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---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:220804
- loss:GISTEmbedLoss
base_model: BAAI/bge-m3
widget:
- source_sentence: friday night lights season 2 how many episodes
sentences:
- Ossicles The ossicles (also called auditory ossicles) are three bones in either
middle ear that are among the smallest bones in the human body. They serve to
transmit sounds from the air to the fluid-filled labyrinth (cochlea). The absence
of the auditory ossicles would constitute a moderate-to-severe hearing loss. The
term "ossicle" literally means "tiny bone". Though the term may refer to any small
bone throughout the body, it typically refers to the malleus, incus, and stapes
(hammer, anvil, and stirrup) of the middle ear.
- 'Ring a Ring o'' Roses It is unknown what the earliest version of the rhyme was
or when it began. Many incarnations of the game have a group of children form
a ring, dance in a circle around a person, and stoop or curtsy with the final
line. The slowest child to do so is faced with a penalty or becomes the "rosie"
(literally: rose tree, from the French rosier) and takes their place in the center
of the ring.'
- Friday Night Lights (season 2) The second season of the American serial drama
television series Friday Night Lights commenced airing in the United States and
Canada on October 5, 2007 and concluded its 15-episode season on February 7, 2008,
on NBC. While initially renewed for a 22-episode full season, the show ended production
for the season after filming the 15th episode, due to the 2007–08 Writers Guild
of America strike.[1] The series' future was once again placed in doubt as it
did not return to production once the strike ended, and it continued to suffer
from low ratings in its new Friday at 9:00 pm time slot. However, NBC announced
in April 2008 that the show would return for a third season, with first-run broadcasts
airing on DirecTV's The 101 Network.[2] The second season was released on DVD
in region 1 on April 22, 2008.[3]
- source_sentence: More than 273 people have died from the 2019-20 coronavirus outside
mainland China .
sentences:
- 'More than 3,700 people have died : around 3,100 in mainland China and around
550 in all other countries combined .'
- 'More than 3,200 people have died : almost 3,000 in mainland China and around
275 in other countries .'
- more than 4,900 deaths have been attributed to COVID-19 .
- source_sentence: Life cycle is the term for the span in the life of an organism
from one generation to the next.
sentences:
- What is the term for the span in the life of an organism from one generation to
the next?
- While spermatogenesis produces four haploid sperm cells, oogenesis produces one
mature what?
- In what type of climate might one find deciduous trees?
- source_sentence: The source of geothermal power is the heat contained inside the
Earth;
sentences:
- A ribosome consists of two elements, rrna and proteins.
- Geothermal power is generated via underground sources of heat.
- The ph scale measures acids and bases and has 7 as a neutral value.
- source_sentence: Which hand puppet would you associate with Shari Lewis?
sentences:
- Names of Presidents and First Ladies President's and First Ladies' Names Alphabetically
Presidents' First Names Presidents' Middle Names Presidents' Last Names First
Ladies' First Names First Ladies' Maiden names Abraham Abram Adams x 2 Abigail
Appleton Andrew Alan Arthur Anna x 3 Bouvier Andrew Baines Buchanan Barbara Carow
Benjamin Birchard Bush x 2 Caroline Childress Calvin Clark Carter Claudia "Lady
Bird" Custis Chester David Cleveland Dolly Davis Dwight Delano Clinton Edith x
2 Dent Franklin x 2 Earl Coolidge Eleanor x 2 deWolf George x 3 Fitzgerald Eisenhower
Eliza Doud Gerald Gamaliel Fillmore Elizabeth "Bess" Fulsom Grover Henry Ford
Elizabeth "Betty" Galt Harry Herbert Walker Garfield Elizabeth "Eliza" Gardiner
Herbert Howard Grant Ellen Goodhew James x 5 Jefferson Harding Florence Henry
John x 4 Knox Harrison x 2 Frances Herndon Lyndon Milhous Hayes Hannah Herron
Martin Quincy Hoover Helen Hoes Millard Rudolph Jackson Hillary Johnson Richard
S Jefferson Ida Kortright Ronald Simpson Johnson x 2 Jacqueline McCardle Rutherford
Walker Kennedy Jane Pierce Theodore Wilson Lincoln Julia x 2 Powers Thomas Madison
Laura Robards Ulysses McKinley Lou Rodham Warren Monroe Louisa Roosevelt William
x 4 Nixon Lucretia Rudolph Woodrow Pierce Lucy Ryan Zachary Polk Margaret Saxton
Reagan Martha x 2 Scott Roosevelt x 2 Mary Skelton Taft Mary "Mamie" Smith x 3
Taylor Nancy Symmes Truman Rachel Taylor Tyler Rosalynn Todd x 2 VanBuren Sarah
Wallace Washington Thelma "Patricia" Warren Wilson Webb Welch
- 'Shari Lewis Puppets | eBay Shari Lewis Puppets Buy It Now Free Shipping This
baby doll puppet was part of my mom''s doll collection. Shari''s Baby: Teach Baby
to Talk. Baby Puppet with Blanket. This doll has been on display, but has not
been played with. It comes from a smo... $11.70 Buy It Now Shari Lewis. Lamb Chop
and 2 Charlie the Horse Puppets. lamb chop: 5" X 8". horse: 6" X 11". horse: 5.5"
X 10". Classic children''s entertainer!all the rubber faces have some areas of
rubbing and tiny ...'
- 'What is the chemical formula for gold? | Reference.com What is the chemical formula
for gold? A: Quick Answer The chemical formula for gold is Au, which is its periodic
table symbol. The symbol comes from the Latin word for gold, "aurum." Full Answer
Gold is a highly valued metal that has been known about for roughly 5,500 years.
It can sometimes be found on its own, but it is more often found mixed with silver,
quartz, lead, zinc and copper. There is also approximately 1 milligram of gold
dissolved in every ton of seawater, but it would cost more to extract it than
the gold is worth. Gold is valuable because it is malleable, a good conductor
of heat and electricity and does not tarnish when exposed to air. Plus, it sparkles
which pleases jewelry lovers.'
datasets:
- bobox/enhanced_NLI-50K
- tals/vitaminc
- allenai/scitail
- bobox/xSum-processed
- allenai/sciq
- allenai/qasc
- bobox/OpenbookQA-4ST
- sentence-transformers/natural-questions
- sentence-transformers/trivia-qa
- sentence-transformers/gooaq
- google-research-datasets/paws
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.9026620207137961
name: Pearson Cosine
- type: spearman_cosine
value: 0.913678627606199
name: Spearman Cosine
- task:
type: binary-classification
name: Binary Classification
dataset:
name: allNLI dev
type: allNLI-dev
metrics:
- type: cosine_accuracy
value: 0.76953125
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7752166986465454
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.6624737945492662
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6564935445785522
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5197368421052632
name: Cosine Precision
- type: cosine_recall
value: 0.9132947976878613
name: Cosine Recall
- type: cosine_ap
value: 0.6627175481841632
name: Cosine Ap
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Qnli dev
type: Qnli-dev
metrics:
- type: cosine_accuracy
value: 0.705078125
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.6866907477378845
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.6931818181818182
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6343963146209717
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.6267123287671232
name: Cosine Precision
- type: cosine_recall
value: 0.7754237288135594
name: Cosine Recall
- type: cosine_ap
value: 0.7567018413685389
name: Cosine Ap
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the [negation-triplets](https://huggingface.co/datasets/bobox/enhanced_NLI-50K), [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](https://huggingface.co/datasets/bobox/xSum-processed), [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), [openbookqa_pairs](https://huggingface.co/datasets/bobox/OpenbookQA-4ST), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq), [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) and global_dataset datasets. It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [negation-triplets](https://huggingface.co/datasets/bobox/enhanced_NLI-50K)
- [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](https://huggingface.co/datasets/bobox/xSum-processed)
- [sciq_pairs](https://huggingface.co/datasets/allenai/sciq)
- [qasc_pairs](https://huggingface.co/datasets/allenai/qasc)
- [openbookqa_pairs](https://huggingface.co/datasets/bobox/OpenbookQA-4ST)
- [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa)
- [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq)
- [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws)
- global_dataset
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): AdvancedWeightedPooling(
(mha): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True)
)
(MLP): Sequential(
(0): Linear(in_features=1024, out_features=2048, bias=True)
(1): ReLU()
(2): Linear(in_features=2048, out_features=1024, bias=True)
)
(layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=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/XLMRoBERTaM3-CustomPoolin-v1-s1-checkpoints-tmp")
# Run inference
sentences = [
'Which hand puppet would you associate with Shari Lewis?',
'Shari Lewis Puppets | eBay Shari Lewis Puppets Buy It Now Free Shipping This baby doll puppet was part of my mom\'s doll collection. Shari\'s Baby: Teach Baby to Talk. Baby Puppet with Blanket. This doll has been on display, but has not been played with. It comes from a smo... $11.70 Buy It Now Shari Lewis. Lamb Chop and 2 Charlie the Horse Puppets. lamb chop: 5" X 8". horse: 6" X 11". horse: 5.5" X 10". Classic children\'s entertainer!all the rubber faces have some areas of rubbing and tiny ...',
'Names of Presidents and First Ladies President\'s and First Ladies\' Names Alphabetically Presidents\' First Names Presidents\' Middle Names Presidents\' Last Names First Ladies\' First Names First Ladies\' Maiden names Abraham Abram Adams x 2 Abigail Appleton Andrew Alan Arthur Anna x 3 Bouvier Andrew Baines Buchanan Barbara Carow Benjamin Birchard Bush x 2 Caroline Childress Calvin Clark Carter Claudia "Lady Bird" Custis Chester David Cleveland Dolly Davis Dwight Delano Clinton Edith x 2 Dent Franklin x 2 Earl Coolidge Eleanor x 2 deWolf George x 3 Fitzgerald Eisenhower Eliza Doud Gerald Gamaliel Fillmore Elizabeth "Bess" Fulsom Grover Henry Ford Elizabeth "Betty" Galt Harry Herbert Walker Garfield Elizabeth "Eliza" Gardiner Herbert Howard Grant Ellen Goodhew James x 5 Jefferson Harding Florence Henry John x 4 Knox Harrison x 2 Frances Herndon Lyndon Milhous Hayes Hannah Herron Martin Quincy Hoover Helen Hoes Millard Rudolph Jackson Hillary Johnson Richard S Jefferson Ida Kortright Ronald Simpson Johnson x 2 Jacqueline McCardle Rutherford Walker Kennedy Jane Pierce Theodore Wilson Lincoln Julia x 2 Powers Thomas Madison Laura Robards Ulysses McKinley Lou Rodham Warren Monroe Louisa Roosevelt William x 4 Nixon Lucretia Rudolph Woodrow Pierce Lucy Ryan Zachary Polk Margaret Saxton Reagan Martha x 2 Scott Roosevelt x 2 Mary Skelton Taft Mary "Mamie" Smith x 3 Taylor Nancy Symmes Truman Rachel Taylor Tyler Rosalynn Todd x 2 VanBuren Sarah Wallace Washington Thelma "Patricia" Warren Wilson Webb Welch',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9027 |
| **spearman_cosine** | **0.9137** |
#### Binary Classification
* Datasets: `allNLI-dev` and `Qnli-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | allNLI-dev | Qnli-dev |
|:--------------------------|:-----------|:-----------|
| cosine_accuracy | 0.7695 | 0.7051 |
| cosine_accuracy_threshold | 0.7752 | 0.6867 |
| cosine_f1 | 0.6625 | 0.6932 |
| cosine_f1_threshold | 0.6565 | 0.6344 |
| cosine_precision | 0.5197 | 0.6267 |
| cosine_recall | 0.9133 | 0.7754 |
| **cosine_ap** | **0.6627** | **0.7567** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
#### negation-triplets
* Dataset: [negation-triplets](https://huggingface.co/datasets/bobox/enhanced_NLI-50K) at [d43e6fe](https://huggingface.co/datasets/bobox/enhanced_NLI-50K/tree/d43e6fe7f1e171f916502c123235d4b9ec997cb4)
* Size: 5,025 training samples
* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | entailment | negative |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 25.85 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.35 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.38 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
| anchor | entailment | negative |
|:----------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| <code>a small clean bathroom with a blue towel and a white wash cloth</code> | <code>A big bathroom with sink and mirror in back. </code> | <code>A big bathroom without sink and mirror in back.</code> |
| <code>A bathroom counter that has a vase with flowers inside of it.</code> | <code>a bathroom with a big bathtub and a big mirror above the sinks </code> | <code>a bathroom with a small bathtub and a small mirror above the sinks </code> |
| <code>A new outdoor vintage flea market will open on Sun., April 22 at Westfield Promenade, 6100 Topanga Canyon Blvd., Woodland Hills.</code> | <code>Vintage flea market opens April 22</code> | <code>Antique store closes doors on April 22</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### vitaminc-pairs
* Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0)
* Size: 5,025 training samples
* Columns: <code>claim</code> and <code>evidence</code>
* Approximate statistics based on the first 1000 samples:
| | claim | evidence |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 20.19 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 45.84 tokens</li><li>max: 294 tokens</li></ul> |
* Samples:
| claim | evidence |
|:-----------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The McLaren 720S is considered a sports car .</code> | <code>The McLaren 720S is a British sports car designed and manufactured by McLaren Automotive .</code> |
| <code>Snow Patrol is a Northern Irish band .</code> | <code>`` } } '' '' Chasing Cars '' '' is a song by Northern Irish/Scottish alternative rock band Snow Patrol . ''</code> |
| <code>Rachid Ghezzal has one nationality .</code> | <code>Rachid Ghezzal ( born 9 May 1992 ) is Algerian footballer who plays for French club Lyon in Ligue 1 and the Algeria national team .</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### scitail-pairs-qa
* Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 5,025 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 19.0 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.33 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Gas bubble holes helps pumice float in water.</code> | <code>What helps pumice float in water?</code> |
| <code>The law of parsimony is the scientific concept stating that when looking at two competing theories, the one with fewer assumptions should be chosen.</code> | <code>What the scientific concept stating that when looking at two competing theories, the one with fewer assumptions should be chosen?</code> |
| <code>Community interactions are important factors in natural selection.</code> | <code>Community interactions are important factors in what?</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### scitail-pairs-pos
* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 5,025 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 28.99 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 19.18 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| <code>Neutrons have no electric charge.</code> | <code>A neutron has neutral or no charge.</code> |
| <code>Blood vessels are part of the circulatory system, the "highway" system of the human body that transports materials to all of its cells.</code> | <code>The main function of the circulatory system is to transport materials.</code> |
| <code>Clouds form by the condensation of water vapor.</code> | <code>Condensation must occur for clouds to form.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### xsum-pairs
* Dataset: [xsum-pairs](https://huggingface.co/datasets/bobox/xSum-processed) at [044020f](https://huggingface.co/datasets/bobox/xSum-processed/tree/044020f516c1830da392e567474cd5452971366f)
* Size: 131,779 training samples
* Columns: <code>summary</code> and <code>document</code>
* Approximate statistics based on the first 1000 samples:
| | summary | document |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 11 tokens</li><li>mean: 30.04 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 61 tokens</li><li>mean: 250.1 tokens</li><li>max: 401 tokens</li></ul> |
* Samples:
| summary | document |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Australia international Christian Lealiifano says there were times during his recovery from leukaemia that he thought he would not play rugby again.</code> | <code>The 29-year-old was diagnosed in August 2016, but made his comeback in the Brumbies' Super Rugby quarter-final loss in July.<br>Lealiifano has been linked with a short-term deal at Pro14 side Ulster.<br>"When I first started training I thought I would never play football again," Lealiifano said.<br>"When the doctor gave me the all clear that I could return back to work, that was when I had my eye on the prize."<br>Lealiifano said his cancer battle has "changed" his outlook on life.<br>He underwent chemotherapy and a bone marrow transplant, and was told the cancer was in remission in February.<br>"I would go through this 10 times again for the person I am today," he said. "The journey I have been through and the person that I have become."<br>Lealiifano say he is "exploring options" in Australia's National Rugby Championship and playing abroad.<br>Addressing the Ulster rumours, he said: "Nothing has been locked away yet - it is something in the pipeline."<br>Lealiifano, who the last of his 19 caps for the Wallabi...</code> |
| <code>A charity said one of its boats suffered "catastrophic damage" searching a river for a missing man.</code> | <code>West Midlands Search & Rescue (WMSAR), which is run by volunteers, was on the River Severn in Shrewsbury after Daniel Hodgin, 20, went missing last weekend.<br>He failed to return home after a night out with friends at The Buttermarket nightclub in the town.<br>The damage on Saturday was believed to have been caused by submerged debris, WMSAR said.<br>Nobody was hurt in this weekend's incident and the crew was rescued by another of its boats nearby, it said.<br>A spokesman said: "We are a small charity, run wholly by specialist volunteers and funded by donations.<br>"The loss of a boat will have a significant financial impact and we desperately need help to replace the boat and get the team back up to full operational capacity."<br>WMSAR provides support to the emergency services in Shropshire, Herefordshire and Worcestershire.<br>West Mercia Police said it believed Mr Hodgin, from Madeley, left the club at about 03:30 GMT on 23 November.<br>Dozens of people assembled at the nightclub on Saturday to search fo...</code> |
| <code>Final preparations are being made for the staging of Scotland's biggest Gaelic cultural festival in Stornoway on Lewis.</code> | <code>The nine-day Royal National Mòd, which will take place from 14-22 October, was last held in the Western Isles in 2011.<br>Organisers An Comunn Gàidhealach said last year's event in Oban was estimated to have generated about £3m for the local economy.<br>The Mòd features music, dance and arts competitions and performances.<br>This year's opening ceremony will include headline performances by Gaelic group Dàimh and the Mischa Macpherson Trio.<br>Lewis Pipe Band will also lead the festival's traditional torchlight procession, which is held on the opening night of the Mòd.<br>John Morrison, chief executive of An Comunn Gàidhealach, said a "tremendous amount" of entries had been received for this year's competitions.<br>He added: "The Mòd is a huge highlight in Scotland's cultural calendar, attracting Gaels and non-Gaels from across the world to celebrate our diverse range of events and competitions.<br>"We're delighted to have the Mischa Macpherson Trio and Dàimh play at this year's opening ceremony, setting t...</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### sciq_pairs
* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815)
* Size: 5,025 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 20.04 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 102.5 tokens</li><li>max: 683 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What astronomical phenomenon is often comprised of remnants of the earliest material that formed in the solar system?</code> | <code>Many meteorites are remnants of the earliest material that formed in the solar system.</code> |
| <code>Female monotremes share what external opening with reptiles and birds?</code> | <code>Female monotremes lack a uterus and vagina. Instead, they have a cloaca with one external opening, like the cloaca of reptiles and birds. The opening is used to excrete wastes as well as lay eggs. The eggs of monotremes have a leathery shell, like the eggs of reptiles. Female monotremes have mammary glands but not nipples. They secrete milk to feed their young from a patch on their belly. This form of reproduction is least risky for the mother but most risky for the offspring.</code> |
| <code>Diarrhea due to food poisoning is an example of a problem involving what organ system?</code> | <code>Much of the time, you probably aren’t aware of your digestive system. It works well without causing any problems. But most people have problems with their digestive system at least once in a while. Did you ever eat something that didn’t “agree” with you? Maybe you had a stomachache or felt sick to your stomach. Perhaps you had diarrhea. These can be symptoms of food poisoning.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### qasc_pairs
* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070)
* Size: 5,025 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 13.09 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 42.35 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What generate heat mainly by keeping their metabolic rate high?</code> | <code>Mammals generate heat mainly by keeping their metabolic rate high.. Gray Squirrel Management Tree squirrels are common Alabama mammals. <br> tree squirrels generate heat mainly by keeping their metabolic rate high</code> |
| <code>Reducing paper production would directly help stop what international problem?</code> | <code>creating paper requires cutting down trees. Saving trees helps stop global warming. <br> Reductions in paper production would help stop global warming</code> |
| <code>Cells form tissues, which form?</code> | <code>Cells are organized into tissues, and tissues form organs.. Skin is a protective organ. <br> Cells form tissues, and tissues form skin</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### openbookqa_pairs
* Dataset: [openbookqa_pairs](https://huggingface.co/datasets/bobox/OpenbookQA-4ST) at [3c2724c](https://huggingface.co/datasets/bobox/OpenbookQA-4ST/tree/3c2724cbd7a9828685de0976c8a6ea6491b2e326)
* Size: 4,957 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: 4 tokens</li><li>mean: 15.73 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.69 tokens</li><li>max: 33 tokens</li></ul> |
* Samples:
| question | fact |
|:---------------------------------------------------------------|:------------------------------------------------------------------|
| <code>Who is most likely to get a job at a lumber yard?</code> | <code>timber companies cut down trees</code> |
| <code>Food is a source of energy for what?</code> | <code>food is a source of energy for animals</code> |
| <code>Porpoises move fastest in</code> | <code>large fins can be used to move quickly through water</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### nq_pairs
* Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 5,025 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 13.41 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 148.54 tokens</li><li>max: 905 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who is the guy on the cover of you've come a long way baby</code> | <code>You've Come a Long Way, Baby The title You've Come a Long Way, Baby was derived from a marketing slogan for Virginia Slims cigarettes.[9] Conceived by Red Design, the album's primary cover art features an obese young man dressed in a T-shirt bearing the words "I'm #1 so why try harder" while holding a cigarette in his left hand.[10][11] The original photograph was taken at the 1983 Fat People's Festival in Danville, Virginia and provided by the Rex Features photo library.[12] Despite a series of enquiries, the man has not been identified.[13] Additional photography for the You've Come a Long Way, Baby liner notes was provided by Simon Thornton.[10] The cover image was changed in North America to an image of shelves stacked with records.</code> |
| <code>govt of india refers to the absolute poverty line in terms of</code> | <code>Below Poverty Line (India) Internationally, an income of less than $1.90 per day per head of purchasing power parity is defined as extreme poverty. By this estimate, about 21.2% of Indians are extremely poor. Income-based poverty lines consider the bare minimum income to provide basic food requirements; it does not account for other essentials such as health care and education.[2] India is an extremely poor country according to this.</code> |
| <code>when did zody's go out of business</code> | <code>Zody's Bankrupt again by the early 1980s, the parent company, now known as HRT Industries, began closing stores in 1984. The remaining Zodys stores in California were shuttered in March 1986,[2][3][4] with many locations being sold to Federated Stores, the parent company of Ralphs supermarket chain,[5][6] while other locations were purchased by HomeClub, a home improvement store chain.[7]</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### trivia_pairs
* Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0)
* Size: 5,025 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 19.59 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 246.6 tokens</li><li>max: 575 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the official language of The Netherlands?</code> | <code>Dutch language | I amsterdam Language Language The official language of the Netherlands is Dutch. The majority of Amsterdam’s residents speak English well and are often fluent in one or two languages on top of that. You can usually get by effortlessly in Amsterdam without a knowing word of Dutch. For those keen to try, learning a few words or phrases will always go a long way with the locals. Tip The Dutch ‘g’ is normally pronounced mid-throat, sounding similar to the ‘ch’ in loch or Bach. Helpful words and phrases</code> |
| <code>Which painting of 1851 by Sir Edwin Landseer features a great stag/deer which stands on a rise among cloud-covered mountains?</code> | <code>Monarch of the Glen Home A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Monarch of the Glen Monarch of the Glen is one of the most famous paintings completed by the English artist Sir Edwin Landseer (1802-1873). Lions might be king’s of the jungle but the message here is clear that this great stag deer is king of the glen. He stands on a rise in a glen among cloud-covered mountains. The magnificent animal surveys the viewer while exhibiting a quiet strength. It was painted in 1851 and was intended to be displayed in the refreshment-room of the House of Lords. The Commons refused the price. (The Famous Artists did not find the original asking price, but did find that the painting was later sold for 7,000 Pounds.) As of when we wrote this post, Monarch of the Glen was housed in the National Museum of Scotland. The world has Andrew McMillan to thank for making this photograph available and placing it into the public domain. If you think this painting looks familiar, that might be bec...</code> |
| <code>"Shirley Conran's book ""Superwoman"", published in 1975, was about what?"</code> | <code>SUPERWOMAN by Shirley Conran | Shirley Conran ‘Will undoubtedly become the Brides’ Bible…’ The Times ‘Shirley Conran has thought of everything. It is the best book on household management… Superwoman is as compulsive a read as any good novel…’ The Times ‘Written with verve and humour…’ Tatler ‘…But best of all are Shirley Conran’s own rather unorthodox ideas for saving your sanity.’ Brigid Keenan, Sunday Times ‘A wise and witty book…Jam-packed with all manner of household hints and endless useful advise…It would make a splendid wedding present.’ Sunday Express ‘Superwoman has a range of comprehensiveness not recently equalled in such a work of information.’ The Times Educational Supplement ‘The writing hard, practical and funny…’ Evening News ‘It is an amazingly good read… Full of valuable tips.’ Financial Times ‘Indispensible for Superman as well as Superwoman…’ Weekend</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### gooaq_pairs
* Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 5,025 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 12.84 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 64.33 tokens</li><li>max: 141 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is the difference between job work and career?</code> | <code>Work is anything where you expend effort, whether paid or not. A job can be either a piece of work, or an activity for which you are paid. A career is a lifetime progression of paid occupation. A profession is a career with high status and one requiring training, often at degree level.</code> |
| <code>are dlr tickets valid on underground?</code> | <code>While the tickets you buy for the Tube are valid on the DLR, if you are using a Oyster Card with pay as you go credit then you must ensure you swipe your card when getting on and off the DLR at each station - the DLR stations are different in that there are no barriers or gates to prevent you exiting without paying.</code> |
| <code>in which season does ross and rachel get together?</code> | <code>The producers had kept Ross and Rachel from being together throughout the first season, eventually bringing them together in the second-season episode "The One Where Ross Finds Out", only to split them up in the following episode.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### paws-pos
* Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09)
* Size: 5,025 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 31.83 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 31.86 tokens</li><li>max: 64 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Phyllonorycter fraxinella is a moth of the Gracillariidae family , which is found from Germany and Poland to Sicily and Greece and from France to Southern Russia .</code> | <code>Phyllonorycter fraxinella is a moth of the Gracillariidae family . It is found from Germany and Poland tot Sicily and Greece and from France to southern Russia .</code> |
| <code>Upernivik Island is an uninhabited island in the Qaasuitsup municipality in northwest Greenland .</code> | <code>Upernivik Island is an uninhabited island in the Qaasuitsup municipality in northwestern Greenland .</code> |
| <code>The following night , Delirious put his problems with Pearce aside to challenge Danielson .</code> | <code>The next night , Delirious put aside his problems with Pearce to challenge Danielson .</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### global_dataset
* Dataset: global_dataset
* Size: 33,818 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: 36.58 tokens</li><li>max: 368 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 57.39 tokens</li><li>max: 690 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|
| <code>The `` Real World Suite '' costs $ 25,000 per night .</code> | <code>`` The level they rebuilt to accommodate MTV is now the `` '' Real World Suite '' '' billed at $ 25,000 per night . ''</code> |
| <code>A young child colors with crayons at his dinner table.</code> | <code>Child colors with crayons at dinner table.</code> |
| <code>Sensori-neural hearing loss is usually permanent and is caused by damage to hair cells in the inner ear.</code> | <code>Loud sounds that cause hearing loss damage the hair cells in the inner ear.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
### Evaluation Datasets
#### vitaminc-pairs
* Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0)
* Size: 128 evaluation samples
* Columns: <code>claim</code> and <code>evidence</code>
* Approximate statistics based on the first 128 samples:
| | claim | evidence |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 23.52 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 39.63 tokens</li><li>max: 92 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>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### negation-triplets
* Dataset: [negation-triplets](https://huggingface.co/datasets/bobox/enhanced_NLI-50K) at [d43e6fe](https://huggingface.co/datasets/bobox/enhanced_NLI-50K/tree/d43e6fe7f1e171f916502c123235d4b9ec997cb4)
* Size: 128 evaluation samples
* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
* Approximate statistics based on the first 128 samples:
| | anchor | entailment | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 15.96 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.28 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.54 tokens</li><li>max: 26 tokens</li></ul> |
* Samples:
| anchor | entailment | negative |
|:----------------------------------------------------------------------|:---------------------------------------------------------------------|:-----------------------------------------------------------------------|
| <code>Two sheep are looking over a cement wall.</code> | <code>Two sheep poking their heads over a white cement wall</code> | <code>Two sheep hiding their bodies under a white cement wall</code> |
| <code>A stone building with a clock displayed on the outside. </code> | <code>A tall multi-story building with a large clock atop it.</code> | <code>A short single-story building with a small clock atop it.</code> |
| <code>An older women tending to a garden.</code> | <code>The lady has a garden</code> | <code>The lady has no garden.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### scitail-pairs-pos
* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 128 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 11 tokens</li><li>mean: 26.51 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 18.73 tokens</li><li>max: 28 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
| <code>Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase;</code> | <code>Wavelength is the distance between two corresponding points of adjacent waves called.</code> |
| <code>humans normally have 23 pairs of chromosomes.</code> | <code>Humans typically have 23 pairs pairs of chromosomes.</code> |
| <code>kinetic energy the energy of motion.</code> | <code>Kinetic energy is the energy of motion.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### scitail-pairs-qa
* Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 128 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 19.12 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 16.72 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The term immunodeficiency means a failure, insufficiency, or delay in the response of the immune system, which may be acquired or inherited.</code> | <code>What term means a failure, insufficiency, or delay in the response of the immune system, which may be acquired or inherited?</code> |
| <code>Each lymph organ has a different job in the immune system.</code> | <code>Each lymph organ has a different job in what system?</code> |
| <code>A(n) an axillary bud is above each leaf scar.</code> | <code>What is above each leaf scar?</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### xsum-pairs
* Dataset: [xsum-pairs](https://huggingface.co/datasets/bobox/xSum-processed) at [044020f](https://huggingface.co/datasets/bobox/xSum-processed/tree/044020f516c1830da392e567474cd5452971366f)
* Size: 131,779 evaluation samples
* Columns: <code>summary</code> and <code>document</code>
* Approximate statistics based on the first 1000 samples:
| | summary | document |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 18 tokens</li><li>mean: 29.7 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 63 tokens</li><li>mean: 235.77 tokens</li><li>max: 405 tokens</li></ul> |
* Samples:
| summary | document |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Glasgow Warriors' Pro12 match against Leinster has been postponed due to severe weather conditions.</code> | <code>Heavy rain overnight on Friday which continued into Saturday rendered the pitch at Scotstoun unplayable.<br>The postponement creates something of a fixture headache for Warriors.<br>They have already had to squeeze their rearranged Champions Cup fixture against Racing 92 into an already packed calendar, and may now need to face Leinster on a midweek date.<br>"The pitch at Scotstoun Stadium is unplayable due to surface water and there is a concern for both players and supporters safety given the adverse weather conditions," a Warriors statement said.<br>Several Scottish football fixtures have also fallen victim to the weather.<br>Pro12<br>Glasgow Warriors P-P Leinster<br>Scottish Premiership<br>Celtic P-P Hamilton Academical<br>Hearts P-P Inverness CT<br>Partick Thistle P-P Motherwell<br>Scottish Championship<br>Falkirk P-P Livingston<br>Raith Rovers P-P Rangers<br>St Mirren P-P Queen of the South<br>Scottish League One<br>Albion Rovers P-P Stranraer<br>Scottish League Two<br>East Stirlingshire P-P Clyde<br>Scottish Cup<br>Lothian Thistle Hutchi...</code> |
| <code>A former consultant has been charged with 10 counts of sexual assault at a hospital in Surrey.</code> | <code>Cardiologist Neil Ineson, 60, of Sandhurst, Berkshire is charged with a series of sexual assaults at Frimley Park Hospital in Surrey.<br>The alleged assaults took place between August 2007 and November 2014.<br>Surrey Police said Dr Ineson had been released on conditional bail and would appear at Guildford Magistrates' Court on 10 May.</code> |
| <code>Jenny Lewis has been photographing artists who live and work in Hackney - but rather than setting out with a list of names, she asks each sitter to suggest the next person in the chain.</code> | <code>This gives the project a vitality that sees her moving from established names to those on the first rung of the ladder, and back again.<br>Lewis says: "Hackney is still full of creative people working down side-alleys, tucked behind residential streets, in old factories and warehouses as well as new studio communities."<br>She is aware that as time passes, the area is changing, and this could be last chance to capture this creative community.<br>Lewis says that even some of those she has photographed have since had to move out.<br>"The borough is evolving so quickly it sometimes feels like historical documentation, recording the way things once were," she says, "capturing a studio with the 20 or so years build up of detritus before the slate is wiped clean and a block of flats takes its place."<br>You can see more of Jenny Lewis's work on her website.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### sciq_pairs
* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 128 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 19.49 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 102.95 tokens</li><li>max: 579 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How does the atomic number change during beta emission?</code> | <code>Beta (β) decay is a more complicated process. Unlike the α-emission, which simply expels a particle, the β-emission involves the transformation of a neutron in the nucleus to a proton and an electron. The electron is then ejected from the nucleus. In the process, the atomic number increases by one while the atomic weight stays the same. As is the case with α-emissions, β-emissions are often accompanied by γ-radiation.</code> |
| <code>Circular water waves decrease in what property as they move away from where a rock is dropped?</code> | <code>16.11 Energy in Waves: Intensity 17. Two identical waves undergo pure constructive interference. Is the resultant intensity twice that of the individual waves? Explain your answer. Circular water waves decrease in amplitude as they move away from where a rock is dropped. Explain why.</code> |
| <code>What are are organisms that feed on small pieces of organic matter?</code> | <code>Deposit feeders, which are organisms that feed on small pieces of organic matter, usually in the top layer of soil. Sea cucumbers are deposit feeders, living on the ocean floor. They eat the tiny scrap particles that are usually abundant in the environments that they inhabit.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### qasc_pairs
* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 128 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 13.09 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 41.5 tokens</li><li>max: 75 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is a major food source for other organisms, including humans?</code> | <code>Mollusks are a major food source for other organisms, including humans.. Mollusks are invertebrates and are no exception. <br> Invertebrates are a major food source for other organisms, including humans.</code> |
| <code>What does not change during melting?</code> | <code>phase changes do not change mass. Melting is a phase change. <br> Mass does not change during melting</code> |
| <code>What causes water to expand as it becomes ice?</code> | <code>Hydrogen bonds cause water to expand when it freezes.. Water freezes into ice when cooled. <br> hydrogen bonds cause water to expand as it becomes ice</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### openbookqa_pairs
* Dataset: [openbookqa_pairs](https://huggingface.co/datasets/bobox/OpenbookQA-4ST) at [3c2724c](https://huggingface.co/datasets/bobox/OpenbookQA-4ST/tree/3c2724cbd7a9828685de0976c8a6ea6491b2e326)
* Size: 500 evaluation samples
* Columns: <code>question</code> and <code>fact</code>
* Approximate statistics based on the first 500 samples:
| | question | fact |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.98 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.32 tokens</li><li>max: 30 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>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### nq_pairs
* Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 128 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 13.3 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 165.62 tokens</li><li>max: 525 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>when did the singer of linkin park die</code> | <code>Chester Bennington Chester Charles Bennington (March 20, 1976 – July 20, 2017) was an American singer, songwriter, musician, and actor. He served as lead singer for the bands Linkin Park, Dead by Sunrise, Grey Daze, and Stone Temple Pilots.</code> |
| <code>when does rose tyler come back to doctor who</code> | <code>Rose Tyler In spin-off series Torchwood (2006–2011), the audience learns that Rose's act of resurrecting Jack cursed him with being unable to die.[14] Her absence and the Doctor's pained estrangement from her proves a point of contention for the Doctor's series 3 companion Martha (Freema Agyeman); when Martha protects the Doctor, living as a human without his memories, it is still Rose that he dreams of.[15] When the Doctor is reunited with Donna Noble (Catherine Tate) in the show's fourth series (2008), Rose mysteriously begins to appear in the Doctor's life—first seen only by Donna, and later in silent video messages which the Doctor fails to notice.[16][17] When a "Time Beetle" creates an alternate universe in which Donna never meets the Doctor and the Doctor dies, Rose travels from her parallel world to this world, working alongside the organisation UNIT to send Donna back in time, and make Donna's younger self turn left at a junction and not right. Rose tells Donna to say two word...</code> |
| <code>who plays simba's mom in the lion king</code> | <code>List of The Lion King characters Sarabi (voiced by Madge Sinclair in the first film, Alfre Woodard in the live-action film) is Mufasa's mate, Simba's mother and Kiara and Kion's paternal grandmother. Her name means "mirage" in Swahili. In The Lion King, she serves as the Queen of Pride Rock. Years after Scar usurps the throne, Sarabi helps Simba fight against Scar and his hyenas. When Simba defeats Scar, Nala becomes Queen and Sarabi becomes the Queen Dowager.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### trivia_pairs
* Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0)
* Size: 128 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 128 samples:
| | query | answer |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 18.95 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 231.95 tokens</li><li>max: 575 tokens</li></ul> |
* Samples:
| query | answer |
|:----------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Excluding Australia, what is the third largest island in the world?</code> | <code>Excluding Australia, what is the third largest island in the world? Excluding Australia, what is the third largest island in the world? +3 votes posted Jun 22, 2016 by Aastha Joshi Share this question Your comment on this question: Email me at this address if a comment is added after mine:Email me if a comment is added after mine Privacy: Your email address will only be used for sending these notifications. Anti-spam verification: To avoid this verification in future, please log in or register . 1 Answer 0 votes Borneo is the largest of the Malay Archipelago and third largest island in the world. Covered by 744,108 area sq. km located at West mid-Pacific (Indonesian, south part; Brunei and Malaysian, north part) However Australia is defined as a continent rather than an island. answer Jun 22, 2016 by Atindra Kumar Nath Your comment on this answer: Email me at this address if a comment is added after mine:Email me if a comment is added after mine Privacy: Your email address will only ...</code> |
| <code>What is a car marque, a software company and the national flower of Egypt?</code> | <code>Home 2016 | Lotus Cars Evora Sport 410 Hethel Test Track Lap × Evora 400 - From Road To Track, It’s Superior × 'Light is Right' - The Lotus Exige Sport 350 × 'Speed of Light' - The Lotus Elise Cup 250 × Lotus 3 Eleven – Two weeks in Germany × Evora 400 - From Road To Track, It’s Superior × 'Light is Right' - The Lotus Exige Sport 350 × 'Speed of Light' - The Lotus Elise Cup 250 × Lotus 3 Eleven – Two weeks in Germany ×</code> |
| <code>What was the first name of the first First Lady of the USA?</code> | <code>Names of Presidents and First Ladies President's and First Ladies' Names Alphabetically Presidents' First Names Presidents' Middle Names Presidents' Last Names First Ladies' First Names First Ladies' Maiden names Abraham Abram Adams x 2 Abigail Appleton Andrew Alan Arthur Anna x 3 Bouvier Andrew Baines Buchanan Barbara Carow Benjamin Birchard Bush x 2 Caroline Childress Calvin Clark Carter Claudia "Lady Bird" Custis Chester David Cleveland Dolly Davis Dwight Delano Clinton Edith x 2 Dent Franklin x 2 Earl Coolidge Eleanor x 2 deWolf George x 3 Fitzgerald Eisenhower Eliza Doud Gerald Gamaliel Fillmore Elizabeth "Bess" Fulsom Grover Henry Ford Elizabeth "Betty" Galt Harry Herbert Walker Garfield Elizabeth "Eliza" Gardiner Herbert Howard Grant Ellen Goodhew James x 5 Jefferson Harding Florence Henry John x 4 Knox Harrison x 2 Frances Herndon Lyndon Milhous Hayes Hannah Herron Martin Quincy Hoover Helen Hoes Millard Rudolph Jackson Hillary Johnson Richard S Jefferson Ida Kortright Ronald S...</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### gooaq_pairs
* Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 128 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 12.64 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 64.33 tokens</li><li>max: 130 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>is flylady app available for android?</code> | <code>FlyHelper is a Free android app to use with Flylady. It has customizable checklists for daily, weekly and monthly routines; checklisted zone lists; a meal planner; a place for your task list and a link to the Flylady day - plan. You also need to download the license key, also for Free (it wasn't always free).</code> |
| <code>can you install vinyl plank flooring over existing ceramic tile?</code> | <code>Vinyl plank flooring works well in any tightly sealed, smooth or seamless surface. Vinyl flooring can be installed over ceramic tile if the grout lines are thin enough.</code> |
| <code>how does your digestive system help us obtain nutrients?</code> | <code>The digestive system converts the foods we eat into their simplest forms, like glucose (sugars), amino acids (that make up protein) or fatty acids (that make up fats). The broken-down food is then absorbed into the bloodstream from the small intestine and the nutrients are carried to each cell in the body.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### paws-pos
* Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09)
* Size: 128 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 128 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 11 tokens</li><li>mean: 31.43 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 31.14 tokens</li><li>max: 54 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>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
#### global_dataset
* Dataset: global_dataset
* Size: 384 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 384 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 35.66 tokens</li><li>max: 341 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 63.78 tokens</li><li>max: 579 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the attachment of ducklings to their mother an example of?</code> | <code>Figure 45.39 The attachment of ducklings to their mother is an example of imprinting. (credit: modification of work by Mark Harkin).</code> |
| <code>Worldwide , there have been more than 152,000 coronavirus cases during 2019-2020 .</code> | <code>more than 154,000 cases have been confirmed worldwide .</code> |
| <code>What helps maintain body temperature?</code> | <code>an animal usually requires a warm body temperature for survival. Mammals are also warm-blooded and are covered with hair or fur. <br> fur helps maintain body temperature</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 256
- `learning_rate`: 0.001
- `weight_decay`: 0.001
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 0.0001}
- `warmup_ratio`: 0.25
- `save_safetensors`: False
- `fp16`: True
- `remove_unused_columns`: False
- `push_to_hub`: True
- `hub_model_id`: bobox/XLMRoBERTaM3-CustomPoolin-v1-s1-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `hub_private_repo`: False
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.001
- `weight_decay`: 0.001
- `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': 0.0001}
- `warmup_ratio`: 0.25
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: False
- `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/XLMRoBERTaM3-CustomPoolin-v1-s1-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
- `include_for_metrics`: []
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `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 | negation-triplets loss | scitail-pairs-pos loss | scitail-pairs-qa loss | xsum-pairs loss | sciq pairs loss | qasc pairs loss | openbookqa pairs loss | nq pairs loss | trivia pairs loss | gooaq pairs loss | paws-pos loss | global dataset loss | sts-test_spearman_cosine | allNLI-dev_cosine_ap | Qnli-dev_cosine_ap |
|:------:|:----:|:-------------:|:-------------------:|:----------------------:|:----------------------:|:---------------------:|:---------------:|:---------------:|:---------------:|:---------------------:|:-------------:|:-----------------:|:----------------:|:-------------:|:-------------------:|:------------------------:|:--------------------:|:------------------:|
| 0.0014 | 1 | 1.2468 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0027 | 2 | 1.4692 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0041 | 3 | 1.2457 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0055 | 4 | 1.1859 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0069 | 5 | 1.2404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0082 | 6 | 0.042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0096 | 7 | 0.8856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0110 | 8 | 1.5417 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0123 | 9 | 0.023 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0137 | 10 | 0.8655 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0151 | 11 | 0.5894 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0165 | 12 | 0.7053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0178 | 13 | 0.5857 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0192 | 14 | 0.8375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0206 | 15 | 0.9043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0219 | 16 | 0.8756 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0233 | 17 | 0.5076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0247 | 18 | 0.4757 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0261 | 19 | 0.9993 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0274 | 20 | 0.2622 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0288 | 21 | 0.3497 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0302 | 22 | 0.2514 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0316 | 23 | 0.1673 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0329 | 24 | 0.203 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0343 | 25 | 0.698 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0357 | 26 | 0.3401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0370 | 27 | 0.2185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0384 | 28 | 0.4424 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0398 | 29 | 0.0381 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0412 | 30 | 0.8215 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0425 | 31 | 0.1542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0439 | 32 | 0.6893 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0453 | 33 | 0.3773 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0466 | 34 | 0.538 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0480 | 35 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0494 | 36 | 2.378 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0508 | 37 | 0.5949 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0521 | 38 | 0.7071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0535 | 39 | 0.1607 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0549 | 40 | 0.7735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0562 | 41 | 0.7594 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0576 | 42 | 0.3569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0590 | 43 | 0.2454 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0604 | 44 | 0.2723 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0617 | 45 | 0.5338 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0631 | 46 | 0.1891 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0645 | 47 | 0.3647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0658 | 48 | 0.383 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0672 | 49 | 0.2353 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0686 | 50 | 0.5541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0700 | 51 | 0.4908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0713 | 52 | 0.586 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0727 | 53 | 0.2241 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0741 | 54 | 0.6046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0754 | 55 | 0.231 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0768 | 56 | 0.7105 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0782 | 57 | 0.5591 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0796 | 58 | 0.5194 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0809 | 59 | 0.3297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0823 | 60 | 0.0299 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0837 | 61 | 0.3514 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0850 | 62 | 0.1932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0864 | 63 | 0.4035 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0878 | 64 | 0.0094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0892 | 65 | 0.0148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0905 | 66 | 0.0231 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0919 | 67 | 0.3204 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0933 | 68 | 0.3011 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0947 | 69 | 0.3871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0960 | 70 | 0.1823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0974 | 71 | 0.3572 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0988 | 72 | 0.5289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1001 | 73 | 0.3223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1015 | 74 | 0.3247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1029 | 75 | 0.2133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1043 | 76 | 0.8249 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1056 | 77 | 0.4341 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1070 | 78 | 0.2932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1084 | 79 | 0.0099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1097 | 80 | 0.3348 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1111 | 81 | 0.6405 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1125 | 82 | 0.1536 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1139 | 83 | 0.1299 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1152 | 84 | 0.5863 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1166 | 85 | 0.7205 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1180 | 86 | 0.4052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1193 | 87 | 0.3953 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1207 | 88 | 0.5598 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1221 | 89 | 0.2856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1235 | 90 | 0.2277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1248 | 91 | 0.3296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1262 | 92 | 0.3079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1276 | 93 | 0.4867 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1289 | 94 | 0.4319 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1303 | 95 | 0.2952 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1317 | 96 | 0.5531 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1331 | 97 | 0.0296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1344 | 98 | 0.8536 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1358 | 99 | 0.4879 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1372 | 100 | 0.67 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1385 | 101 | 0.4813 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1399 | 102 | 0.0488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1413 | 103 | 0.5388 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1427 | 104 | 0.376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1440 | 105 | 0.017 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1454 | 106 | 0.7542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1468 | 107 | 0.4063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1481 | 108 | 0.3658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1495 | 109 | 0.4389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1509 | 110 | 0.3803 | 2.0093 | 1.5957 | 0.0618 | 0.0092 | 0.3721 | 0.0412 | 0.4749 | 1.1541 | 0.2363 | 0.6520 | 0.2262 | 0.0282 | 0.3067 | 0.9137 | 0.6627 | 0.7567 |
| 0.1523 | 111 | 0.2478 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1536 | 112 | 0.8402 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1550 | 113 | 0.6608 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1564 | 114 | 0.0934 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1578 | 115 | 0.3907 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1591 | 116 | 0.449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1605 | 117 | 0.4041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1619 | 118 | 0.6749 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1632 | 119 | 0.4847 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1646 | 120 | 0.0526 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1660 | 121 | 0.6795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1674 | 122 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1687 | 123 | 0.5918 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1701 | 124 | 0.3544 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1715 | 125 | 0.3849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1728 | 126 | 0.2051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1742 | 127 | 0.2129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1756 | 128 | 2.7937 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1770 | 129 | 0.0166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1783 | 130 | 0.7856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1797 | 131 | 0.8368 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1811 | 132 | 0.3813 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1824 | 133 | 0.5695 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1838 | 134 | 0.351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1852 | 135 | 0.3821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1866 | 136 | 0.3249 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1879 | 137 | 0.3404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1893 | 138 | 0.4535 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1907 | 139 | 0.0577 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1920 | 140 | 0.7431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1934 | 141 | 0.6778 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1948 | 142 | 0.5436 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1962 | 143 | 0.3582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1975 | 144 | 0.316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1989 | 145 | 0.4446 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2003 | 146 | 0.7792 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2016 | 147 | 1.1147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2030 | 148 | 0.8267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2044 | 149 | 0.8149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2058 | 150 | 0.942 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2071 | 151 | 2.4865 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2085 | 152 | 1.0715 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2099 | 153 | 0.6219 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2112 | 154 | 0.8705 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2126 | 155 | 0.2407 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2140 | 156 | 0.4925 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2154 | 157 | 0.0316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2167 | 158 | 0.3935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2181 | 159 | 0.2083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2195 | 160 | 0.2798 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2209 | 161 | 0.8777 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2222 | 162 | 0.0002 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2236 | 163 | 0.2736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2250 | 164 | 2.4185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2263 | 165 | 0.7767 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2277 | 166 | 0.7971 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2291 | 167 | 0.4535 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2305 | 168 | 0.6654 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2318 | 169 | 0.3985 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2332 | 170 | 0.0338 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2346 | 171 | 0.1834 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2359 | 172 | 0.603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2373 | 173 | 0.7871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2387 | 174 | 0.4304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2401 | 175 | 0.649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2414 | 176 | 0.048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2428 | 177 | 0.4079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2442 | 178 | 0.4627 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2455 | 179 | 0.3703 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2469 | 180 | 0.8343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2483 | 181 | 0.692 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2497 | 182 | 2.7071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2510 | 183 | 0.8451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2524 | 184 | 0.635 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2538 | 185 | 0.312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2551 | 186 | 0.6996 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2565 | 187 | 0.4432 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2579 | 188 | 0.375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2593 | 189 | 0.9366 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2606 | 190 | 0.755 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2620 | 191 | 0.6068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2634 | 192 | 0.5336 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2647 | 193 | 0.8783 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2661 | 194 | 0.3576 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2675 | 195 | 2.1854 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2689 | 196 | 0.7835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2702 | 197 | 0.5668 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2716 | 198 | 0.7033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2730 | 199 | 0.0002 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2743 | 200 | 0.5791 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2757 | 201 | 0.2697 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2771 | 202 | 0.6261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2785 | 203 | 0.3253 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2798 | 204 | 0.8323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2812 | 205 | 0.4472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2826 | 206 | 0.3342 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2840 | 207 | 0.6313 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2853 | 208 | 0.059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2867 | 209 | 0.1195 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2881 | 210 | 0.0296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2894 | 211 | 0.5316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2908 | 212 | 0.5201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2922 | 213 | 0.6602 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2936 | 214 | 0.9578 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2949 | 215 | 0.2089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2963 | 216 | 1.2112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2977 | 217 | 0.3294 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2990 | 218 | 0.867 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3004 | 219 | 1.1745 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.3.1
- Tokenizers: 0.21.0
## 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",
}
```
#### GISTEmbedLoss
```bibtex
@misc{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
year={2024},
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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