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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:99622
- loss:CachedGISTEmbedLoss
widget:
- source_sentence: does alcohol cause anxiety
sentences:
- "Kim Jong Unâ\x80\x99s Wife â\x80\x98Missingâ\x80\x99, Assumed Pregnant. Kim Jong\
\ Unâ\x80\x99s wife, Ri Sol Ju, has reportedly â\x80\x98gone missingâ\x80\x99\
\ after not making any public appearances for the last 40 days, according to data\
\ released by North Korea news-monitoring website NK News."
- "Japan is the worldâ\x80\x99s largest mobile games market, with $6.2 billion in\
\ 2015E revenue. Despite having fewer players than China or the U.S., Japan has\
\ the highest average mobile games spending of any major country. China has 785\
\ million mobile gamers, 62% of Asiaâ\x80\x99s total."
- Alcohol Causes Anxiety and Behavior Changes. Frequent alcohol consumption can
extremely impair several functions of your brain including the area that is responsible
for controlling your behavior. This is why as you consume alcohol, your behavior,
conduct and other cognitive abilities will usually be the first to go.
- 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 action of flushing the toilet, performed multiple times daily,
is the single biggest use of water in the home.
sentences:
- What action, performed multiple times daily, is the single biggest use of water
in the home?
- Which of these would most likely improve the air quality in large Texas cities?
- The innermost layer of the sun is known as what?
- source_sentence: You call cellular respiration that does not need oxygen to proceed
anaerobic respiration.
sentences:
- A binary molecular compound is made up of two of what?
- What do you call cellular respiration that does not need oxygen to proceed?
- Roots grow in length and width from the primary and secondary what?
- source_sentence: What was the cause of the disqualification of Swedish pentathlete
Hans-Gunnar Liljenwall at Mexico City in 1968, the first as a result of failing
a doping test at an Olympic Games?
sentences:
- '''Cogito, ergo sum'' - the meaning and origin of this phrase Cogito, ergo sum
Usually translated from the Latin as ''I think, therefore I am''. Origin Possibly
the best known of all philosophical quotations; this is from the French philosopher
René Descartes in Discourse on Method, 1637, where he attempted to prove his existence
as a thinking being, by thinking. ''I think, therefore I am'' comes to us in English
via two translations. Descartes'' original statement in French was "Je pense,
donc je suis". This is such a well-known line that it has spawned humorous alternatives,
not least: "I''m pink, therefore I''m spam" "René Descartes was a drunken fart
- I drink therefore I am".'
- Hans Gunnar Liljenwall - Alchetron, The Free Social Encyclopedia I Love to read
n write about Interesting People Hans Gunnar Liljenwall Role Olympic athlete
Born 9 July 1941 (age 74) (1941-07-09) Hans-Gunnar Liljenwall (born 9 July 1941)
is a Swedish modern pentathlete who caused the disqualification of the Swedish
men's team at the 1968 Summer Olympics held in Mexico City for his alcohol use.
Liljenwall was the first athlete to be disqualified at the Olympics for drug use,
following the introduction of anti-doping regulations by the International Olympic
Committee in 1967. Sponsored Links Liljenwall reportedly had "two beers" to calm
his nerves before the pistol shooting portion of the modern pentathlon. The Swedish
team eventually had to return their bronze medals.
- Erstwhile | Definition of Erstwhile by Merriam-Webster Examples of erstwhile in
a sentence <there's now a store where erstwhile lay green and pleasant pastures>
Did You Know? The adverb erstwhile has been part of English since the 16th century,
but it is formed from two words that are much older. It comes from the Old English
words ær, meaning "early," and hwīl, which has much the same meaning as the modern
word while. (The English word ere, meaning "before," is also descendant of ær.)
The adjective erstwhile, as in erstwhile enemies, joined the language around 1900.
1569 First Known Use of erstwhile 1569
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: 0.9106921279068343
name: Pearson Cosine
- type: spearman_cosine
value: 0.9239059466308052
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9246302028077688
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9208650089167368
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9256757227724981
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9216089091348462
name: Spearman Euclidean
- type: pearson_dot
value: 0.8935209770403933
name: Pearson Dot
- type: spearman_dot
value: 0.8917772707787254
name: Spearman Dot
- type: pearson_max
value: 0.9256757227724981
name: Pearson Max
- type: spearman_max
value: 0.9239059466308052
name: Spearman Max
- task:
type: binary-classification
name: Binary Classification
dataset:
name: allNLI dev
type: allNLI-dev
metrics:
- type: cosine_accuracy
value: 0.732421875
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8159675598144531
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.6309278350515464
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6821494102478027
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.49038461538461536
name: Cosine Precision
- type: cosine_recall
value: 0.884393063583815
name: Cosine Recall
- type: cosine_ap
value: 0.5991355834488622
name: Cosine Ap
- type: dot_accuracy
value: 0.736328125
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 879.013916015625
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6206896551724138
name: Dot F1
- type: dot_f1_threshold
value: 740.831787109375
name: Dot F1 Threshold
- type: dot_precision
value: 0.4948453608247423
name: Dot Precision
- type: dot_recall
value: 0.8323699421965318
name: Dot Recall
- type: dot_ap
value: 0.5930218788414645
name: Dot Ap
- type: manhattan_accuracy
value: 0.734375
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 642.3578491210938
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.6324786324786326
name: Manhattan F1
- type: manhattan_f1_threshold
value: 796.259521484375
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.5016949152542373
name: Manhattan Precision
- type: manhattan_recall
value: 0.8554913294797688
name: Manhattan Recall
- type: manhattan_ap
value: 0.598127538996714
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.728515625
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 20.647186279296875
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.6322869955156951
name: Euclidean F1
- type: euclidean_f1_threshold
value: 24.933399200439453
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.5164835164835165
name: Euclidean Precision
- type: euclidean_recall
value: 0.815028901734104
name: Euclidean Recall
- type: euclidean_ap
value: 0.5969327943116731
name: Euclidean Ap
- type: max_accuracy
value: 0.736328125
name: Max Accuracy
- type: max_accuracy_threshold
value: 879.013916015625
name: Max Accuracy Threshold
- type: max_f1
value: 0.6324786324786326
name: Max F1
- type: max_f1_threshold
value: 796.259521484375
name: Max F1 Threshold
- type: max_precision
value: 0.5164835164835165
name: Max Precision
- type: max_recall
value: 0.884393063583815
name: Max Recall
- type: max_ap
value: 0.5991355834488622
name: Max Ap
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Qnli dev
type: Qnli-dev
metrics:
- type: cosine_accuracy
value: 0.712890625
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7136298418045044
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.705685618729097
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6252090930938721
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5828729281767956
name: Cosine Precision
- type: cosine_recall
value: 0.8940677966101694
name: Cosine Recall
- type: cosine_ap
value: 0.7429006619357093
name: Cosine Ap
- type: dot_accuracy
value: 0.689453125
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 764.2352294921875
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.7067137809187279
name: Dot F1
- type: dot_f1_threshold
value: 678.6840209960938
name: Dot F1 Threshold
- type: dot_precision
value: 0.6060606060606061
name: Dot Precision
- type: dot_recall
value: 0.847457627118644
name: Dot Recall
- type: dot_ap
value: 0.7164630756489325
name: Dot Ap
- type: manhattan_accuracy
value: 0.71875
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 725.244384765625
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.7078039927404719
name: Manhattan F1
- type: manhattan_f1_threshold
value: 829.4242553710938
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.6190476190476191
name: Manhattan Precision
- type: manhattan_recall
value: 0.826271186440678
name: Manhattan Recall
- type: manhattan_ap
value: 0.7439472197082637
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.71875
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 23.21845245361328
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.7014388489208634
name: Euclidean F1
- type: euclidean_f1_threshold
value: 26.616165161132812
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.609375
name: Euclidean Precision
- type: euclidean_recall
value: 0.826271186440678
name: Euclidean Recall
- type: euclidean_ap
value: 0.7459968056180196
name: Euclidean Ap
- type: max_accuracy
value: 0.71875
name: Max Accuracy
- type: max_accuracy_threshold
value: 764.2352294921875
name: Max Accuracy Threshold
- type: max_f1
value: 0.7078039927404719
name: Max F1
- type: max_f1_threshold
value: 829.4242553710938
name: Max F1 Threshold
- type: max_precision
value: 0.6190476190476191
name: Max Precision
- type: max_recall
value: 0.8940677966101694
name: Max Recall
- type: max_ap
value: 0.7459968056180196
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-v1-checkpoints-tmp")
# Run inference
sentences = [
'What was the cause of the disqualification of Swedish pentathlete Hans-Gunnar Liljenwall at Mexico City in 1968, the first as a result of failing a doping test at an Olympic Games?',
'Hans Gunnar Liljenwall - Alchetron, The Free Social Encyclopedia I Love to read n write about Interesting People Hans Gunnar Liljenwall Role\xa0\xa0Olympic athlete Born\xa0\xa09 July 1941 (age\xa074) (1941-07-09) Hans-Gunnar Liljenwall (born 9 July 1941) is a Swedish modern pentathlete who caused the disqualification of the Swedish men\'s team at the 1968 Summer Olympics held in Mexico City for his alcohol use. Liljenwall was the first athlete to be disqualified at the Olympics for drug use, following the introduction of anti-doping regulations by the International Olympic Committee in 1967. Sponsored Links Liljenwall reportedly had "two beers" to calm his nerves before the pistol shooting portion of the modern pentathlon. The Swedish team eventually had to return their bronze medals.',
'Erstwhile | Definition of Erstwhile by Merriam-Webster Examples of erstwhile in a sentence <there\'s now a store where erstwhile lay green and pleasant pastures> Did You Know? The adverb erstwhile has been part of English since the 16th century, but it is formed from two words that are much older. It comes from the Old English words ær, meaning "early," and hwīl, which has much the same meaning as the modern word while. (The English word ere, meaning "before," is also descendant of ær.) The adjective erstwhile, as in erstwhile enemies, joined the language around 1900. 1569 First Known Use of erstwhile 1569',
]
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]
```
<!--
### 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 | 0.9107 |
| **spearman_cosine** | **0.9239** |
| pearson_manhattan | 0.9246 |
| spearman_manhattan | 0.9209 |
| pearson_euclidean | 0.9257 |
| spearman_euclidean | 0.9216 |
| pearson_dot | 0.8935 |
| spearman_dot | 0.8918 |
| pearson_max | 0.9257 |
| spearman_max | 0.9239 |
#### 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 | 0.7324 |
| cosine_accuracy_threshold | 0.816 |
| cosine_f1 | 0.6309 |
| cosine_f1_threshold | 0.6821 |
| cosine_precision | 0.4904 |
| cosine_recall | 0.8844 |
| cosine_ap | 0.5991 |
| dot_accuracy | 0.7363 |
| dot_accuracy_threshold | 879.0139 |
| dot_f1 | 0.6207 |
| dot_f1_threshold | 740.8318 |
| dot_precision | 0.4948 |
| dot_recall | 0.8324 |
| dot_ap | 0.593 |
| manhattan_accuracy | 0.7344 |
| manhattan_accuracy_threshold | 642.3578 |
| manhattan_f1 | 0.6325 |
| manhattan_f1_threshold | 796.2595 |
| manhattan_precision | 0.5017 |
| manhattan_recall | 0.8555 |
| manhattan_ap | 0.5981 |
| euclidean_accuracy | 0.7285 |
| euclidean_accuracy_threshold | 20.6472 |
| euclidean_f1 | 0.6323 |
| euclidean_f1_threshold | 24.9334 |
| euclidean_precision | 0.5165 |
| euclidean_recall | 0.815 |
| euclidean_ap | 0.5969 |
| max_accuracy | 0.7363 |
| max_accuracy_threshold | 879.0139 |
| max_f1 | 0.6325 |
| max_f1_threshold | 796.2595 |
| max_precision | 0.5165 |
| max_recall | 0.8844 |
| **max_ap** | **0.5991** |
#### 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 | 0.7129 |
| cosine_accuracy_threshold | 0.7136 |
| cosine_f1 | 0.7057 |
| cosine_f1_threshold | 0.6252 |
| cosine_precision | 0.5829 |
| cosine_recall | 0.8941 |
| cosine_ap | 0.7429 |
| dot_accuracy | 0.6895 |
| dot_accuracy_threshold | 764.2352 |
| dot_f1 | 0.7067 |
| dot_f1_threshold | 678.684 |
| dot_precision | 0.6061 |
| dot_recall | 0.8475 |
| dot_ap | 0.7165 |
| manhattan_accuracy | 0.7188 |
| manhattan_accuracy_threshold | 725.2444 |
| manhattan_f1 | 0.7078 |
| manhattan_f1_threshold | 829.4243 |
| manhattan_precision | 0.619 |
| manhattan_recall | 0.8263 |
| manhattan_ap | 0.7439 |
| euclidean_accuracy | 0.7188 |
| euclidean_accuracy_threshold | 23.2185 |
| euclidean_f1 | 0.7014 |
| euclidean_f1_threshold | 26.6162 |
| euclidean_precision | 0.6094 |
| euclidean_recall | 0.8263 |
| euclidean_ap | 0.746 |
| max_accuracy | 0.7188 |
| max_accuracy_threshold | 764.2352 |
| max_f1 | 0.7078 |
| max_f1_threshold | 829.4243 |
| max_precision | 0.619 |
| max_recall | 0.8941 |
| **max_ap** | **0.746** |
<!--
## Bias, Risks and Limitations
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### Recommendations
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## Training Details
### Training Datasets
#### negation-triplets
* Dataset: negation-triplets
* Size: 5,025 training samples
* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | entailment | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 22.27 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.77 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.08 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
| anchor | entailment | negative |
|:--------------------------------------------------------------------------|:---------------------------------------------------------|:-------------------------------------------------------------|
| <code>A white kitty cat sitting on a bike seat.</code> | <code>a cat on top of a bike parked indoors</code> | <code>a cat underneath a bike parked indoors</code> |
| <code>A bathroom with a sink and toilet and a bowl on the counter.</code> | <code>A glass sink that is under a faucet.</code> | <code>A glass sink that is not under a faucet.</code> |
| <code>Seven people are jumping in the air, along the shore.</code> | <code>People at the shore are jumping in the air.</code> | <code>People at the shore are not jumping in the air.</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: 5,025 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: 16.21 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 37.01 tokens</li><li>max: 187 tokens</li></ul> |
* Samples:
| claim | evidence |
|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Boss Key Productions is yet to release the BlueStreak video game .</code> | <code>BlueStreak is an upcoming video game developed by Boss Key Productions and published by Nexon .</code> |
| <code>Jay-Z appeared on Blue Ivy 's first two albums .</code> | <code>Jay-Z appeared on Blue Ivy 's first two albums as well , and the two frequently collaborated .</code> |
| <code>The film was reviewed by more than 140 critics .</code> | <code>On review aggregator Rotten Tomatoes , the film has an approval rating of 80 % based on 142 reviews , with an average rating of 7.1/10 .</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: 5,025 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: 15.97 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.15 tokens</li><li>max: 33 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------|:---------------------------------------------------------|
| <code>The cytoskeleton is the skeleton of the cell.</code> | <code>What is the skeleton of the cell?</code> |
| <code>Muscular dystrophy is a a wasting disease.</code> | <code>What type of disease is muscular dystrophy?</code> |
| <code>A pumpkin is a fruit.</code> | <code>Which food is a fruit?</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### scitail-pairs-pos
* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 5,025 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: 23.62 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.74 tokens</li><li>max: 40 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------|
| <code>There have been five mass extinctions in Earth's history.</code> | <code>Five mass distinctions have radically altered the history of life.</code> |
| <code>The ultimate source of energy for life on Earth is the sun.</code> | <code>Ultimately, most life forms get their energy from the sun.</code> |
| <code>N Neurotransmitter Any one of numerous chemicals in the nervous system that modify or result in the transmission of nerve impulses between synapses.</code> | <code>Like a runner passing a baton, the transmission of nerve impulses between neurons depends on neurotransmitters.</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: 5,025 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: 25.63 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 211.46 tokens</li><li>max: 371 tokens</li></ul> |
* Samples:
| summary | document |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Rising British star Hannah Barnes will ride for the newly-formed Canyon//SRAM team in next year's inaugural UCI Women's WorldTour circuit.</code> | <code>Barnes, who has spent two seasons with American team UHC-Healthcare, won the white jersey for the best young rider at this year's Women's Tour.<br>"It's been really good in America but I'm happy to come back to Europe," said the 22-year-old from Northamptonshire.<br>"My whole goal for 2015 was to get some good results and then come back here."<br>Joining Barnes on the team are the overall winner of the Women's Tour, German time trial star Lisa Brennauer, and RideLondon Grand Prix winner, Italy's Barbara Guarischi.<br>The team will ride the full 17-race Women's WorldTour calendar in 2016.<br>Germany's Canyon will supply the bike frames, with American firm SRAM providing the components.<br>The team's nine riders, from six countries, will wear kit from British company Rapha, which has recently announced it will no longer be supplying elite men's outfit Team Sky after 2016.<br>The team will begin its 2016 season when Tiffany Cromwell rides in the Australian national championships in January, with the first full race being the Ladies Tour of Qatar in February.<br>Helping riders qualify for the Rio Olympics will be a focus for the team, as will the big American races, the Tour of California and Philadelphia Cycling Classic, as well as Britain's Women's Tour and the women's race at the Tour de France, La Course.<br>"My main goal for the year is to make the team for the Olympics but with this injury I don't really know," said Barnes, who broke her ankle in August.<br>"I'd like to go back to California and do the Tour there too."</code> |
| <code>A doctor was injured when a prisoner tried to escape during a routine medical appointment at a hospital in North Lanarkshire.</code> | <code>The 31-year-old inmate from HMP Shotts was being escorted by G4S security staff when he tried to get away.<br>A doctor who tried to assist the security staff suffered minor injuries during the incident on Friday.<br>The Scottish Prison Service said an investigation had begun into what happened.<br>A spokesman said: "I can confirm there was an incident involving a prisoner from HMP Shotts at Wishaw General Hospital today.<br>"We will be working with Police Scotland in investigating the full circumstances of the incident."<br>A G4S spokesman said: "During a routine hospital appointment a prisoner attempted to evade custody but was immediately apprehended by G4S staff.<br>"The prompt actions of our officers averted a more serious incident and demonstrates the challenging situations our staff can face while carrying out their duties."</code> |
| <code>West Ham winger Michail Antonio has been ruled out for the rest of the season with a "significant injury", manager Slaven Bilic says.</code> | <code>The 27-year-old was injured in the Hammers' 1-0 win over Swansea at London Stadium last weekend.<br>"It's a significant injury and he's out for the season," Bilic confirmed.<br>Antonio, who has scored nine goals for the Hammers this season, was called up by England for the first time in August.<br>"It is a big blow. We know what he has been giving. He is one of our best players," Bilic added.<br>He was again called up for England's World Cup qualifier against Lithuania last month but pulled out of the squad with a hamstring injury and has yet to make his international debut.<br>He joined West Ham from Nottingham Forest in 2015 and signed a new four-year deal with the club last summer.</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### sciq_pairs
* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815)
* Size: 5,025 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: 16.81 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 83.54 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What chemical affects the onset of puberty and duration?</code> | <code>Puberty lasts from about ages 12 to 18 years and is controlled by hormones.</code> |
| <code>Within the chloroplast, synthesis of what takes place in the fluid inside the inner membrane called the stroma?</code> | <code>Figure 4.17 The chloroplast has an outer membrane, an inner membrane, and membrane structures called thylakoids that are stacked into grana. The space inside the thylakoid membranes is called the thylakoid space. The light harvesting reactions take place in the thylakoid membranes, and the synthesis of sugar takes place in the fluid inside the inner membrane, which is called the stroma. Chloroplasts also have their own genome, which is contained on a single circular chromosome.</code> |
| <code>What type of scientist uses earth-orbiting telescopes?</code> | <code>Astronomers use many tools to study things in space. Earth-orbiting telescopes view stars and galaxies from the darkness of space ( Figure below ). They may have optical and radio telescopes to see things that the human eye can't see. Spacecraft travel great distances to send back information on faraway places.</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: 5,025 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 11.37 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 33.52 tokens</li><li>max: 64 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What can help decrease emissions?</code> | <code>carpooling decreases the amount of cars used to travel to a place. Cars and gasoline-burning engines are large sources of emissions. <br> Carpooling decreases emissions</code> |
| <code>What can antibodies (large Y-shaped proteins) recognize and bind to?</code> | <code>Antibodies are large, Y-shaped proteins that recognize and bind to antigens.. ALL immunogens are antigens. <br> Antibodies are large, Y-shaped proteins that can recognize and bind to immunogens</code> |
| <code>What process is needed to support a baby in the womb?</code> | <code>Oxygen is essential for cellular respiration for all aerobic organisms.. Less oxygen for the mother means less oxygen for the baby. <br> Mothers need respiration for their babies.</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: 3,029 training samples
* Columns: <code>question</code> and <code>fact</code>
* Approximate statistics based on the first 1000 samples:
| | question | fact |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 13.89 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.54 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
| question | fact |
|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| <code>A prickly pear absorbs nutrients from</code> | <code>plants absorb nutrients from soil into themselves through their roots</code> |
| <code>When it's summer in the USA it's winter</code> | <code>June is during the winter in the southern hemisphere</code> |
| <code>To move electrical energy around a field, a person would use</code> | <code>metal is an electrical energy conductor</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: 5,025 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: 8.59 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 75.16 tokens</li><li>max: 208 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>is switzerland expensive</code> | <code>Hi Im Swiss and I can answer your question. For us Swiss people Switzerland is not that expensive because out salary makes up for it (we have one of the highest salaries per/person worldwide). For foreigners visiting Switzerland many will find it very expensive, from public transportation to food.</code> |
| <code>what does dhea sulfate do for women</code> | <code>DHEA, DHEA-S. This test measures the level of dehydroepiandrosterone (DHEA) and dehydroepiandrosterone sulfate (DHEA-S) in your blood. It may also be used to check how well your adrenal glands are working. DHEA is a hormone made by your adrenal glands and to a lesser degree by the ovaries and testes. DHEA is changed into DHEA-S in your adrenal glands and liver. In both men and women, the sex hormones estrogen and testosterone depend on DHEA. DHEA also has a role in the making of insulin growth factor-1 (IGF-1).</code> |
| <code>how long do i need to keep tax returns</code> | <code>After filing your late taxes, remember you will keep your tax return three years from the date you file rather than three years from that tax year. For those that have old tax returns on file, donât be in a rush to shred them. While fall cleaning, organize and file away tax returns.</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: 5,025 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: 11.81 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 129.07 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who owns the rights to call of duty</code> | <code>Call of Duty The Call of Duty games are published and owned by Activision. While Infinity Ward is still a developer, Treyarch and Sledgehammer Games also develop several of the titles with the release of the studios' games alternating with each other. Some games have been developed by Gray Matter Interactive, Nokia, Exakt Entertainment, Spark Unlimited, Amaze Entertainment, n-Space, Aspyr, Rebellion Developments, Ideaworks Game Studio, and nStigate Games. The games use a variety of engines, including the id Tech 3, the Treyarch NGL, and the IW engine.</code> |
| <code>who sings i wanna get next to you</code> | <code>I Wanna Get Next to You "I Wanna Get Next to You" is a 1976 soul single written, composed and produced by American songwriter and producer Norman Whitfield, and most famously sung by American R&B band Rose Royce. It is the third official single from the Car Wash soundtrack. The song has also become a staple on oldies radio and on adult contemporary stations.</code> |
| <code>when was the birth control pill made available</code> | <code>Birth control In 1909, Richard Richter developed the first intrauterine device made from silkworm gut, which was further developed and marketed in Germany by Ernst Gräfenberg in the late 1920s.[152] In 1951, a chemist, named Carl Djerassi from Mexico City made the hormones in progesterone pills using Mexican yams.[153] Djerassi had chemically created the pill but was not equipped to distribute it to patients. Meanwhile, Gregory Pincus and John Rock with help from the Planned Parenthood Federation of America developed the first birth control pills in the 1950s, such as mestranol/noretynodrel, which became publicly available in the 1960s through the Food and Drug Administration under the name Enovid.[146][154] Medical abortion became an alternative to surgical abortion with the availability of prostaglandin analogs in the 1970s and mifepristone in the 1980s.[155]</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: 5,025 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: 16.85 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 202.22 tokens</li><li>max: 411 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What famous London building is officially called 1 Canada Square?</code> | <code>One Canada Square, London, England Tourist Information Locals and travelers to connect with About London Borough of Tower Hamlets, London, England 51.505-0.0196 One Canada Square (often incorrectly called Canary Wharf , after its location) is a skyscraper in Canary Wharf, London . It was the tallest building in the United Kingdom from 1990 to 2010, standing at 235 metres (770 ft) above ground level and containing 50 storeys. In late 2010, it was surpassed by The Shard (completed in July 2012) which stands at 309.6 metres (1,016 ft). One Canada Square was designed by principal architect Cesar Pelli, who based the design and shape mainly on the World Financial Center and the Elizabeth Tower. The building is clad with expensive stainless steel. One of the predominant features of the building is the pyramid roof which contains a flashing aircraft warning light, a rare feature for buildings in the United Kingdom. The distinctive pyramid pinnacle is at 240 metres (800 ft) above sea level. One Canada Square is primarily used for offices, though there are some retail units on the lower ground floor. It is a prestigious location for offices and as of January 2013 was 100% let. The building is recognised as a London landmark and it has gained much attention through film, television and other media when its status was the tallest building in the United Kingdom and continues to gain attention. Map</code> |
| <code>Who is the patron saint of dentists?</code> | <code>St. Apollonia - Saints & Angels - Catholic Online Saints & Angels Author and Publisher - Catholic Online Facts Take the Saints Trivia Quiz now! St. Apollonia, who died in the year 249, was martyred for not renouncing her faith during the reign of Emperor Philip. The account of the life of St. Apollonia was written by St. Dionysius to Fabian, Bishop of Antioch. Apollonia had all her teeth knocked out after being hit in the face by a Christian persecutor under the reign of Emperor Philip. After she was threatened with fire unless she renounced her faith, Apollonia jumped into the flames voluntarily. She is considered the patron of dental diseases and is often invoked by those with toothaches. Ancient art depicts her with a golden tooth at the end of her necklace. Also in art, she is seen with pincers holding a tooth.</code> |
| <code>Which country has the international vehicle registration CDN?</code> | <code>Why does Canada have the letters CDN as its international car registration plate? Surely it would be more logical to have CND? | Notes and Queries | guardian.co.uk Why does Canada have the letters CDN as its international car registration plate? Surely it would be more logical to have CND? THE DN stands for Dominion. The abbreviation was created when Canada was one. Bob Hammarberg, Minneapolis, US. CDN for Canada is no more idiotic than CH for Switzerland, proof that it is time to standardize the spelling of place names around the globe. If we call "Livorno" "Leghorn," is it possible that Hispanics say, "Neuva Yorca," and the French transmogrify "Little Rock" into "La Petite Roche"? Nancy Belck ([email protected]) , Canada still is the Dominion of Canada. And if D did stand for Dominion why were Australia, New Zealand and South Africa not also given a D? Keith Mills, Alne, Yorks There is nothing idiotic about Switzerland's use of CH for her international designation. CH is Latin and it stands for "Confoederatio Helvetica". Switzerland is a country of four official languages - so choosing Latin was the most neutral choice. What is idiotic is that, by law, all Swiss cars have to display the CH plate - even if they never leave the country. This even applies to police cars, road sweepers and public buses. These vehicles clearly rarely leave the country (although there are cross border bus routes in some areas). Mark Butcher, Geneva, Switzerland</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: 5,025 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: 11.43 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 55.2 tokens</li><li>max: 126 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what are fettuccine used for?</code> | <code>Long flattish noodle-shaped pasta, similar to tagliatelle. A very good pasta to serve with oil or butter-based sauces, as the sauce goes a long way, coats the pasta evenly and also helps to prevent the strands of pasta from clumping together.</code> |
| <code>can you drink lemon water after brushing teeth?</code> | <code>DO NOT brush your teeth for at least 30 minutes after drinking the lemon water. Use a soft toothbrush and fluoridated toothpaste (fluoride toughens your enamel) and do not brush aggressively. Acid softens the enamel and makes it more prone to erosion during brushing.</code> |
| <code>is ui ux front end?</code> | <code>User experience (UX) design is centered around the satisfaction the user experiences with your software. Front-end development is the technical implementation of the software's user interface (UI). UI design is the graphical bridge that connects the two.</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: 5,025 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: 25.54 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 25.51 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>A railway station of the same name on the Golitsyno -- Minsk railway , is located in Moscow .</code> | <code>A railway station of the same name on the Golitsyno -- Minsk railway line is located in Moscow .</code> |
| <code>To calculate such a point mass , an integration is carried out over the entire range of the continuous variable , on the probability density of the random part .</code> | <code>In order to calculate such a point mass , an integration over the entire range of continuous size is carried out on the probability density of the random part .</code> |
| <code>It also has representation at the local and regional level .</code> | <code>It also has a representation at regional and local level .</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: 36,293 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: 29.83 tokens</li><li>max: 328 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 52.56 tokens</li><li>max: 479 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>An autotroph ( from Greek autos = self and trophe = nutrition ) is an organism that makes organic compounds from simple molecules .</code> | <code>The word autotroph comes from the Greek autos = self and trophe = nutrition , related to trephein = to make solid , congeal , thicken</code> |
| <code>Nonrenewable resources cannot be replaced as easily as it is consumed.</code> | <code>What type of resource cannot be replaced as easily as it is consumed?</code> |
| <code>The data included email addresses and passwords that had been stored without any protection, a security firm said.<br>Leaked Source said the massive cache of credentials dated from 2012 but had only now been leaked and put online.<br>And it had come from a hacker who had supplied security firms with 43 million user names from music service Last.fm.<br>Rambler has been described as the Russian equivalent of Yahoo as it offers email services as well as acting as a news and content hub for its users.<br>"We know about that database," said the service in a statement.<br>"It was leaked March 2014 and contained millions of accounts. Right after the accident we forced our users to change their passwords.<br>"We also have forbidden to use the previously used passwords for the same account."<br>Leaked Source broke the news about the breach and said it had verified some of the data with the help of Russian journalists. .<br>Leaked Source said passwords associated with login names had been stored with "no encryption or hashing". Instead, it said, they had been listed in plain text.<br>Analysis of the long list of passwords showed that "asdasd" was the most popular string, used by more than 723,000 people, it said.<br>The second most popular password among the 98 million users was "asdasd123".<br>In June this year, details of more than 100 million users of the Russian VK.com service were shared online.<br>Copies of the long list of login names and passwords was offered online at a price of one bitcoin (£456).</code> | <code>Login names and passwords for more than 98 million users of the Russian Rambler.ru email service have reportedly been stolen and put online.</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: 14.11 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.27 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.65 tokens</li><li>max: 19 tokens</li></ul> |
* Samples:
| anchor | entailment | negative |
|:--------------------------------------------------------------------------|:-------------------------------------------------------------|:--------------------------------------------------------------|
| <code>a bike leaning on a metal fence next to some flowing water. </code> | <code>A bicycle parked next to a flooded river</code> | <code>A bicycle parked far away from a flooded river.</code> |
| <code>A woman is painting a mural of a woman's face.</code> | <code>There is a woman painting.</code> | <code>There is no woman painting.</code> |
| <code>A woman sitting at a table while holding a pair of scissors.</code> | <code>A woman smiles and holds up a pair of scissors.</code> | <code>A woman frowns and puts down a pair of scissors.</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: 16.09 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 14.87 tokens</li><li>max: 28 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| <code>The field of study known as mathematics is called the language of science.</code> | <code>What field of study is called the language of science?</code> |
| <code>Roots grow in length and width from the primary and secondary meristem.</code> | <code>Roots grow in length and width from the primary and secondary what?</code> |
| <code>Muscle groups are controlled by the motor cortex .</code> | <code>Muscle groups are controlled by what mechanism?</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: 14 tokens</li><li>mean: 25.09 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 58 tokens</li><li>mean: 230.7 tokens</li><li>max: 342 tokens</li></ul> |
* Samples:
| summary | document |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>A diamond ring that belonged to former child star Shirley Temple is going up for auction next month at a starting price of $25m (£17.3m), Sotheby's says.</code> | <code>The auction house said the 9.54-carat "Fancy Deep Blue" ring was bought by Temple's father in 1940 for $7,210.<br>Temple, one of Hollywood's most popular stars in the 1930s, died in 2014 at the age of 85.<br>A private buyer bought the ring from her estate and is now putting it up for auction, Sotheby's said.<br>It is estimated to be worth up to $35 million.<br>Temple pursued a career in politics after leaving the entertainment industry, serving as US ambassador to Ghana and Czechoslovakia.<br>A selection of her belongings, including a dress worn at the 1935 Oscars, has previously been auctioned off by her family.</code> |
| <code>A cybersecurity researcher working for anti-virus firm Kaspersky Lab in Russia has been arrested.</code> | <code>Ruslan Stoyanov, a member of Kasperksy's investigations team, was arrested in December but news of his apprehension has only just surfaced.<br>He was arrested as part of an investigation into payments he allegedly received from foreign firms.<br>At Kaspersky, Mr Stoyanov helped look into hack attacks and breaches at Russian companies.<br>In a statement, Kaspkersy Lab said the arrest had nothing to do with his work for the security firm.<br>"Ruslan Stoyanov is under investigation for a period predating his employment at Kaspersky Lab," said the company in a statement. Mr Stoyanov joined Kaspersky in 2012.<br>It added: "We do not possess details of the investigation."<br>Prior to working for Kaspersky, Mr Stoyanov was employed at other security firms. From 2000 to 2006 he was a major in the Russian Ministry of Interior's Moscow cybercrime unit.<br>Information about the reasons for the arrest are scant, but one Russian newspaper linked it to a probe into Sergei Mikhailov - a senior official at Russia's FSB intelligence service.<br>Forbes reported that Mr Stoyanov has been arrested under Article 275 of Russia's criminal code which lets prosecutors charge people for treason for "providing financial, technical, advisory, or other assistance" to other countries or non-Russian organisations seen as hostile.</code> |
| <code>Scotland's Russell Knox won the Travelers Championship after Jim Furyk became the first player in PGA Tour history to shoot a round of 58.</code> | <code>Knox, 31, closed with a two-under 68 to beat Jerry Kelly by one shot.<br>Daniel Berger went into the final round as leader but carded a four-over-par 74 to end tied for fifth with Furyk, Robert Garrigus and Tyrone van Aswegen.<br>American Furyk, 46, carded 10 birdies and an eagle in his bogey-free round of 12 under par to finish on 11 under.<br>Knox, who also won in Shanghai in November 2015, becomes the fifth player to have multiple wins in the 2015-16 PGA Tour season.<br>The others are the current top three players in the world - Jason Day, Dustin Johnson and Jordan Spieth - and world number eight Adam Scott.<br>The victory lifts the Scot into contention for at least a wildcard place in the European Ryder Cup team for the biennial tournament against the United States, which takes place at the end of September.<br>"It's been an incredible year for me," said Knox.<br>"I keep believing in myself, I tell myself every day that I'm good enough to be up there and win tournaments. It's been an enjoyable ride."<br>Set to move inside the top 20 in the world rankings, he added that winning this tournament would make it difficult for European captain Darren Clarke "not to pick me".<br>Furyk, who was already one of only six men to have recorded a score of 59 for 18 holes, said: "A million and a half rounds played in the history of the PGA Tour and you look at the great names ahead of me.<br>"It's humbling. To stand alone at 58 is really a cool accomplishment."</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### sciq_pairs
* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 16.28 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 72.52 tokens</li><li>max: 400 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Internal resistance, or (electrical) resistance in general, involves the resistance of the flow of what?</code> | <code>Internal Resistance As noted before, a 12-V truck battery is physically larger, contains more charge and energy, and can deliver a larger current than a 12-V motorcycle battery. Both are lead-acid batteries with identical emf, but, because of its size, the truck battery has a smaller internal resistance r . Internal resistance is the inherent resistance to the flow of current within the source itself. Figure 21.9 is a schematic representation of the two fundamental parts of any voltage source. The emf (represented by a script E in the figure) and internal resistance r are in series. The smaller the internal resistance for a given emf, the more current and the more power the source can supply.</code> |
| <code>If a solute is a gas, increasing the temperature will do what?</code> | <code>If a solute is a gas, increasing the temperature decreases its solubility. For example, less carbon dioxide can dissolve in warm ocean water than in cold ocean water.</code> |
| <code>What are usually planted in rows with bare soil in between the rows?</code> | <code>The problem doesn’t stop with plowing. Crops are usually planted in rows with bare soil in between the rows. In places where crops grow only during part of the year, the land may be bare for a few months.</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### qasc_pairs
* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 11.02 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 33.07 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What kind of organism use cellulose for their cell walls?</code> | <code>Plants use cellulose for their cell walls.. If the plant is green, it is a producer. <br> Producers use cellulose for their cell walls.</code> |
| <code>Energy enters what in the form of sunlight or chemical compounds.</code> | <code>Energy enters ecosystems in the form of sunlight or chemical compounds.. Biomes are global ecosystems. <br> Energy enters biomes in the form of sunlight or chemical compounds.</code> |
| <code>What does heat and pressure change into natural gas?</code> | <code>heat and pressure change the remains of prehistoric living things into natural gas. Dinosaurs and Other Prehistoric Creatures Dinosaurs are just one group of prehistoric animals. <br> heat and pressure change the remains of dinosaurs into natural gas</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: 8.45 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 72.06 tokens</li><li>max: 227 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how long is flight from vegas to california</code> | <code>Flying time from Las Vegas, NV to Los Angeles, CA. The total flight duration from Las Vegas, NV to Los Angeles, CA is 41 minutes. This is the average in-air flight time (wheels up to wheels down on the runway) based on actual flights taken over the past year, including routes like LAS to LAX.</code> |
| <code>tangible net worth calculation meaning</code> | <code>A measure of the physical worth of a company, which does not include any value derived from intangible assets such as copyrights, patents and intellectual property. Tangible net worth is calculated by taking a firm's total assets and subtracting the value of all liabilities and intangible assets.Next Up.REAKING DOWN 'Tangible Net Worth'. In terms of a consumer, tangible net worth is the sum of all your tangible assets (cash, home, cars, etc) less any liabilities you may have.</code> |
| <code>who is father ferdinand</code> | <code>Ferdinand does indeed fall in love with Prospero's daughter Miranda, aided by the magic of Ariel. Prospero does also have a plan for Ferdinand, which is for him to marry his daughter and cement the reconciliation between Prospero, right Duke of Milan and Alonso, Ferdinand's father and King of Milan. It is interesting to note that Ferdinand is presented as more passive than his romantic counterpart, Miranda.</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### nq_pairs
* Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.92 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 133.38 tokens</li><li>max: 416 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who owns the rights to guns and roses</code> | <code>Guns N' Roses Slash was replaced by Nine Inch Nails touring guitarist Robin Finck in January 1997, who signed a two-year contract with the band in August 1997, making him an official member.[130] Finck was originally recommended by Matt Sorum to Rose a year earlier as a possible second guitarist to complement Slash.[122] Slash's departure was followed shortly thereafter by Matt Sorum in April 1997, who was fired by Rose after getting in an argument about Tobias's inclusion in the band.[131] Sorum later stated Tobias was the "Yoko Ono of Guns N' Roses".[122] McKagan was the last of the Appetite lineup to leave, resigning as bassist in August 1997.[132] McKagan had recently become a father and wrote about his decision to leave in his autobiography, stating "Guns had been paying rent on studios for three years now—from 1994 to 1997—and still did not have a single song. The whole operation was so erratic that it didn't seem to fit with my hopes for parenthood, for stability."[132] McKagan was replaced later that year by former Replacements bassist Tommy Stinson.[133] An actual break-up of Guns N' Roses never occurred, as new players were brought in as the old ones left. Rose reportedly purchased the full rights to the Guns N' Roses name in 1997.[129][134] Slash claimed he and bandmates signed over the name in duress, stating "Axl refused to go onstage one night during the Use Your Illusion tour in 1992 unless the band signed away the name rights to the band. Unfortunately, we signed it. I didn't think he'd go on stage otherwise."[135] Rose denied the claim, saying "(it) Never happened, all made up, fallacy and fantasy. Not one single solitary thread of truth to it. Had that been the case I would have been cremated years ago legally, could've cleaned me out for the name and damages. It's called under duress with extenuating circumstances."[135]</code> |
| <code>what is red hot chili peppers otherside about</code> | <code>Otherside "Otherside" refers to former band member Hillel Slovak, who died of a heroin overdose on June 25, 1988. The song talks about his struggles from this addiction.[1]</code> |
| <code>when do you have to pay death duties</code> | <code>Estate tax in the United States If an asset is left to a spouse or a federally recognized charity, the tax usually does not apply. In addition, a maximum amount, varying year by year, can be given by an individual, before and/or upon their death, without incurring federal gift or estate taxes:[2] $5,340,000 for estates of persons dying in 2014[3] and 2015,[4] $5,450,000 (effectively $10.90 million per married couple) for estates of persons dying in 2016.[5] Because of these exemptions, it is estimated that only the largest 0.2% of estates in the U.S. will pay the tax.[6]</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: 7 tokens</li><li>mean: 17.31 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 206.09 tokens</li><li>max: 385 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Which musical features the song Sit Down You’re Rockin’ The Boat?</code> | <code>GUYS & DOLLS (Broadway) - "Sit Down, You're Rockin' the Boat" [LIVE @ The 2009 Tony Awards] - YouTube GUYS & DOLLS (Broadway) - "Sit Down, You're Rockin' the Boat" [LIVE @ The 2009 Tony Awards] 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 Jun 19, 2013 The cast of the Broadway revival of the musical GUYS & DOLLS, perform the number "Sit Down, You're Rockin' the Boat" live at the 2009 Tony Awards Category</code> |
| <code>"John Wayne played the lead role of whom in the 1958 film ""The Conqueror""?"</code> | <code>The movie so toxic it killed John Wayne: the tragedy of The Conqueror The movie so toxic it killed John Wayne: the tragedy of The Conqueror John Wayne and Susan Hayward in The Conqueror Credit: Rex Chris Bell 17 January 2017 • 6:24pm The Duke playing Genghis Khan in yellowface, filming on a nuclear testing ground? Chris Bell on why nobody escaped the fallout from The Conqueror Towards the end of his life, Howard Hughes – the billionaire tycoon, aviator and filmmaker – had become a recluse. Locked in the penthouse suite at his Xanadu Princess Resort hotel in the Bahamas, he refused to bathe, cut his nails or hair, use a toilet or even open the curtains. Instead, he would sit for hours in his darkened bedroom, naked except for a pink hotel napkin, eating nothing but chocolate bars and chicken, surrounded by dozens of Kleenex boxes that he continuously stacked and rearranged. But another ritual obsession would come dominate his final few months in 1976: two movies, played continually via a projector on the wall, that he watched over and over again. The first was his favourite film , Ice Station Zebra – Rock Hudson’s tense... Premium</code> |
| <code>Which country has won the most soccer World Cups?</code> | <code>Brazil may have won the most World Cup titles, but Germany has been the most consistent team | For The Win Brazil may have won the most World Cup titles, but Germany has been the most consistent team Brazil may have won the most World Cup titles, but Germany has been the most consistent team By Micah Peters July 13, 2014 7:22 pm Follow @micahpeters_ 6.1k shares Follow @micahpeters_ Germany just became the third country to win four World Cup titles, behind Italy, who also has four, and the most winningest, Brazil, which has 5. Brazil may have won 5 World Cup titles, but throughout the competition’s long history, Germany has been more consistent. They have never been eliminated in the group stage, and have consistently featured in the later stages. Since Germany began playing as a unified country in 1994, they have not gone out before the third round of the competition. #GER in WC: '54 Win '58 SF '62 QF '66 Final '70 SF '74 Win '78 QF '82 Final '86 Final '90 Win '94 QF '98 QF '02 Final '06 SF '10 SF '14 Win — The Football Café (@thefootballcafe) July 13, 2014 They’re also the first European team to win a World Cup title on South American soil.</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: 11.65 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 57.55 tokens</li><li>max: 129 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>do nz residents need a visa to work in australia?</code> | <code>Most NZ citizens can visit, live and work in Australia without applying for a tourist or work visa. NZ permanent residents need to apply for visas to Australia.</code> |
| <code>is clep testing worth it?</code> | <code>Money is a valuable resource, but time is even more valuable. CLEP tests can help you get a degree much faster than the traditional college path. The conventional amount of time to get an undergraduate degree is 4 years, though it can take many students as long as six years.</code> |
| <code>does someone know if you block them on iphone?</code> | <code>If you block someone, they do not receieve any notification that they have been blocked. The only way for them to know would be for you to tell them. Furthermore, if they send you an iMessage, it will say that it was delivered on their phone, so they won't even know that you're not seeing their message.</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: 31.63 tokens</li><li>max: 324 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 58.18 tokens</li><li>max: 416 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what height should a floating vanity be</code> | <code>If the piece measures 20 inches high and you want a height of 31 inches, you must mount it 11 inches above the floor. If you want the look of a floating sink with the entire vanity raised 12 inches or higher above the floor, you want a vanity of a smaller height.</code> |
| <code>Owner Steve Hayes has been actively trying to sell the club since October and four parties were interested back in February.<br>But in a statement the High Wycombe-based outfit said they were seeking fresh interest following stalled talks.<br>Hayes announced his desire to sell up after Wycombe District Council decided not to back plans for a new stadium.<br>Wasps chairman Mark Rigby added: "London Wasps has a long and successful history and is one of the best known brands in the game, and the board is convinced that the right backer exists.<br>"With a great squad in place and the excellent Dai Young at the helm, we believe we are set to make a strong impact next season.<br>"Time is however short and we urgently need a new investor or consortium to back this belief."<br>The statement said the board, after independent advice, could confirm that London Wasps Holdings Limited remained a going concern.<br>Wasps are enduring a torrid domestic season. They have won just two of their last 12 Premiership fixtures and sit second from bottom in the table.<br>In addition Hayes, who also owns League One football club Wycombe Wanderers, is currently<br>The 50-year-old businessman was one of two men arrested in February as part of Operation Tuleta, the investigation running alongside Operation Weeting, which was set up to probe alleged law-breaking at News International.<br>He is currently on bail until June, subject to further enquiries.</code> | <code>An 'imminent takeover' of London Wasps has fallen through, the Premiership strugglers have confirmed.</code> |
| <code>can apple cider vinegar cure urinary infection?</code> | <code>The takeaway Apple cider vinegar may have many health benefits, but it's not a cure for UTIs. If you have a UTI, make an appointment with your doctor. A short course of medication should relieve your symptoms within a few days.</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
- `learning_rate`: 2e-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': 5e-06}
- `warmup_ratio`: 0.3
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa2-0.9B-ST-v1-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`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-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': 5e-06}
- `warmup_ratio`: 0.3
- `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-v1-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 | sciq pairs loss | trivia pairs loss | msmarco pairs loss | scitail-pairs-qa loss | openbookqa pairs loss | nq pairs loss | global dataset loss | vitaminc-pairs loss | scitail-pairs-pos loss | gooaq pairs loss | paws-pos loss | qasc pairs loss | xsum-pairs loss | negation-triplets loss | Qnli-dev_max_ap | allNLI-dev_max_ap | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:-----------------:|:------------------:|:---------------------:|:---------------------:|:-------------:|:-------------------:|:-------------------:|:----------------------:|:----------------:|:-------------:|:---------------:|:---------------:|:----------------------:|:---------------:|:-----------------:|:------------------------:|
| 0.0102 | 16 | 7.1882 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0205 | 32 | 9.1489 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0307 | 48 | 8.805 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0409 | 64 | 5.7489 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0512 | 80 | 4.7163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0614 | 96 | 3.176 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0716 | 112 | 2.034 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0818 | 128 | 1.1278 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0921 | 144 | 0.7996 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1004 | 157 | - | 0.1090 | 0.4664 | 0.7974 | 0.1105 | 0.7853 | 1.1498 | 0.4867 | 3.8214 | 0.0780 | 0.7758 | 0.0375 | 0.8442 | 0.1444 | 1.2527 | 0.6672 | 0.5230 | 0.8776 |
| 0.1023 | 160 | 0.54 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1125 | 176 | 0.6267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1228 | 192 | 0.401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1330 | 208 | 0.455 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1432 | 224 | 0.308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1535 | 240 | 0.2808 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1637 | 256 | 0.319 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1739 | 272 | 0.3241 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1841 | 288 | 0.2181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1944 | 304 | 0.3247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2008 | 314 | - | 0.0483 | 0.1067 | 0.1743 | 0.0014 | 0.4045 | 0.1814 | 0.2370 | 3.3627 | 0.0271 | 0.2038 | 0.0234 | 0.1129 | 0.0382 | 0.7010 | 0.7243 | 0.5918 | 0.9171 |
| 0.2046 | 320 | 0.233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2148 | 336 | 0.1946 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2251 | 352 | 0.2393 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2353 | 368 | 0.1476 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2455 | 384 | 0.1976 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2558 | 400 | 0.1139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2660 | 416 | 0.1986 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2762 | 432 | 0.2405 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2864 | 448 | 0.1519 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2967 | 464 | 0.1508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3012 | 471 | - | 0.0414 | 0.0961 | 0.0865 | 0.0004 | 0.3847 | 0.1033 | 0.1735 | 2.4167 | 0.0062 | 0.1280 | 0.0252 | 0.0391 | 0.0329 | 0.6828 | 0.7293 | 0.6023 | 0.9200 |
| 0.3069 | 480 | 0.1457 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3171 | 496 | 0.1086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3274 | 512 | 0.1412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3376 | 528 | 0.1538 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3478 | 544 | 0.1013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3581 | 560 | 0.1007 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3683 | 576 | 0.0853 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3785 | 592 | 0.0696 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3887 | 608 | 0.1468 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3990 | 624 | 0.1314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4015 | 628 | - | 0.0295 | 0.0647 | 0.0976 | 0.0001 | 0.3722 | 0.0893 | 0.2361 | 3.2355 | 0.0099 | 0.1432 | 0.0264 | 0.0721 | 0.0172 | 0.6764 | 0.7428 | 0.6036 | 0.9153 |
| 0.4092 | 640 | 0.149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4194 | 656 | 0.1402 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4297 | 672 | 0.1056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4399 | 688 | 0.0932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4501 | 704 | 0.0534 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4604 | 720 | 0.2175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4706 | 736 | 0.1107 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4808 | 752 | 0.2301 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4910 | 768 | 0.2317 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5013 | 784 | 0.1084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5019 | 785 | - | 0.0324 | 0.0932 | 0.1156 | 0.0001 | 0.4128 | 0.1071 | 0.2526 | 3.9009 | 0.0121 | 0.1237 | 0.0254 | 0.0729 | 0.0098 | 0.6337 | 0.7422 | 0.6029 | 0.9171 |
| 0.5115 | 800 | 0.0799 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5217 | 816 | 0.1466 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5320 | 832 | 0.1208 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5422 | 848 | 0.1021 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5524 | 864 | 0.1391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5627 | 880 | 0.185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5729 | 896 | 0.1108 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5831 | 912 | 0.0926 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5934 | 928 | 0.157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6023 | 942 | - | 0.0307 | 0.0858 | 0.0844 | 0.0025 | 0.3581 | 0.1235 | 0.1544 | 2.8148 | 0.0050 | 0.1563 | 0.0268 | 0.0460 | 0.0177 | 0.5489 | 0.7193 | 0.6065 | 0.9218 |
| 0.6036 | 944 | 0.126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6138 | 960 | 0.1416 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6240 | 976 | 0.157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6343 | 992 | 0.076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6445 | 1008 | 0.0956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6547 | 1024 | 0.1297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6650 | 1040 | 0.1673 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6752 | 1056 | 0.0801 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6854 | 1072 | 0.1508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6957 | 1088 | 0.082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7027 | 1099 | - | 0.0361 | 0.0896 | 0.0879 | 0.0001 | 0.4053 | 0.0798 | 0.3097 | 4.5101 | 0.0260 | 0.1373 | 0.0255 | 0.0795 | 0.0101 | 0.6732 | 0.7434 | 0.5941 | 0.9084 |
| 0.7059 | 1104 | 0.112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7161 | 1120 | 0.0565 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7263 | 1136 | 0.1297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7366 | 1152 | 0.1792 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7468 | 1168 | 0.1376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7570 | 1184 | 0.1362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7673 | 1200 | 0.1589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7775 | 1216 | 0.0846 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7877 | 1232 | 0.1241 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7980 | 1248 | 0.1532 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8031 | 1256 | - | 0.0304 | 0.0667 | 0.1060 | 0.0003 | 0.3666 | 0.1305 | 0.2140 | 3.1243 | 0.0114 | 0.2109 | 0.0277 | 0.0328 | 0.0213 | 0.5495 | 0.7479 | 0.5907 | 0.9194 |
| 0.8082 | 1264 | 0.0859 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8184 | 1280 | 0.0872 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8286 | 1296 | 0.0685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8389 | 1312 | 0.0729 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8491 | 1328 | 0.0679 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8593 | 1344 | 0.0752 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8696 | 1360 | 0.1651 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8798 | 1376 | 0.0975 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8900 | 1392 | 0.166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9003 | 1408 | 0.079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9035 | 1413 | - | 0.0356 | 0.0784 | 0.0609 | 0.0003 | 0.4281 | 0.0720 | 0.2313 | 3.4939 | 0.0141 | 0.2300 | 0.0268 | 0.0522 | 0.0061 | 0.5946 | 0.7379 | 0.6052 | 0.9102 |
| 0.9105 | 1424 | 0.09 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9207 | 1440 | 0.0777 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9309 | 1456 | 0.1623 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9412 | 1472 | 0.08 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9514 | 1488 | 0.0628 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9616 | 1504 | 0.1695 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9719 | 1520 | 0.0715 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9821 | 1536 | 0.1493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9923 | 1552 | 0.0431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0026 | 1568 | 0.0549 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0038 | 1570 | - | 0.0429 | 0.0583 | 0.0681 | 0.0019 | 0.4316 | 0.1454 | 0.1614 | 2.8374 | 0.0053 | 0.0735 | 0.0250 | 0.0282 | 0.0120 | 0.5641 | 0.7139 | 0.6081 | 0.9289 |
| 1.0128 | 1584 | 0.102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0230 | 1600 | 0.0806 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0332 | 1616 | 0.0643 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0435 | 1632 | 0.2551 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0537 | 1648 | 0.1509 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0639 | 1664 | 0.0928 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0742 | 1680 | 0.1388 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0844 | 1696 | 1.2414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0946 | 1712 | 4.1558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1042 | 1727 | - | 0.0589 | 0.2021 | 9.6693 | 0.6669 | 3.1183 | 1.2794 | 1.6158 | 3.3692 | 0.0116 | 5.4553 | 0.0264 | 4.7383 | 0.0407 | 0.9119 | 0.7052 | 0.5709 | 0.9119 |
| 1.1049 | 1728 | 1.8742 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1151 | 1744 | 1.7176 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1253 | 1760 | 0.3091 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1355 | 1776 | 0.3178 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1458 | 1792 | 0.173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1560 | 1808 | 0.1028 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1662 | 1824 | 0.1533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1765 | 1840 | 0.2395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1867 | 1856 | 0.2036 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1969 | 1872 | 0.2104 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2046 | 1884 | - | 0.0323 | 0.0504 | 0.0626 | 0.0001 | 0.3769 | 0.0541 | 0.1662 | 2.7120 | 0.0100 | 0.0981 | 0.0282 | 0.0162 | 0.0219 | 0.5891 | 0.7460 | 0.5991 | 0.9239 |
</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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