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---
base_model: microsoft/deberta-v3-small
datasets:
- jinaai/negation-dataset-v2
- 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
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- 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:226010
- loss:CachedGISTEmbedLoss
widget:
- source_sentence: what is the common lifespan of a star
sentences:
- Mites can leave bites that look like they came from bed bugs (see these pictures
of bed bug bites), but not all mites are the same, so let me quickly explain.
In fact, there are almost 46,000 species of mites, but only a few bite humans!
They are the Northern Fowl Mite, Tropical Rat Mite, and Itch or Scabies Mite.
- Cost of Cardiac Catheterization Procedures Any type of cardiac care in the United
States is growing increasingly pricey. A cardiac catheterization procedure, depending
on location, may range in price between $2,400 and $4,000 in the United States.
- "Lifespans for main sequence stars have a vast range. Whilst our Sun will spend\
\ 10 billion years on the main sequence, a high-mass, ten solar-mass (10 M Sun)\
\ star will only last 20 million years (2.0Ã\x97 10 7 years) on the main sequence.A\
\ star with a only half the mass of Sun can spend 80 billion years on the main\
\ sequence.tars are composed almost entirely of hydrogen and helium. A star such\
\ as our Sun is about 73% hydrogen by mass and 25% helium. If determined by number\
\ of nuclei then it is 92% hydrogen and 7.8% helium. The remaining 2% by mass\
\ or 0.2% by number is all the heavier elements."
- source_sentence: More than 169 countries had reported over 212,000 COVID-19 cases
before March 19 , 2020 .
sentences:
- As of 23 March , more than 341,000 cases of COVID-19 have been reported in 192
countries and territories , resulting in more than 14,700 deaths and 99,000 recoveries
.
- As of 21 March , more than 278,000 cases of COVID-19 have been reported in over
186 countries and territories , resulting in more than 11,500 deaths and 92,000
recoveries. virus seems to mostly spread between people via respiratory droplets
.
- As of 18 March 2020 , more than 212,000 cases of COVID-19 have been reported in
at least 170 countries and territories , with major outbreaks in China , Iran
and the European Union .
- source_sentence: 'The memory walk saw participants gather at Bents Park in South
Shields on Saturday and travel 7km (4.3miles) along a coastal route.
The Alzheimer''s Society said it was the biggest event of its kind it had staged
in the north-east of England.
Organisers said the number of participants almost doubled that of last year''s
event.
About 35,000 people in the region have dementia, according to the charity.'
sentences:
- More than 4,500 people have taken part in a charity event raising funds for the
fight against Alzheimer's disease.
- Gareth Southgate should be appointed England manager "as soon as possible" and
be given the same contract as predecessor Sam Allardyce, says former Three Lions
defender Danny Mills.
- The owners of a Gwynedd skip hire business have been jailed for illegally storing
waste.
- source_sentence: Electrical energy can be converted into kinetic energy and heat
energy by an electric motor.
sentences:
- Solution is the term for a homogeneous mixture of two or more substances.
- Solution is the term for a homogeneous mixture of two or more substances.
- Electric motors transform electrical energy into kinetic energy.
- source_sentence: who did ben assault in home and away
sentences:
- List of Home and Away characters (2017) Ben and Maggie learn that Ziggy has been
dating Brody in secret and they disapprove of the relationship. Ziggy leaves the
house and Ben tells her not to come back. He apologises to her the next day, but
does not accept her relationship with Brody, so Ziggy refuses to come home. Brody
later breaks up with her. Ben begins making surf boards to sell at the pier. Ben
finds Coco convulsing in the garden and he and Maggie learn she has bulimia. Ziggy
later leaves home. Days later, Ben sees her with Brody, who is attempting to bring
her home, and punches him in the face. Olivia Fraser Richards (Raechelle Banno)
tells Sergeant Phillip McCarthy (Nicholas Cassim) and Ben is arrested. McCarthy
and Kat Chapman (Pia Miller) learns he has a criminal record for assaulting his
brother. Ben insults Kat, which leads to him being charged. Maggie secures a loan
to get him out on bail. Maggie's mother, Diana (Sarah Chadwick) came to Summer
Bay to visit the family and Diana told Ben and Maggie that she is the one who
bailed Ben out of jail.
- 'Stone (unit) The name "stone" derives from the use of stones for weights, a practice
that dates back into antiquity. The Biblical law against the carrying of "diverse
weights, a large and a small"[7] is more literally translated as "you shall not
carry a stone and a stone (אבן ואבן), a large and a small". There was no standardised
"stone" in the ancient Jewish world,[8] but in Roman times stone weights were
crafted to multiples of the Roman pound.[9] Such weights varied in quality: the
Yale Medical Library holds 10 and 50-pound examples of polished serpentine,[10]
while a 40-pound example at the Eschborn Museum (see right) is made of sandstone.[11]'
- Bad Things (Machine Gun Kelly and Camila Cabello song) "Bad Things" is a song
by American rapper Machine Gun Kelly and Cuban-American singer Camila Cabello.
The song was released on October 14, 2016 and was produced by The Futuristics.
Its music video was directed by Hannah Lux Davis and premiered on December 1,
2016. The song features an interpolation of Fastball's 1999 single "Out of My
Head". The single peaked at number four on the US Billboard Hot 100.
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-small
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.846466596875356
name: Pearson Cosine
- type: spearman_cosine
value: 0.8904026640082261
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8784788599905751
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8827673246412652
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8775593452462649
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8810458375755512
name: Spearman Euclidean
- type: pearson_dot
value: 0.8480375809921659
name: Pearson Dot
- type: spearman_dot
value: 0.8705808575512824
name: Spearman Dot
- type: pearson_max
value: 0.8784788599905751
name: Pearson Max
- type: spearman_max
value: 0.8904026640082261
name: Spearman Max
- task:
type: triplet
name: Triplet
dataset:
name: NLI v2
type: NLI-v2
metrics:
- type: cosine_accuracy
value: 1.0
name: Cosine Accuracy
- type: dot_accuracy
value: 0.0
name: Dot Accuracy
- type: manhattan_accuracy
value: 1.0
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 1.0
name: Euclidean Accuracy
- type: max_accuracy
value: 1.0
name: Max Accuracy
- task:
type: binary-classification
name: Binary Classification
dataset:
name: VitaminC
type: VitaminC
metrics:
- type: cosine_accuracy
value: 0.578125
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.787956714630127
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.6577540106951871
name: Cosine F1
- type: cosine_f1_threshold
value: 0.2885514795780182
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.4900398406374502
name: Cosine Precision
- type: cosine_recall
value: 1.0
name: Cosine Recall
- type: cosine_ap
value: 0.5535321831241837
name: Cosine Ap
- type: dot_accuracy
value: 0.5703125
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 331.16162109375
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6577540106951871
name: Dot F1
- type: dot_f1_threshold
value: 112.65699005126953
name: Dot F1 Threshold
- type: dot_precision
value: 0.4900398406374502
name: Dot Precision
- type: dot_recall
value: 1.0
name: Dot Recall
- type: dot_ap
value: 0.5538572188565111
name: Dot Ap
- type: manhattan_accuracy
value: 0.57421875
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 273.09307861328125
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.6577540106951871
name: Manhattan F1
- type: manhattan_f1_threshold
value: 495.97412109375
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.4900398406374502
name: Manhattan Precision
- type: manhattan_recall
value: 1.0
name: Manhattan Recall
- type: manhattan_ap
value: 0.5467997618408617
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.58203125
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 13.288713455200195
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.6594594594594595
name: Euclidean F1
- type: euclidean_f1_threshold
value: 22.802650451660156
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.4939271255060729
name: Euclidean Precision
- type: euclidean_recall
value: 0.991869918699187
name: Euclidean Recall
- type: euclidean_ap
value: 0.5502971665088517
name: Euclidean Ap
- type: max_accuracy
value: 0.58203125
name: Max Accuracy
- type: max_accuracy_threshold
value: 331.16162109375
name: Max Accuracy Threshold
- type: max_f1
value: 0.6594594594594595
name: Max F1
- type: max_f1_threshold
value: 495.97412109375
name: Max F1 Threshold
- type: max_precision
value: 0.4939271255060729
name: Max Precision
- type: max_recall
value: 1.0
name: Max Recall
- type: max_ap
value: 0.5538572188565111
name: Max Ap
---
# SentenceTransformer based on microsoft/deberta-v3-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2), [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) and [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2)
- [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)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("bobox/DeBERTa-small-ST-v1-toytest")
# Run inference
sentences = [
'who did ben assault in home and away',
"List of Home and Away characters (2017) Ben and Maggie learn that Ziggy has been dating Brody in secret and they disapprove of the relationship. Ziggy leaves the house and Ben tells her not to come back. He apologises to her the next day, but does not accept her relationship with Brody, so Ziggy refuses to come home. Brody later breaks up with her. Ben begins making surf boards to sell at the pier. Ben finds Coco convulsing in the garden and he and Maggie learn she has bulimia. Ziggy later leaves home. Days later, Ben sees her with Brody, who is attempting to bring her home, and punches him in the face. Olivia Fraser Richards (Raechelle Banno) tells Sergeant Phillip McCarthy (Nicholas Cassim) and Ben is arrested. McCarthy and Kat Chapman (Pia Miller) learns he has a criminal record for assaulting his brother. Ben insults Kat, which leads to him being charged. Maggie secures a loan to get him out on bail. Maggie's mother, Diana (Sarah Chadwick) came to Summer Bay to visit the family and Diana told Ben and Maggie that she is the one who bailed Ben out of jail.",
'Bad Things (Machine Gun Kelly and Camila Cabello song) "Bad Things" is a song by American rapper Machine Gun Kelly and Cuban-American singer Camila Cabello. The song was released on October 14, 2016 and was produced by The Futuristics. Its music video was directed by Hannah Lux Davis and premiered on December 1, 2016. The song features an interpolation of Fastball\'s 1999 single "Out of My Head". The single peaked at number four on the US Billboard Hot 100.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8465 |
| **spearman_cosine** | **0.8904** |
| pearson_manhattan | 0.8785 |
| spearman_manhattan | 0.8828 |
| pearson_euclidean | 0.8776 |
| spearman_euclidean | 0.881 |
| pearson_dot | 0.848 |
| spearman_dot | 0.8706 |
| pearson_max | 0.8785 |
| spearman_max | 0.8904 |
#### Triplet
* Dataset: `NLI-v2`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:--------|
| cosine_accuracy | 1.0 |
| dot_accuracy | 0.0 |
| manhattan_accuracy | 1.0 |
| euclidean_accuracy | 1.0 |
| **max_accuracy** | **1.0** |
#### Binary Classification
* Dataset: `VitaminC`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.5781 |
| cosine_accuracy_threshold | 0.788 |
| cosine_f1 | 0.6578 |
| cosine_f1_threshold | 0.2886 |
| cosine_precision | 0.49 |
| cosine_recall | 1.0 |
| cosine_ap | 0.5535 |
| dot_accuracy | 0.5703 |
| dot_accuracy_threshold | 331.1616 |
| dot_f1 | 0.6578 |
| dot_f1_threshold | 112.657 |
| dot_precision | 0.49 |
| dot_recall | 1.0 |
| dot_ap | 0.5539 |
| manhattan_accuracy | 0.5742 |
| manhattan_accuracy_threshold | 273.0931 |
| manhattan_f1 | 0.6578 |
| manhattan_f1_threshold | 495.9741 |
| manhattan_precision | 0.49 |
| manhattan_recall | 1.0 |
| manhattan_ap | 0.5468 |
| euclidean_accuracy | 0.582 |
| euclidean_accuracy_threshold | 13.2887 |
| euclidean_f1 | 0.6595 |
| euclidean_f1_threshold | 22.8027 |
| euclidean_precision | 0.4939 |
| euclidean_recall | 0.9919 |
| euclidean_ap | 0.5503 |
| max_accuracy | 0.582 |
| max_accuracy_threshold | 331.1616 |
| max_f1 | 0.6595 |
| max_f1_threshold | 495.9741 |
| max_precision | 0.4939 |
| max_recall | 1.0 |
| **max_ap** | **0.5539** |
<!--
## Bias, Risks and Limitations
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### Recommendations
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## Training Details
### Training Datasets
#### negation-triplets
* Dataset: [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2)
* Size: 26,000 training samples
* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | entailment | negative |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 22.32 tokens</li><li>max: 124 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.05 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.36 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
| anchor | entailment | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------|
| <code>BraÅ ov is part of the Transylvania area .</code> | <code>Like many other cities in Transylvania , BraÅ ov is also home for a significant ethnic Hungarian minority .</code> | <code>Like many other cities in Transylvania, BraÅ ov is also home for a significant ethnic Romanian majority.</code> |
| <code>If some of the principles of supersymmetry are correct , it is possible to recreate these superparticles with particle accelerators . This attempt could prove or disprove the ideas of supersymmetry .</code> | <code>It is possible to have more than one kind of supersymmetry transformation .</code> | <code>It is impossible to find even one kind of supersymmetry transformation.</code> |
| <code>A group of people running a bicycle race past a red building.</code> | <code>A bunch of people run past a building</code> | <code>A few people stay still near a building</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': 0.05}
```
#### vitaminc-pairs
* Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0)
* Size: 24,000 training samples
* Columns: <code>claim</code> and <code>evidence</code>
* Approximate statistics based on the first 1000 samples:
| | claim | evidence |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 17.43 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 37.41 tokens</li><li>max: 366 tokens</li></ul> |
* Samples:
| claim | evidence |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>By March 2016 , Baby was above the 8th most viewed YouTube video .</code> | <code>On March 5 , 2014 , `` Baby '' became the second video , after `` Gangnam Style '' , to receive 1 billion views on YouTube , and is the ninth most viewed video on the site , with over 1.33 billion views as of March 2016 .</code> |
| <code>The movie Think Like A Man had a rating of less than 50 % on Metacritic .</code> | <code>Early reviews for the film were mixed , the film currently holds a 47 % on Metacritic , indicating `` mixed or average reviews '' .</code> |
| <code>Animal consumption at the Huanan Seafood Market is suspected to be where the severe acute respiratory syndrome coronavirus 2 originated .</code> | <code>Animals sold for food are suspected to be the reservoir because many of first identified infected individuals were workers at the Huanan Seafood Market .</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': 0.05}
```
#### scitail-pairs-qa
* Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 14,237 training samples
* Columns: <code>sentence2</code> and <code>sentence1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence2 | sentence1 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 16.12 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.23 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| sentence2 | sentence1 |
|:---------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
| <code>Instruments that measure the angle of the slope of a volcano are called tilt meters.</code> | <code>Instruments that measure the angle of the slope of a volcano are called what?</code> |
| <code>Ultrasound, a diagnostic technology, uses high-frequency vibrations transmitted into any tissue in contact with the transducer.</code> | <code>What diagnostic technology uses high-frequency vibrations transmitted into any tissue in contact with the transducer?</code> |
| <code>Many species of birds in new england fly south for the winter months to find an environment with more food.</code> | <code>Which of the following best explains why many species of birds in New England fly south for the winter 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': 0.05}
```
#### scitail-pairs-pos
* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 8,600 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 23.36 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.79 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|
| <code>Anyway, what makes it possible for insects to walk on water is called Surface tension .</code> | <code>Surface tension is responsible for the fact that small insects can walk on water.</code> |
| <code>Elastic potential energy is the potential energy of an elastic object (for example a bow or a catapult) that is deformed under tension or compression (or stressed in formal terminology).</code> | <code>The term elastic potential energy is used to describe potential energy due to an object’s shape.</code> |
| <code>But, season or not, tornadoes can occur at any time of the year, if the weather conditions are right.</code> | <code>Tornadoes can occur in any.</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': 0.05}
```
#### xsum-pairs
* Dataset: xsum-pairs
* Size: 24,000 training samples
* Columns: <code>document</code> and <code>summary</code>
* Approximate statistics based on the first 1000 samples:
| | document | summary |
|:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 45 tokens</li><li>mean: 255.53 tokens</li><li>max: 487 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 25.67 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
| document | summary |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|
| <code>Jean Galligan, 82, from Dumfries, died when her car caught fire after it was involved in a collision with a Vauxhall Zafira on 14 May.<br>Police Scotland said Mrs Galligan was driving a red Daihatsu which was burnt out as a result of the accident on the A76 at Holywood.<br>Neither the driver nor the front seat passenger in the Zafira were injured.</code> | <code>A woman killed in a road accident near Dumfries has been named by police.</code> |
| <code>Police officers carried out arrests on Thursday in connection with alleged sex offences against females which occurred between 2008 and 2015.<br>Six men were charged with the rape of a girl under 16 as well as other sexual offences, while a seventh man was charged with conspiracy to rape.<br>Six men - all from Oxford - will appear before Oxford magistrates.<br>They are: Shabir Dogar, 22; Shabaz Khan, 23; Shohab Dogar, 23; Yasin Hamid, 20; Usman Iddris, 22; and Joseph Suraina, 22.<br>Waqas Hussain, 24, of no fixed abode, will appear at Oxford Magistrates' Court on 4 April.<br>Mr Hussain has also been charged with the attempted sexual assault of a girl under 13, as have Shabir Dogar and Shohab Dogar.<br>The raids were part of what the police are calling Operation Nautical.<br>A further 10 men were also arrested on Wednesday as part of the same operation.</code> | <code>Seven men have been charged in connection with a major child sexual exploitation investigation in Oxford.</code> |
| <code>In February 1957, 11-year-old Moira Anderson left her grandmother's house in Coatbridge to go to the shops but never returned.<br>Bus driver and convicted paedophile Alexander Gartshore, who died in 2006, is suspected of her murder.<br>Police are now looking at an area of Monkland Canal in an attempt to find her remains.<br>Moira Anderson was last seen on 23 February 1957 when she left on an errand during a heavy snowstorm, and boarded a Baxter's bus that was driven by Gartshore.<br>Later that year, he was jailed for raping a 17-year-old babysitter.<br>In 1999, convicted child abuser James Gallogley named his former friend Gartshore as Moira's murderer.<br>Gartshore's own daughter Sandra Brown was convinced he was the killer and campaigned to have him charged.<br>In 2014 prosecutors took the unusual step of announcing that Gartshore would have faced prosecution for the schoolgirl's murder if he were still alive.<br>In 1957 a witness reported seeing a tall man carrying a large, heavy sack towards the canal the morning after Moira disappeared but the possible sighting was not followed up.<br>Four years ago a grave in Old Monkland Cemetery in Coatbridge was exhumed as part of the search but no evidence was found that Moira was buried there.</code> | <code>A new search to find a schoolgirl who disappeared 60 years in North Lanarkshire has begun.</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': 0.05}
```
#### sciq_pairs
* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815)
* Size: 11,095 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: 16.67 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 82.57 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The punnett square shows the possible what, and their most likely ratios?</code> | <code>If the parents had four offspring, their most likely genotypes would be one BB, two Bb, and one bb. But the genotype ratios of their actual offspring may differ. That's because which gametes happen to unite is a matter of chance, like a coin toss. The Punnett square just shows the possible genotypes and their most likely ratios.</code> |
| <code>Which hormones work together to control the level of glucose in the blood?</code> | <code>The pancreas is located near the stomach. Its hormones include insulin and glucagon. These two hormones work together to control the level of glucose in the blood. Insulin causes excess blood glucose to be taken up by the liver, which stores the glucose as glycogen. Glucagon stimulates the liver to break down glycogen into glucose and release it back into the blood. The pancreas also secretes digestive enzymes into the digestive tract.</code> |
| <code>What is the“packet” of energy called that the nucleus emits during gamma decay?</code> | <code>Gamma rays are produced during gamma decay of an excited nucleus. During gamma decay, the nucleus emits a “packet” of energy called a gamma particle.</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': 0.05}
```
#### qasc_pairs
* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070)
* Size: 7,727 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: 11.36 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 34.66 tokens</li><li>max: 66 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Orbiting spacecraft reentering the Earth's atmosphere</code> | <code>friction causes an object to lose energy. Dear Ashlee, The heat in the reentry phase is due to friction between the spacecraft and the air.. Spacecraft that are reentering the atmosphere lose energy</code> |
| <code>The chance of you developing cancer depends most on your</code> | <code>Cancer genes can be inherited.. Genes are inherited from parents.. Developing cancer can depend on your parents</code> |
| <code>Why does a snake look for shelter in the winter?</code> | <code>shelter is used for protection by animals against weather. Many snakes seek shelter from the winter weather by holding up in dens.. Snakes use shelter to protect themselves in the winter</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
```
#### openbookqa_pairs
* Dataset: openbookqa_pairs
* Size: 4,522 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: 13.8 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.5 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| question | fact |
|:-----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| <code>What is animal competition?</code> | <code>if two animals eat the same prey then those animals compete for that pey</code> |
| <code>If you wanted to make a metal bed frame, where would you start?</code> | <code>alloys are made of two or more metals</code> |
| <code>Places lacking warmth have few what</code> | <code>cold environments contain few organisms</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': 0.05}
```
#### 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: 22,000 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.75 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 76.85 tokens</li><li>max: 201 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is hydrolysis in digestion</code> | <code>Digestion and Hydrolysis The digestion process relies upon hydrolysis to render the biochemical reactions that break down food. The digestive tract secretes enzymes, such as proteases, carbohydrases, nucleases and lipases that, along with water, catalyze the hydrolysis that releases various nutrients.</code> |
| <code>is cartier a good watch</code> | <code>CARTIER WATCHES. AuthenticWatches.com is one of the largest Internet Dealers for authentic Cartier watches. Cartier watches have no equal with respect to elegance and luxury. The name Cartier is synonymous with exquisite luxury and quality. Founded in 1847 by Louis-Fran ois Cartier, Cartier has led the industry in jewelry and watches alike. Cartier watches boast a large variety of design and functionality, yet maintain the utmost quality and sophistication in every series.</code> |
| <code>what vitamin is a precursor for a neurotransmitter</code> | <code>Tryptophan is an essential amino acid which is the precursor of serotonin. Serotonin is a brain neurotransmitter, platelet clotting factor and neurohormone found in organs throughout the body. Metabolism of tryptophan to serotonin requires nutrients such as vitamin B6, niacin and glutathione.ower doses, as little as 1000 to 2000 mg, have been found to be effective clinically, as well as experimentally in animals. The minimum daily requirement for adults of tyrosine and its precursor, phenylalanine, is 16 mg/kg a day or about 1000 mg total. Hence, 6 g is at least six times the minimum daily requirement.</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': 0.05}
```
#### 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: 22,000 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.92 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 132.65 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>friends episode with the turkey on the head</code> | <code>The One with All the Thanksgivings "The One with All the Thanksgivings" (also known as "The One with the Thanksgiving Flashbacks"[2]) is the eighth episode of the fifth season of Friends. It first aired on the NBC network in the United States on November 19, 1998. In the episode, the main characters spend Thanksgiving at Monica's (Courteney Cox) apartment and begin telling stories about their worst Thanksgivings: Chandler (Matthew Perry) learning of his parents' divorce, Phoebe (Lisa Kudrow) losing arms in past lives and Joey (Matt LeBlanc) having his head stuck in a turkey. Rachel (Jennifer Aniston) reveals Monica's worst Thanksgiving—accidentally cutting off Chandler's toe after he called her "fat" in their first encounter. When Monica begs Chandler to forgive her, he accidentally reveals that he loves her.</code> |
| <code>who played the first buford pusser in walking tall</code> | <code>Walking Tall (1973 film) Buford Pusser (Joe Don Baker), at his wife Pauline's (Elizabeth Hartman) behest, retires from the professional wrestling ring and moves back to Tennessee to start a logging business with his father, Carl Pusser (Noah Beery, Jr.).</code> |
| <code>when did the us let go of the philippines</code> | <code>History of the Philippines (1898–1946) The history of the Philippines from 1898 to 1946 covers the period of American rule in the Philippines and began with the outbreak of the Spanish–American War in April 1898, when the Philippines was still part of the Spanish East Indies, and concluded when the United States formally recognised the independence of the Republic of the Philippines on July 4, 1946.</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': 0.05}
```
#### 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: 20,000 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 18.89 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 456.48 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>With the symbol Wb what is the unit of magnetic flux?</code> | <code>schoolphysics ::Welcome:: HOME > AGE 16 - 19 > ELECTRICITY AND MAGNETISM > ELECTROMAGNETISM > FLUX AND FLUX DENSITY Flux and flux density To understand the meaning of magnetic flux (Φ) and magnetic flux density (B) think first about an ordinary bar magnet. Around the magnet there is a magnetic field and this gives a �flow of magnetic energy� around the magnet. It is this flow of energy that we call magnetic flux (Φ). We think of magnetic flux as flowing from the north pole of a magnet round to its south pole as shown by the arrows on the lines in the diagram. Looking at the diagram you should see that there is as much flux flowing �from the north pole� as there is �flowing into the south pole�. Magnetic flux is given the symbol Φ and is measured in units called Webers (Wb). However the amount of magnetic flux flowing through a given area will change from one point to another around the magnet and you can understand this by thinking about a loop of wire placed in the field at two different points (A and B). You can see that in position B there are a smaller number of magnetic field lines passing through the loop than there is when it is in position A. We call the amount of flux passing through a unit area at right angles to the magnetic field lines the flux density (B) at that point. Flux density is measured in Tesla (T) where 1 T = 1 Wbm-2 So: Flux (Φ) = Flux density (B) x area through which flux passes (A) Φ = BA If we now use more than one loop of wire, in others words a coil of N turns as shown in position C the flux flowing through the N turns is simply N times that flowing through the single loop. The quantity NΦ is called the the flux linkage for the coil at that point. Therefore:</code> |
| <code>The informal term for a gangster, especially belonging to the Mafia is?</code> | <code>goodfella - definition of goodfella in English | Oxford Dictionaries Definition of goodfella in English: goodfella A gangster, especially a member of a Mafia family. Example sentences ‘Saturday nights are reserved by the goodfellas for their wives, and Fridays, for the mistresses.’ ‘Bobby of course is the most sympathetic goodfella in the Soprano family.’ ‘If you notice a few goodfellas gawking every so often, don't reach for your pockets too quickly.’ ‘Compared to this, the goodfellas seem like sedentary sentimentalists, trapped animals rooted in dying communities, doomed territories.’ Pronunciation Which of the following is correct? She took over the business She brought over the business Which of the following is correct? The bad weather took famine The bad weather brought famine Which of the following is correct? The matter is taken before a jury The matter is brought before a jury Which of the following is correct? She took a deep breath She brought a deep breath Which of the following is correct? These pills should take the pain away These pills should bring the pain away Which of the following is correct? The memory took a smile to my face The memory brought a smile to my face Which of the following is correct? Take the pastry and roll it out Bring the pastry and roll it out Which of the following is correct? He took her coat away from her He brought her coat away from her Which of the following is correct? The crisis could bring down the regime The crisis could take down the regime Which of the following is correct? Will you bring me back some scent from Paris? Will you take me back some scent from Paris? You scored /10 practise again? Retry</code> |
| <code>What did the band S Club 7 change their name to when Paul Cattermole left in June 2002?</code> | <code>BBC News | MUSIC | Paul leaves S Club 7 Thursday, 28 March, 2002, 11:53 GMT Paul leaves S Club 7 Paul Cattermole, 25, is the group's oldest member S Club 7 star Paul Cattermole, who has announced he is quitting the band, is to join a heavy metal rock group. The band will continue as a six-piece under the name S Club, and Cattermole will remain with them to work on their fourth TV series. He will also perform at the Queen's Golden Jubilee concert at Buckingham Palace in June. They were founded and managed by Spice Girls svengali Simon Fuller But Cattermole is to switch styles by joining Skua, a group of old school friends playing nu-metal music. He told the Sun newspaper: "I want a change musically." No new release is likely for some time - as Skua do not have a record deal. Cattermole's departure came as his former bandmates re-signed their deal with both their management company and record label, which will make each of the members a millionaire. An S Club spokeswoman described the split as amicable. 'Rumour' Aged 25, Cattermole is the oldest member of the group, and is currently dating S Club's Hannah Spearitt. S Club's Rachel will be part of the newly named S Club In January, the band denied claims they were set to split, issuing a statement that there was "no truth in the rumour". They released their first single, Bring It All Back, in June 1999, and it went straight to number one. In 2000 they started work on their second TV series, LA7, which saw them move to Los Angeles. The series went on to become a big hit on American TV. The group won best British newcomer at the Brit Awards in March 2000 and hit the top spot in December with Never Had A Dream Come True, which saw profits going to the BBC's Children In Need charity. 'Stupid' They had two more number ones in 2001 with Don't Stop Movin' and their second Children In Need song, Have You Ever. The group also hit the headlines in March when three of their members were arrested for possessing cannabis. Bradley McIntosh, Paul Cattermole and Jon Lee were found carrying the drug in London's West End. They later apologised for the incident to their fans, saying they had been "stupid". The four other members of the band are Hannah Spearritt, Jo O'Meara, Rachel Stevens and Tina Barrett. The group are the brainchild of former Spice Girls manager Simon Fuller.</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': 0.05}
```
#### gooaq_pairs
* Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 20,000 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.52 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 57.68 tokens</li><li>max: 121 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>gyan is called in english?</code> | <code>Gyan (Sanskrit), a Sanskrit word that roughly translates to 'knowledge' in English.</code> |
| <code>are mud baths good for dogs?</code> | <code>Mud has many benefits for your dog. It can soothe irritations by removing dead irritated skin. It can soothe hot spots. The mud applied to your dogs coat during the bath can help moisturize the skin and remove dandruff.</code> |
| <code>how many calories do you burn doing interval running?</code> | <code>Cost in Calories exerciser who performs sprint intervals at a speed of 12 mph for 20 minutes expends roughly 608 calories. To demonstrate how body weight affects calorie expenditure, a 250-lb. person burns approximately 845 calories using the identical sprint speed and workout duration.</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': 0.05}
```
#### 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: 21,829 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 25.38 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 25.41 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>After some protests from the girl 's father , the man of Douji and Hime was killed before Suzu and her father 's corpse were consumed by the Orochi .</code> | <code>After some protesting from the girl 's father , the man was killed by Douji and Hime before Suzu and her father 's corpse were consumed by the Orochi .</code> |
| <code>162 . Fighter Escadrille was a unit of the Łódź Army at the start of the Second World War . The unit was attached to the Polish Air Force .</code> | <code>At the beginning of the Second World War , the Fighter Escadrille was a unit of the Łódź army , which was attached to the Polish Air Force .</code> |
| <code>The first music video for the album filmed and edited for the song 'Trust You ' was made by Pierre Bouvier-Patron of Friends Studio , London .</code> | <code>The first music video for the album , filmed and edited for the song 'Trust You ' , was made by Pierre Bouvier-Patron of Friends Studio , London .</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': 0.05}
```
### Evaluation Datasets
#### vitaminc-pairs
* Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0)
* Size: 108 evaluation samples
* Columns: <code>claim</code> and <code>evidence</code>
* Approximate statistics based on the first 1000 samples:
| | claim | evidence |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 21.36 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 36.11 tokens</li><li>max: 79 tokens</li></ul> |
* Samples:
| claim | evidence |
|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Dragon Con had over 5000 guests .</code> | <code>Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell .</code> |
| <code>COVID-19 has reached more than 185 countries .</code> | <code>As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths .</code> |
| <code>In March , Italy had 3.6x times more cases of coronavirus than China .</code> | <code>As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China .</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
```
#### negation-triplets
* Dataset: [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2)
* Size: 64 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: 10 tokens</li><li>mean: 13.88 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 13.31 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 13.64 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| anchor | entailment | negative |
|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|
| <code>1 military jet fighter flying in formation alongside a 1 military propeller pilot. </code> | <code>The two planes are different in design, but flying in a similar flight pattern.</code> | <code>The two planes are identical in design, but flying in different flight patterns.</code> |
| <code>A random plane in the sky flying alone</code> | <code>An airplane flying high in the blue sky. </code> | <code>A helicopter flying low in the cloudy sky. </code> |
| <code>A picture of a white gas range with figurines above.</code> | <code>a white stove turned off with a digital clock</code> | <code>a black stove turned on with a digital clock</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': 0.05}
```
#### scitail-pairs-pos
* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 54 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: 20.81 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 10 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': 0.05}
```
#### xsum-pairs
* Dataset: xsum-pairs
* Size: 128 evaluation samples
* Columns: <code>document</code> and <code>summary</code>
* Approximate statistics based on the first 1000 samples:
| | document | summary |
|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 74 tokens</li><li>mean: 242.33 tokens</li><li>max: 374 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 25.18 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
| document | summary |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The region is already struggling to cope with a huge influx of migrants arriving from Tunisia.<br>Since January, at least 15,000 migrants have arrived, many of them landing on the tiny island of Lampedusa which is struggling to cope.<br>Thousands of people are living in basic camps on the island, leading to health concerns and rising local tensions.<br>"Until now the only migrants to arrive in Lampedusa were Tunisians," said Laura Boldrini a spokeswoman for the UN's refugee agency.<br>"This is the first boat coming from Libya with people fleeing the military escalation, the vendettas and the retaliation attacks," she said.<br>Overnight on Saturday, a boat carrying some 300 migrants was escorted by the Italian coastguard to Linosa, an even smaller island some 50 km (35 miles) north of Lampedusa.<br>The passengers were mostly Somalis, Eritreans and Ethiopians and included a woman who had just given birth - she and the baby were flown to Lampedusa for medical care.<br>Several other boats from Libya, each carrying hundreds more migrants, are expected to reach Italy within hours.<br>Officials on Lampedusa, which is less than 160km from the Tunisian coast, have moved thousands of migrants to reception centres on the mainland, but some 5,000 remain.<br>The island's mayor has said he is desperate for help to relieve pressure on the island's very limited resources. Local people have said they are afraid of an outbreak of disease in the camps.<br>The Italian government has appealed to the international community for help.</code> | <code>Boatloads of migrants fleeing fighting in Libya are beginning to arrive in southern Italy, say officials.</code> |
| <code>The Belgium midfielder was one of a handful of players Mourinho had deemed to have underperformed this season.<br>Mourinho has also been under scrutiny, with the champions 15th in the league, 14 points behind leaders Leicester.<br>"I don't have a problem with him. We hope we can win a lot of trophies together," said Hazard, 24.<br>"Maybe not this season because it will be difficult, but next season and on."<br>The Blues moved above Norwich into 15th with a 1-0 win over the Canaries on Saturday - it was only their fourth victory in 13 league games this season.<br>Hazard, who was recently linked with a move to Real Madrid, has yet to score for the Blues in 18 appearances in all competitions in 2015-16.<br>He managed 20 last season en route to winning the Professional Footballers' Association and Football Writers' Association player of the year awards.<br>On his own form, he said: "I didn't start the season well. I tried to find out why, but I don't know.<br>"Sometimes you don't know. You have to keep going. I gave everything in training, on the pitch when I played.<br>"I hope I can get a lot of form and try to help the team win games."</code> | <code>Eden Hazard has denied having a strained relationship with Chelsea boss Jose Mourinho and suggested he wants to stay at the Premier League champions.</code> |
| <code>Amadou Gallo Fall, the NBA's vice-president for Africa, told the BBC the centre would train boys and girls aged between 16 and 18.<br>He said the centre would be part of its global network of elite training academies.<br>Several Africans have played for top teams in the NBA league.<br>Mr Fall, who is originally from Senegal, said the pan-African academy would use its network to scout for players from around the continent.<br>He said the players would be given access to facilities and resources available to elite players including nutritionists, personal coaches and physiotherapists.<br>The centre will be in Thies, 60km (40 miles) east of the capital, Dakar.<br>Senegal's national teams - men and women - have traditionally been among the strongest in Africa.<br>Mr Fall said those who don't make it to the NBA would have other avenues, such as "other great leagues around the world, including the NBA development league or in US universities".<br>He said there were 14 African-born players on the NBA opening roster this year, including Senegal's Gorgui Dieng and Cameroon's Pascal Siakam.<br>He added the NBA had a long association with the continent, citing legendary players such as Hakeem Olajuwon, Manute Bol and Dikembe Mutombo.<br>"That generation has paved the way and they've inspired and ushered a significant number of other young players over the years, a lot of them from Senegal," he said.<br>The NBA launched three academy centres in China in October, one in India last month and is planning to open another global centre based in Australia.<br>The NBA held its first game in Africa in 1 August 2015 in the South African city of Johannesburg.</code> | <code>The US National Basketball Association (NBA) has announced it will open its first African training academy in Senegal early next year.</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': 0.05}
```
#### sciq_pairs
* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 16.24 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 71.48 tokens</li><li>max: 375 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Water molds mostly live in water or moist?</code> | <code>Define physical change, and give examples of physical change.</code> |
| <code>By allowing blood levels of a hormone to be regulated within a narrow range, feedback loops contribute to maintaining what state?</code> | <code>Role of Feedback Loops The contribution of feedback loops to homeostasis will only be briefly reviewed here. Positive feedback loops are characterized by the release of additional hormone in response to an original hormone release. The release of oxytocin during childbirth is a positive feedback loop. The initial release of oxytocin begins to signal the uterine muscles to contract, which pushes the fetus toward the cervix, causing it to stretch. This, in turn, signals the pituitary gland to release more oxytocin, causing labor contractions to intensify. The release of oxytocin decreases after the birth of the child. The more common method of hormone regulation is the negative feedback loop. Negative feedback is characterized by the inhibition of further secretion of a hormone in response to adequate levels of that hormone. This allows blood levels of the hormone to be regulated within a narrow range. An example of a negative feedback loop is the release of glucocorticoid hormones from the adrenal glands, as directed by the hypothalamus and pituitary gland. As glucocorticoid concentrations in the blood rise, the hypothalamus and pituitary gland reduce their signaling to the adrenal glands to prevent additional glucocorticoid secretion (Figure 17.6).</code> |
| <code>What changes the chemical composition of a substance and can only occur through a chemical reaction?</code> | <code>Pure substances, such as compounds, can be separated through chemical changes. Chemical changes change the chemical composition of a substance and can only occur through a chemical reaction.</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': 0.05}
```
#### 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: 11.16 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 34.24 tokens</li><li>max: 55 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what code proteins?</code> | <code>Chromosomes contain genes, which code for proteins.. Chromosomes are composed of DNA and proteins.. genes code proteins</code> |
| <code>Furry animals grow thicker coats which has what impact on their survival?</code> | <code>staying warm has a positive impact on an animal 's survival. Furry animals grow thicker coats to keep warm in the winter.. Furry animals grow thicker coats which has a positive impact on their survival. </code> |
| <code>Erosion can be caused by </code> | <code>heavy rains cause flooding. Flooding is problematic because it causes erosion problems.. heavy rains cause erosion</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': 0.05}
```
#### openbookqa_pairs
* Dataset: openbookqa_pairs
* Size: 128 evaluation samples
* Columns: <code>question</code> and <code>fact</code>
* Approximate statistics based on the first 1000 samples:
| | question | fact |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 13.98 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.78 tokens</li><li>max: 28 tokens</li></ul> |
* Samples:
| question | fact |
|:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------|
| <code>The thermal production of a stove is generically used for</code> | <code>a stove generates heat for cooking usually</code> |
| <code>What creates a valley?</code> | <code>a valley is formed by a river flowing</code> |
| <code>when it turns day and night on a planet, what cause this?</code> | <code>a planet rotating causes cycles of day and night on that planet</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
```
#### 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: 8.68 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 72.57 tokens</li><li>max: 159 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what types of functions might you use for looking items up within your data? can you list any examples of where the formulas you chose might be useful?</code> | <code>Tip: Use MATCH instead of one of the LOOKUP functions when you need the position of an item in a range instead of the item itself. For example, you might use the MATCH function to provide a value for the row_num argument of the INDEX function.</code> |
| <code>ppt vehicle definition</code> | <code>A policy purchased by vehicle owners to mitigate costs associated with getting into an auto accident. Instead of paying out of pocket for auto accidents, people pay annual premiums to an auto insurance company; the company then pays all or most of the costs associated with an auto accident or other vehicle damage.</code> |
| <code>difference between integrated and dedicated graphics</code> | <code>Key Difference: Dedicated and Integrated Graphics Cards are two types of graphics cards. The main difference between two is that the integrated graphics card comes built in to the computer. Whereas, the dedicated graphics card is an external attachment that must be connected to the motherboard.</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': 0.05}
```
#### 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: 11.65 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 133.51 tokens</li><li>max: 299 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>when was everything that rises must converge written</code> | <code>Everything That Rises Must Converge Everything That Rises Must Converge is a collection of short stories written by Flannery O'Connor during the final decade of her life. The collection's eponymous story derives its name from the work of Pierre Teilhard de Chardin.[1][2] The collection was published posthumously in 1965 and contains an introduction by Robert Fitzgerald. Of the volume's nine stories, seven had been printed in magazines or literary journals prior to being collected. "Judgment Day" is a dramatically reworked version of "The Geranium," which was one of O'Connor's earliest publications and appeared in her graduate thesis at the University of Iowa. "Parker's Back," the collection's only completely new story, was a last-minute addition.</code> |
| <code>what are the creatures in the woods american horror story</code> | <code>American Horror Story: Asylum Dr. Arden is a former Nazi whose experiments have produced "Raspers", mutated former patients, who lurk in the woods surrounding the institution, and who are fed the flesh of dead patients. Dr. Thredson is assigned to evaluate Kit, who is accused of being the infamous serial killer 'Bloody Face' and believes his wife Alma (Britne Oldford) was abducted by aliens. Thredson also tries to "reform" Lana, who was an ambitious journalist attempting to expose Briarcliff's mistreatments of patients. She was in a relationship with Wendy (Clea Duvall), who was blackmailed by Sister Jude into committing Winters, before being killed by Bloody Face. Thredson helps Lana escape from the asylum, but she learns that Thredson is actually Bloody Face, and is kept prisoner. He rapes her and tries to kill her, but she manages to escape, only to end up back at Briarcliff. She later learns she is pregnant with Thredson's baby.</code> |
| <code>what does the pink panther movie have to do with the cartoon</code> | <code>The Pink Panther The first film in the series derives its name from the eponymous pink diamond that has an enormous size and value. The diamond is called the "Pink Panther" because the flaw at its centre, when viewed closely, is said to resemble a leaping pink panther. The phrase reappears in the title of the fourth film The Return of the Pink Panther, in which the theft of the diamond is again the centre of the plot. The phrase was used for all the subsequent films in the series, even when the jewel did not figure in the plot. It ultimately appeared in six of the eleven films.</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': 0.05}
```
#### 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>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: 19.75 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 58 tokens</li><li>mean: 452.12 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>In which country was Ursula Andrews born?</code> | <code>Ursula Andress - Biography - IMDb Ursula Andress Biography Showing all 59 items Jump to: Overview (3) | Mini Bio (1) | Spouse (1) | Trade Mark (4) | Trivia (34) | Personal Quotes (15) | Salary (1) Overview (3) 5' 5" (1.65 m) Mini Bio (1) The quintessential jet-set Euro starlet, Ursula Andress was born in the Swiss canton of Berne on March 19, 1936, one of six children in a strict German Protestant family. Although often seeming icily aloof, a restless streak early demonstrated itself in her personality, and she had an impetuous desire to explore the world outside Switzerland. (For instance, she was tracked down by Interpol for running away from boarding school at 17 years old.) The stunning young woman found work as an art model in Rome and did walk-ons in three quickie Italian features before coming to the Hollywood. At 19, she met fading matinée idol John Derek , who left his first wife and two children to marry Ursula in 1957, despite the fact that she only spoke a few words of English at the time, and persuaded the new bride to put her acting ambitions on hold for a few years. The year 1962 saw the virtually unknown Swiss beauty back on the set, playing a small role in the first movie version of Ian Fleming 's fanciful "James Bond" espionage novels, Dr. No (1962), opposite Sean Connery . Because her Swiss/German accent was so strong, Andress' entire performance had to be dubbed by a voiceover artist. Nevertheless, her striking beauty and smoldering screen presence made a strong impression on moviegoers, immediately establishing her as one of the most desired women in the world and as an ornament to put on-screen alongside some of the most bankable talent of the era, such as Elvis Presley in Fun in Acapulco (1963) and Dean Martin in 4 for Texas (1963). In 1965, she was one of several European starlets to co-star in What's New Pussycat (1965) -- a film that perhaps sums up mid-'60s pop culture better than any other -- written by Woody Allen , starring Allen and Peter Sellers , with music by Burt Bacharach , a title song performed by Tom Jones and much on-screen sexual romping. Andress appeared in many more racy-for-their time movies in both the United States and Europe from the mid-'60s to the late '70s, including The 10th Victim (1965), in which she wears a famously ballistic bra; The Blue Max (1966), where she is aptly cast as the sultry, insatiable wife of an aristocratic World War I German general; the James Bond satire Casino Royale (1967); the excellent crime caper Perfect Friday (1970); Red Sun (1971), as a foul-mouthed prostitute taken hostage by outlaws; The Sensuous Nurse (1975), as a bombshell nurse hired to titillate a doddering millionaire to death; the notorious The Mountain of the Cannibal God (1978), in which she is stripped and slathered in orange paint by a pair of nubiles; and The Fifth Musketeer (1979), in the role of King Louis XIV's alluring mistress. Unmarried since 1966 when she'd divorced Derek after falling in love with French actor Jean-Paul Belmondo on the Malaysian set of Up to His Ears (1965), Andress played the field for years, reportedly involved at various times with a host of others including (but by no means limited to) Ryan O'Neal , Marcello Mastroianni , Dennis Hopper and Fabio Testi . In 1979, she began what would be a long-term romance with Harry Hamlin , her handsome young co-star from Clash of the Titans (1981) (in which she was cast, predictably, as "Aphrodite"). While subsequently traveling in India, Andress' belly began to swell out of her clothing, and she felt very nauseous. What at first seemed a severe case of "Delhi Belly" turned out to be pregnancy, her first and only, at age 43. She and Hamlin named the child, who was born in 1980, Dimitri Hamlin . After the birth of her son, Andress scaled back her career, which now focused mostly on slight European films and occasional television roles, as she was raising Dimitri in Rome. Her relationship with Hamlin ended in 1983, and she last worked on a film in 2005. - IMDb Mini Biography By: Larry-115 Spouse (1) Seductive</code> |
| <code>What chemical element, symbol Cr, is named due its colourful/colorful compound effect?</code> | <code>Etymologies of element names | PlanetStar Wiki | Fandom powered by Wikia Etymologies of element names Share Ad blocker interference detected! Wikia is a free-to-use site that makes money from advertising. We have a modified experience for viewers using ad blockers Wikia is not accessible if you’ve made further modifications. Remove the custom ad blocker rule(s) and the page will load as expected. This is the list of 173 elements discussing their etymologies, including official, proposed, predicted, and made-up names. A subatomic particle neutron , since this element only contains neutron(s) in its nucleus . 1 Hydrogen H From the Latin hydor genes, derived from the Ancient Greek ὕδωρ γείνομαι (hydor geinomai), meaning "to beget water," because water is the most common and important hydrogen compound . 2 From the Greek ἥλιος ( Helios ), meaning " Sun " and also " the god of Sun " in mythology . 3 Lithium Li From the Greek λιθος (lithos), meaning "stone," because this element was discovered from a mineral while other common alkali metals (sodium and potassium) were discovered from plant tissue. 4 Beryllos, denoting " beryl ," which contains beryllium. 5 لاعقشا (buraq), derived from the Persian "بورون" (burah), referring to " borax ." 6 Carbon C From the French charbone, which in turn came from the Latin carbo, meaning "charcoal." (In German and Dutch , kohlenstoff and koolstof, respectively, both literally meaning " coal -stuff"). 7 N From the Latin nitrum genes, derived from the Greek νιτρον γείνομαι (nitron geinomai), meaning "native-soda ( niter ) forming." It is sometimes known as Azotum, which means "Ashdod", the English name of that element, and its symbol is sometimes known as "Az". 8 Oxygen O From the Greek οξύς γείνομαι (oxys geinomai), meaning "acid to bring forth," as he believed it to be an essential component of acids. 9 Fluorspar , one of its compounds. Fluor is the Latin for "flowing." 10 From the Greek νέος (neos), meaning "new." 11 Sodium Na From the English soda , used in names for sodium compounds such as caustic soda , soda ash , and baking soda . The symbol Na is from the Modern Latin name Natrium, derived from the Greek νιτρον (nítron), meaning "natural soda," a kind of salt. 12 From the Ancient Greek Μαγνήσια ( Magnesia ) (district in Thessaly ), where this element was discovered. 13 From the Latin alum , meaning "bitter salt." 14 From the Latin silex or silicis, meaning " flint ," a kind of stone. 15 Phosphorus P From the Greek φωσ φόρος (phós phoros), meaning "light bearer," because "white phosphorus" emits a faint glow upon exposure to oxygen. Phosphorus was an ancient name for the " planet Venus " as Phosphorus ( Morning Star ). 16 Sulfur S Almost certainly from the Arabic صفرا (sufra), "yellow," the bright color of the naturally occurring form. The word passed into Sanskrit गन्धक (sulvere or sulvari), the Latin "sulpur," the English "sulphur" or "sulfur," and also was commonly referred as "brimstone" in English translations of the Bible . 17 Chlorine Cl From the Greek χλώρος (chlorós), meaning "yellowish green" or "greenish yellow," because of the color of the gas. 18 Means "inactive" in Greek (literally "lazy"). 19 Potassium K From the English potash , meaning "pot-ash" ( potassium compound prepared from an alkali extracted in a pot from the ash of burnt wood or tree leaves). The symbol K is from this elment's Latin name, Kalium, derived from the Arabic القلي (al qalīy), meaning "calcined ashes." 20 Calcium Ca From the Latin calx, meaning "lime." Calcium was known as early as the first century when the Romans prepared lime as calcium oxide . 21 From the Latin Scandia, meaning " Scandinavia ." 22 Titanium Ti From the Greek τιτάν (titan), meaning " Earth ." Also from Titans , the first sons of Gaia in Greek mythology. 23 From Vanadis , a goddess in Scandinavian mythology , because of its beautifully multicolored chemical compounds. 24 From the Greek chroma, meaning " color ," because there are many colorful chromium compounds . 25 From the Latin magnes, meaning "magnet," for Magnetite or its magne</code> |
| <code>What religion is the Dalai Lama?</code> | <code>BBC - Religions - Buddhism: Dalai Lama Dalai Lama Last updated 2006-09-21 The institution of the Dalai Lama is a relatively recent one. There have been only 14 Dalai Lamas in the history of Buddhism. On this page The rôle of the Dalai Lama Potala Palace, the Dalai Lama's residence until 1959 The Dalai Lama is the head monk of Tibetan Buddhism and traditionally has been responsible for the governing of Tibet, until the Chinese government took control in 1959. Before 1959, his official residence was Potala Palace in Lhasa, the capital of Tibet. The Dalai Lama belongs to the Gelugpa tradition of Tibetan Buddhism, which is the largest and most influential tradition in Tibet. The institution of the Dalai Lama is a relatively recent one. There have been only 14 Dalai Lamas in the history of Buddhism, and the first and second Dalai Lamas were given the title posthumously. According to Buddhist belief, the current Dalai Lama is a reincarnation of a past lama who decided to be reborn again to continue his important work, instead of moving on from the wheel of life. A person who decides to be continually reborn is known as tulku. Buddhists believe that the first tulku in this reincarnation was Gedun Drub, who lived from 1391-1474 and the second was Gendun Gyatso. However, the name Dalai Lama, meaning Ocean of Wisdom, was not conferred until the third reincarnation in the form of Sonam Gyatso in 1578. The current Dalai Lama is Tenzin Gyatso. Tenzin Gyatso, 14th Dalai Lama, as a child © Choosing a Dalai Lama After the death of a Dalai Lama it has traditionally been the responsibility of the High Lamas of the Gelugpa Tradition and the Tibetan government to find his reincarnation. The High Lamas search for a boy who was born around the same time as the death of the Dalai Lama. It can take around two or three years to identify the Dalai Lama, and for the current, 14th Dalai Lama, it was four years before he was found. There are several ways in which the High Lamas might find out where the next reincarnation will be found. Dream One of the High Lamas may dream about some mark or location that will identify the boy. Smoke If the previous Dalai Lama was cremated, High Lamas will watch the direction of the smoke and search accordingly. Oracle Lake High Lamas go to a holy lake, called Lhamo Lhatso, in central Tibet and watch for a sign from the lake itself. This may be either a vision or some indication of the direction in which to search.The home and village of Tenzin Gyatso was identified in a vision from this lake. Once the High Lamas have located the home and the boy, they present a number of artefacts which they have brought with them in preparation, to the child. Amongst these artefacts are a number of items that belonged to the deceased Dalai Lama. If the boy chooses the items that belonged to the previous Dalai Lama, this is seen as a sign, in conjunction with all of the other indications, that the boy is a reincarnation. This procedure, however, as Tenzin Gyatso has said himself, is not set in stone; if two thirds of the Tibetan people wish to change the method of identifying the next reincarnation, this would be just as valid. The search for the Dalai Lama has usually been limited to Tibet, although the third tulku was born in Mongolia. However, as Tibet has been taken by the Chinese government, Tenzin Gyatso says that if he is reborn it will not be in a country run by the People's Republic of China, or any other country which is not free. In order to see this content you need to have both Javascript enabled and Flash installed. Visit BBC Webwise for full instructions Interestingly, Tenzin Gyatso has also expressed doubts over whether he will be reborn at all, suggesting the function of the Dalai Lama may be over. However, until Tibet is reunited with its spiritual leader, it seems likely that there will continue to be a Dalai Lama. Top Tenzin Gyatso, the 14th Dalai Lama Tenzin Gyatso is the fourteenth Dalai Lama of Tibetan Buddhism. He was born in 1935 and recognised as the reincarnation of Thubten Gyatso at a young age. Tenzin</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': 0.05}
```
#### 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: 11.23 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 57.14 tokens</li><li>max: 107 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who are the dc characters?</code> | <code>['Superman.', 'Batman. ... ', 'Flash. ... ', 'Green Lantern. ... ', 'Wonder Woman. ... ', 'Martian Manhunter. ... ', 'Aquaman. ... ', 'John Constantine. ... ']</code> |
| <code>what restaurants are giving free food today?</code> | <code>['Burger King.', 'The Cheesecake Factory.', "Steak 'n Shake.", "Wendy's.", "TGI Friday's.", 'Panera.', "Moe's Southwest Grill."]</code> |
| <code>who is paige on pretty little liars?</code> | <code>McCullers is a character in Pretty Little Liars television series on ABC Family. She is portrayed by Lindsey Shaw. Paige is a talented swimmer and a pretty good fighter, as we see in This Is A Dark Ride. She is part of Rosewood High School's swim team.</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
```
#### paws-pos
* Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 25.72 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 25.55 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>They were there to enjoy us and they were there to pray for us .</code> | <code>They were there for us to enjoy and they were there for us to pray .</code> |
| <code>After the end of the war in June 1902 , Higgins left Southampton in the `` SSBavarian '' in August , returning to Cape Town the following month .</code> | <code>In August , after the end of the war in June 1902 , Higgins Southampton left the `` SSBavarian '' and returned to Cape Town the following month .</code> |
| <code>From the merger of the Four Rivers Council and the Audubon Council , the Shawnee Trails Council was born .</code> | <code>Shawnee Trails Council was formed from the merger of the Four Rivers Council and the Audubon Council .</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 160
- `per_device_eval_batch_size`: 64
- `gradient_accumulation_steps`: 8
- `learning_rate`: 4e-05
- `weight_decay`: 0.0001
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1.3333333333333335e-05}
- `warmup_ratio`: 0.33
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa-small-ST-v1-toytest-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`: 160
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `learning_rate`: 4e-05
- `weight_decay`: 0.0001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1.3333333333333335e-05}
- `warmup_ratio`: 0.33
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTa-small-ST-v1-toytest-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 | vitaminc-pairs loss | trivia pairs loss | xsum-pairs loss | paws-pos loss | sciq pairs loss | msmarco pairs loss | openbookqa pairs loss | gooaq pairs loss | nq pairs loss | scitail-pairs-pos loss | qasc pairs loss | negation-triplets loss | NLI-v2_max_accuracy | VitaminC_max_ap | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:-------------------:|:-----------------:|:---------------:|:-------------:|:---------------:|:------------------:|:---------------------:|:----------------:|:-------------:|:----------------------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:------------------------:|
| 0.0169 | 3 | 7.2372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0339 | 6 | 6.855 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0508 | 9 | 7.4707 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0677 | 12 | 7.0187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0847 | 15 | 6.6756 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1016 | 18 | 6.0155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1186 | 21 | 6.1644 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1355 | 24 | 6.2158 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1524 | 27 | 6.1369 | 2.6986 | 6.3356 | 6.0730 | 2.2308 | 0.3450 | 6.9377 | 4.4060 | 6.4060 | 6.7941 | 1.9217 | 3.2268 | 5.1429 | 1.0 | 0.5356 | 0.1067 |
| 0.1694 | 30 | 5.7653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1863 | 33 | 6.1259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2032 | 36 | 5.7539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2202 | 39 | 6.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2371 | 42 | 6.0074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2541 | 45 | 5.7125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2710 | 48 | 5.5634 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2879 | 51 | 5.2924 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3049 | 54 | 5.2286 | 2.6647 | 5.6474 | 5.2498 | 0.8336 | 0.2962 | 5.2464 | 3.8855 | 5.2259 | 5.3326 | 1.2414 | 2.5309 | 4.6218 | 1.0 | 0.5225 | 0.1969 |
| 0.3218 | 57 | 4.4811 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3387 | 60 | 4.4239 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3557 | 63 | 4.0273 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3726 | 66 | 3.4508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3896 | 69 | 3.9702 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4065 | 72 | 3.5295 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4234 | 75 | 3.6395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4404 | 78 | 3.2398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4573 | 81 | 3.116 | 2.5044 | 3.1392 | 2.4290 | 0.1975 | 0.1526 | 2.9677 | 2.4785 | 2.8775 | 3.3587 | 0.2785 | 1.2902 | 3.4229 | 1.0 | 0.5306 | 0.4892 |
| 0.4742 | 84 | 2.6049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4912 | 87 | 2.7738 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5081 | 90 | 2.5416 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5251 | 93 | 2.3913 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5420 | 96 | 2.3144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5589 | 99 | 2.1857 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5759 | 102 | 1.8881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5928 | 105 | 2.2699 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6097 | 108 | 2.1425 | 2.7217 | 1.7080 | 1.2066 | 0.0800 | 0.0949 | 1.6446 | 1.5739 | 1.7924 | 2.3649 | 0.2329 | 0.8462 | 2.3389 | 1.0 | 0.5323 | 0.7806 |
| 0.6267 | 111 | 2.1276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6436 | 114 | 1.7531 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6606 | 117 | 2.0179 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6775 | 120 | 1.5305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6944 | 123 | 1.6925 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7114 | 126 | 1.5248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7283 | 129 | 1.523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7452 | 132 | 1.5474 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7622 | 135 | 1.7221 | 2.8521 | 1.4495 | 0.7707 | 0.0601 | 0.0751 | 1.1524 | 1.4015 | 1.3955 | 1.7769 | 0.2150 | 0.6356 | 2.0742 | 1.0 | 0.5327 | 0.8315 |
| 0.7791 | 138 | 1.5366 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7960 | 141 | 1.3045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8130 | 144 | 1.1999 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8299 | 147 | 1.3483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8469 | 150 | 1.2009 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8638 | 153 | 1.4495 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8807 | 156 | 1.2329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8977 | 159 | 1.1905 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9146 | 162 | 1.277 | 2.7764 | 1.2929 | 0.5587 | 0.0525 | 0.0604 | 0.8656 | 1.1903 | 1.1581 | 1.1554 | 0.1988 | 0.4943 | 2.0055 | 1.0 | 0.5311 | 0.8548 |
| 0.9315 | 165 | 1.339 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9485 | 168 | 1.1535 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9654 | 171 | 1.1643 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9824 | 174 | 1.2221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9993 | 177 | 1.0974 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0162 | 180 | 1.0984 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0332 | 183 | 1.0543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0501 | 186 | 1.0994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0670 | 189 | 1.0621 | 2.6755 | 1.2004 | 0.3837 | 0.0421 | 0.0556 | 0.6897 | 1.0837 | 1.0353 | 0.9604 | 0.1854 | 0.4047 | 1.9071 | 1.0 | 0.5420 | 0.8680 |
| 1.0840 | 192 | 0.8724 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1009 | 195 | 0.9381 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1179 | 198 | 0.9617 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1348 | 201 | 1.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1517 | 204 | 1.1073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1687 | 207 | 0.8365 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1856 | 210 | 1.1012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2025 | 213 | 1.0016 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2195 | 216 | 1.0957 | 2.5466 | 1.1412 | 0.3591 | 0.0395 | 0.0517 | 0.5819 | 0.9366 | 0.9686 | 0.8172 | 0.1901 | 0.3075 | 1.9161 | 1.0 | 0.5385 | 0.8656 |
| 1.2364 | 219 | 1.1273 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2534 | 222 | 1.2568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2703 | 225 | 0.873 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2872 | 228 | 1.0003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3042 | 231 | 1.142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3211 | 234 | 0.807 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3380 | 237 | 1.0231 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3550 | 240 | 0.797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3719 | 243 | 0.8473 | 2.5140 | 1.1067 | 0.2802 | 0.0343 | 0.0467 | 0.5559 | 0.8562 | 0.8929 | 0.7435 | 0.1750 | 0.2355 | 1.8629 | 1.0 | 0.5508 | 0.8687 |
| 1.3888 | 246 | 0.9531 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4058 | 249 | 0.9023 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4227 | 252 | 0.8922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4397 | 255 | 0.9874 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4566 | 258 | 0.8508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4735 | 261 | 0.7149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4905 | 264 | 0.894 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5074 | 267 | 0.867 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5243 | 270 | 0.7493 | 2.5574 | 1.0634 | 0.2217 | 0.0319 | 0.0435 | 0.5027 | 0.7999 | 0.8005 | 0.6530 | 0.1693 | 0.2443 | 1.8535 | 1.0 | 0.5499 | 0.8716 |
| 1.5413 | 273 | 0.7974 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5582 | 276 | 0.797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5752 | 279 | 0.6749 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5921 | 282 | 0.9325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6090 | 285 | 0.8418 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6260 | 288 | 1.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6429 | 291 | 0.6961 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6598 | 294 | 0.9361 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6768 | 297 | 0.6747 | 2.4871 | 0.9762 | 0.2242 | 0.0291 | 0.0396 | 0.5025 | 0.7668 | 0.7546 | 0.6427 | 0.1596 | 0.1963 | 1.7349 | 1.0 | 0.5461 | 0.8787 |
| 1.6937 | 300 | 0.7786 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7107 | 303 | 0.7171 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7276 | 306 | 0.6627 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7445 | 309 | 0.6711 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7615 | 312 | 0.9076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7784 | 315 | 0.7414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7953 | 318 | 0.582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8123 | 321 | 0.6068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8292 | 324 | 0.6219 | 2.5197 | 1.0206 | 0.1630 | 0.0273 | 0.0383 | 0.4859 | 0.7109 | 0.7736 | 0.5533 | 0.1535 | 0.2044 | 1.7016 | 1.0 | 0.5532 | 0.8807 |
| 1.8462 | 327 | 0.5862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8631 | 330 | 0.678 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8800 | 333 | 0.6272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8970 | 336 | 0.5048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9139 | 339 | 0.7653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9308 | 342 | 0.6613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9478 | 345 | 0.6122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9647 | 348 | 0.5939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9817 | 351 | 0.6923 | 2.4379 | 0.9582 | 0.1464 | 0.0264 | 0.0382 | 0.4348 | 0.7554 | 0.7220 | 0.5432 | 0.1481 | 0.1640 | 1.7345 | 1.0 | 0.5560 | 0.8837 |
| 1.9986 | 354 | 0.5712 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0155 | 357 | 0.5969 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0325 | 360 | 0.5881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0494 | 363 | 0.6005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0663 | 366 | 0.6066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0833 | 369 | 0.4921 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1002 | 372 | 0.5354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1171 | 375 | 0.5602 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1341 | 378 | 0.5686 | 2.3908 | 0.9614 | 0.1454 | 0.0271 | 0.0374 | 0.4246 | 0.7796 | 0.6965 | 0.5298 | 0.1401 | 0.1604 | 1.7678 | 1.0 | 0.5539 | 0.8804 |
| 2.1510 | 381 | 0.6496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1680 | 384 | 0.4713 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1849 | 387 | 0.6345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2018 | 390 | 0.5994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2188 | 393 | 0.6763 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2357 | 396 | 0.7254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2526 | 399 | 0.8032 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2696 | 402 | 0.4914 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2865 | 405 | 0.6307 | 2.4388 | 0.9862 | 0.1308 | 0.0262 | 0.0379 | 0.3928 | 0.7434 | 0.6976 | 0.4998 | 0.1192 | 0.1466 | 1.7093 | 1.0 | 0.5533 | 0.8859 |
| 2.3035 | 408 | 0.7493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3204 | 411 | 0.5139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3373 | 414 | 0.6364 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3543 | 417 | 0.4763 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3712 | 420 | 0.583 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3881 | 423 | 0.5912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4051 | 426 | 0.5936 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4220 | 429 | 0.5959 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4390 | 432 | 0.676 | 2.4265 | 0.9634 | 0.1220 | 0.0260 | 0.0362 | 0.4292 | 0.7433 | 0.6771 | 0.4752 | 0.1282 | 0.1304 | 1.6943 | 1.0 | 0.5532 | 0.8878 |
| 2.4559 | 435 | 0.5622 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4728 | 438 | 0.4633 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4898 | 441 | 0.5955 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5067 | 444 | 0.6271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5236 | 447 | 0.4988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5406 | 450 | 0.519 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5575 | 453 | 0.5538 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5745 | 456 | 0.4826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5914 | 459 | 0.6322 | 2.4541 | 0.9231 | 0.1224 | 0.0253 | 0.0345 | 0.4048 | 0.7595 | 0.6607 | 0.4713 | 0.1168 | 0.1323 | 1.7024 | 1.0 | 0.5557 | 0.8868 |
| 2.6083 | 462 | 0.6342 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6253 | 465 | 0.7012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6422 | 468 | 0.4175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6591 | 471 | 0.7575 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6761 | 474 | 0.4687 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6930 | 477 | 0.5907 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7100 | 480 | 0.4796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7269 | 483 | 0.4809 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7438 | 486 | 0.4696 | 2.4899 | 0.9546 | 0.1169 | 0.0247 | 0.0343 | 0.4138 | 0.7444 | 0.6688 | 0.4838 | 0.1166 | 0.1279 | 1.6605 | 1.0 | 0.5527 | 0.8883 |
| 2.7608 | 489 | 0.6588 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7777 | 492 | 0.5675 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7946 | 495 | 0.4007 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8116 | 498 | 0.4476 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8285 | 501 | 0.433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8454 | 504 | 0.4154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8624 | 507 | 0.5416 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8793 | 510 | 0.4546 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8963 | 513 | 0.3326 | 2.4924 | 0.9493 | 0.1071 | 0.0248 | 0.0344 | 0.4033 | 0.7376 | 0.6558 | 0.4478 | 0.1148 | 0.1219 | 1.6918 | 1.0 | 0.5534 | 0.8907 |
| 2.9132 | 516 | 0.594 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9301 | 519 | 0.4727 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9471 | 522 | 0.4701 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9640 | 525 | 0.4606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9809 | 528 | 0.5025 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9979 | 531 | 0.4314 | 2.4532 | 0.9270 | 0.1131 | 0.0247 | 0.0344 | 0.3951 | 0.7123 | 0.6345 | 0.4383 | 0.1143 | 0.1159 | 1.7003 | 1.0 | 0.5539 | 0.8904 |
</details>
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.1.2
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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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