 | 0.4505 | 0.4391 | 0.0200 | 0.7392 | 0.5874 | 0.9206 |
| 0.5032 | 261 | 0.1849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5090 | 264 | 0.0453 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5148 | 267 | 0.118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5206 | 270 | 0.2285 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5263 | 273 | 0.072 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5321 | 276 | 0.1449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5379 | 279 | 0.2179 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5437 | 282 | 0.0687 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5495 | 285 | 0.071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5514 | 286 | - | 0.0061 | 0.2986 | 0.0183 | 0.1662 | 0.1212 | 0.0679 | 0.0217 | 3.9508 | 0.0967 | 0.6451 | 0.0508 | 0.0296 | 0.4552 | 0.0236 | 0.7463 | 0.5938 | 0.9179 |
| 0.5553 | 288 | 0.0855 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5611 | 291 | 0.103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5668 | 294 | 0.1555 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5726 | 297 | 0.1069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5784 | 300 | 0.2014 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5842 | 303 | 0.1028 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5900 | 306 | 0.2425 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5958 | 309 | 0.1639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6015 | 312 | 0.099 | 0.0050 | 0.2365 | 0.0077 | 0.0879 | 0.1507 | 0.0483 | 0.0146 | 3.3625 | 0.0870 | 0.5403 | 0.0421 | 0.0490 | 0.4215 | 0.0250 | 0.7519 | 0.5889 | 0.9191 |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.32.1
- 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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