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---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:7176192
- loss:AnglELoss
- loss:CoSENTLoss
- loss:CachedMultipleNegativesRankingLoss
base_model: jhu-clsp/ettin-encoder-32m
widget:
- source_sentence: what is paediatric clinical psychology
sentences:
- Pediatric neuropsychology (paediatric in the UK) is a sub-speciality within the
field of clinical neuropsychology that studies the relationship between brain
health and behaviour in children.any pediatric neuropsychologists are involved
in teaching, research, supervision, and training of undergraduate and graduate
students in the field. In the United States undergraduate and graduate psychology
programs generally do not offer a track in pediatric neuropsychology, per se.
- "â\x80\x9CRealâ\x80\x9D hummus, should contain about 175 calories, out of which\
\ 70-80 calories are contributed by fat. The average Israeli eats 8-10 kilograms\
\ (18-22 pounds) of hummus every year, so weâ\x80\x99re talking about extra 15,000\
\ calories which can make him gain about 2.5kg of body weight each year. So you\
\ can see how excessive consumption of the packaged product might be fattening\
\ over the years. The common serving size of hummus (real hummus, that is), which\
\ is around one cup (220-240g) may contain 400-450 calories. And every pita (â\x80\
\x9Cpita breadâ\x80\x9D) contains another 270, so itâ\x80\x99s not really â\x80\
\x9Cdietaryâ\x80\x9D."
- "Pediatrics (also spelled paediatrics or pædiatrics) is the branch of medicine\
\ that involves the medical care of infants, children, and adolescents. The American\
\ Academy of Pediatrics recommends people be under pediatric care up to the age\
\ of 21.[1] A medical practitioner who specializes in this area is known as a\
\ pediatrician, or paediatrician. The word pediatrics and its cognates mean healer\
\ of children; they derive from two Greek words: Ï\x80αá¿\x96Ï\x82 (pais child)\
\ and ἰαÏ\x84Ï\x81Ï\x8CÏ\x82 (iatros doctor, healer)."
- source_sentence: However , in 1919 , concluded that no more operational awards would
be made for the recently decreed war .
sentences:
- At executive level , EEAA represents the central arm of the ministry .
- In 1919 , however , no operational awards would be made for the recently concluded
war .
- He was asked his opinion about the books `` Mission to Moscow '' by Joseph E.
Davies and `` One World '' by Wendell Willkie .
- source_sentence: Twelve killed in bomb blast on Pakistani train
sentences:
- Five killed by bomb blast in East India
- Five million citizens get unofficial salary in Ukraine
- Above that, seniors would be responsible for 100 percent of drug costs until the
out-of-pocket total reaches $3,600.
- source_sentence: Pen Hadow, who became the first person to reach the geographic
North Pole unsupported from Canada, has just over two days of rations left.
sentences:
- Remnants of highly enriched uranium were found near an Iranian nuclear facility
by United Nations inspectors, deepening fears that Iran possibly has a secret
nuclear weapons program.
- However, the singer believes that artists similar to his self should not receive
any blame.
- Pen Hadow, the first person to to reach the North Pole, has only a little more
than two days of rations left.
- source_sentence: what are the three subatomic particles called?
sentences:
- Subatomic particles include electrons, the negatively charged, almost massless
particles that nevertheless account for most of the size of the atom, and they
include the heavier building blocks of the small but very dense nucleus of the
atom, the positively charged protons and the electrically neutral neutrons.
- Your body needs cholesterol to build healthy cells, but high levels of cholesterol
can increase your risk of heart disease. With high cholesterol, you can develop
fatty deposits in your blood vessels. Eventually, these deposits grow, making
it difficult for enough blood to flow through your arteries.
- 'If you experience any of the following symptoms, stop taking ibuprofen and call
your doctor: stomach pain, heartburn, vomit that is bloody or looks like coffee
grounds, blood in the stool, or black and tarry stools. Keep all appointments
with your doctor and the laboratory.'
datasets:
- google-research-datasets/paws
- nyu-mll/glue
- mwong/fever-evidence-related
- tasksource/parade
- tasksource/apt
- tasksource/sts-companion
- tasksource/zero-shot-label-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on jhu-clsp/ettin-encoder-32m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jhu-clsp/ettin-encoder-32m](https://huggingface.co/jhu-clsp/ettin-encoder-32m) on 19 datasets. It maps sentences & paragraphs to a 384-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:** [jhu-clsp/ettin-encoder-32m](https://huggingface.co/jhu-clsp/ettin-encoder-32m) <!-- at revision 1b8ba06455dd44f80fc9c1ca9e22806157a57379 -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [paws/labeled_final](https://huggingface.co/datasets/paws)
- [glue/mrpc](https://huggingface.co/datasets/glue)
- [fever-evidence-related](https://huggingface.co/datasets/mwong/fever-evidence-related)
- [parade](https://huggingface.co/datasets/tasksource/parade)
- [apt](https://huggingface.co/datasets/tasksource/apt)
- [glue/stsb](https://huggingface.co/datasets/glue)
- sick/relatedness
- [sts-companion](https://huggingface.co/datasets/tasksource/sts-companion)
- [zero-shot-label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli)
- tomaarsen/natural-questions-hard-negatives
- tomaarsen/gooaq-hard-negatives
- bclavie/msmarco-500k-triplets
- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
- sentence-transformers/gooaq
- sentence-transformers/natural-questions
- sentence-transformers/quora-duplicates
- sentence-transformers/s2orc
- sentence-transformers/codesearchnet
- sentence-transformers/stackexchange-duplicates
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## 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("tasksource/ettin-32m-embed")
# Run inference
queries = [
"what are the three subatomic particles called?",
]
documents = [
'Subatomic particles include electrons, the negatively charged, almost massless particles that nevertheless account for most of the size of the atom, and they include the heavier building blocks of the small but very dense nucleus of the atom, the positively charged protons and the electrically neutral neutrons.',
'Your body needs cholesterol to build healthy cells, but high levels of cholesterol can increase your risk of heart disease. With high cholesterol, you can develop fatty deposits in your blood vessels. Eventually, these deposits grow, making it difficult for enough blood to flow through your arteries.',
'If you experience any of the following symptoms, stop taking ibuprofen and call your doctor: stomach pain, heartburn, vomit that is bloody or looks like coffee grounds, blood in the stool, or black and tarry stools. Keep all appointments with your doctor and the laboratory.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 384] [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.6104, 0.0070, 0.0514]])
```
<!--
### 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.*
-->
<!--
## 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
<details><summary>paws/labeled_final</summary>
#### paws/labeled_final
* Dataset: [paws/labeled_final](https://huggingface.co/datasets/paws) at [161ece9](https://huggingface.co/datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09)
* Size: 148,203 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 10 tokens</li><li>mean: 27.74 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 27.73 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>0: ~54.60%</li><li>1: ~45.40%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Göttsche received international acclaim with his formula for the generating function for the Hilbert numbers of the Betti scheme of points on an algebraic surface :</code> | <code>With his formula for the producing function for the Betti - numbers of the Hilbert scheme of points on an algebraic surface , Göttsche received international recognition :</code> | <code>0</code> |
| <code>The former AFL players Tarkyn Lockyer ( Collingwood ) and Ryan Brabazon ( Sydney ) , Jason Mandzij ( Gold Coast ) , started their football careers and played for the Kangas .</code> | <code>Former AFL players Ryan Brabazon ( Collingwood ) and Tarkyn Lockyer ( Sydney ) , Jason Mandzij ( Gold Coast ) started their football careers playing for the Kangas .</code> | <code>0</code> |
| <code>Potter married in 1945 . He and his wife Anne ( a weaver ) had two children , Julian ( born 1947 ) and Mary ( born 1952 ) .</code> | <code>He and his wife Anne ( a weaver ) had two children , Julian ( born 1947 ) and Mary ( born in 1952 ) .</code> | <code>0</code> |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
</details>
<details><summary>glue/mrpc</summary>
#### glue/mrpc
* Dataset: [glue/mrpc](https://huggingface.co/datasets/glue) at [bcdcba7](https://huggingface.co/datasets/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
* Size: 11,004 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 10 tokens</li><li>mean: 27.33 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 27.3 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>0: ~32.40%</li><li>1: ~67.60%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Passed in 1999 but never put into effect , the law would have made it illegal for bar and restaurant patrons to light up .</code> | <code>Passed in 1999 but never put into effect , the smoking law would have prevented bar and restaurant patrons from lighting up , but exempted private clubs from the regulation .</code> | <code>0</code> |
| <code>" Indeed , Iran should be put on notice that efforts to try to remake Iraq in their image will be aggressively put down , " he said .</code> | <code>" Iran should be on notice that attempts to remake Iraq in Iran 's image will be aggressively put down , " he said .</code> | <code>1</code> |
| <code>But U.S. troops will not shrink from mounting raids and attacking their foes when their locations can be pinpointed .</code> | <code>But American troops will not shrink from mounting raids in the locations of their foes that can be pinpointed .</code> | <code>1</code> |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
</details>
<details><summary>fever-evidence-related</summary>
#### fever-evidence-related
* Dataset: [fever-evidence-related](https://huggingface.co/datasets/mwong/fever-evidence-related) at [14aba00](https://huggingface.co/datasets/mwong/fever-evidence-related/tree/14aba009b5fcd97b1a9ee6f3e3b0da0e308cf7cb)
* Size: 800,000 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 13.83 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 340.09 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>0: ~31.40%</li><li>1: ~68.60%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Performance (film) is a religion.</code> | <code>The associative model of data is a data model for database systems .. data model. data model. database. database. Other data models , such as the relational model and the object data model , are record-based .. data model. data model. relational model. relational model. These models involve encompassing attributes about a thing , such as a car , in a record structure .. Such attributes might be registration , colour , make , model , etc. .. In the associative model , everything which has `` discrete independent existence '' is modeled as an entity , and relationships between them are modeled as associations .. The granularity at which data is represented is similar to schemes presented by Chen -LRB- Entity-relationship model -RRB- ; Bracchi , Paolini and Pelagatti -LRB- Binary Relations -RRB- ; and Senko -LRB- The Entity Set Model -RRB- .. Entity-relationship model. Entity-relationship model. A number of claims made about the model by Simon Williams , in his book The Associative Model ...</code> | <code>1</code> |
| <code>American Gods (TV series) has one showrunner, whose name is Greg Berlanti.</code> | <code>American Gods is an American television series based on the novel of the same name , written by Neil Gaiman and originally published in 2001 .. American Gods. American Gods. Neil Gaiman. Neil Gaiman. novel of the same name. American Gods. The television series was developed by Bryan Fuller and Michael Green for the premium cable network Starz .. Bryan Fuller. Bryan Fuller. Michael Green. Michael Green ( writer ). Starz. Starz. Fuller and Green are the showrunners for the series .. Gaiman serves as an executive producer along with Fuller , Green , Craig Cegielski , Stefanie Berk , and Thom Beers .. Thom Beers. Thom Beers. The first episode premiered on the Starz network and through their streaming application on April 30 , 2017 .. Starz. Starz. In May 2017 , the series was renewed for a second season .</code> | <code>0</code> |
| <code>The Ren & Stimpy Show was one of the original four Nicktoons.</code> | <code>Cloud was a browser-based operating system created by Good OS LLC , a Los Angeles-based corporation .. Los Angeles. Los Angeles. The company initially launched a Linux distribution called gOS which is heavily based on Ubuntu , now in its third incarnation .. gOS. gOS ( operating system ). Ubuntu. Ubuntu ( operating system )</code> | <code>1</code> |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
</details>
<details><summary>parade</summary>
#### parade
* Dataset: [parade](https://huggingface.co/datasets/tasksource/parade) at [466978f](https://huggingface.co/datasets/tasksource/parade/tree/466978f31aebf4d052287f32ea3ae393f178f386)
* Size: 22,650 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 22.32 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 22.15 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>0: ~54.50%</li><li>1: ~45.50%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>the process of shrinking the size of a file by removing data or recoding it more efficiently</code> | <code>reducing the amount of space needed to store a piece of data/bandwidth to transmit it (ex. zip files)</code> | <code>0</code> |
| <code>the siem software can ensure that the time is the same across devices so the security events across devices are recorded at the same time.</code> | <code>feature of a siem that makes sure all products are synced up so they are running with the same timestamps.</code> | <code>1</code> |
| <code>a model that is part of a dssa to describe the context and domain semantics important to understand a reference architecture and its architectural decisions</code> | <code>provide a means of information about that class of system and of comparing different architectures</code> | <code>0</code> |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
</details>
<details><summary>apt</summary>
#### apt
* Dataset: [apt](https://huggingface.co/datasets/tasksource/apt) at [f6c07f6](https://huggingface.co/datasets/tasksource/apt/tree/f6c07f66d3eccebd36418885ce10aff295d436dd)
* Size: 10,047 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 17.82 tokens</li><li>max: 124 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 17.3 tokens</li><li>max: 143 tokens</li></ul> | <ul><li>0: ~37.20%</li><li>1: ~62.80%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>"Kahuku Ranch has world - class qualities - tremendous resources, tremendous beauty and tremendous value to global biodiversity."</code> | <code>"TRENENDOUS BEAUTY AND TREMENDOUS VALUE TO GLOBAL BIODERVERSITY TRENENDOUS RESOURCES-CLASS QUALITIES KAHUKU RANCH HAS WORLD"</code> | <code>1</code> |
| <code>In Damascus, Syrian Information Minister Ahmad al-Hassan called the charges "baseless and illogical".</code> | <code>The Syrian Information Minister Ahmad al-Hassan, in Damascus, termed the charges without base and with no logic behind</code> | <code>1</code> |
| <code>We'd talk about the stars... ...and whether there might be somebody else like us out in space,... ...places we wanted to go and... it made our trials seem smaller.</code> | <code>We often would talk about the stars and if somebody else is similar to us out in the universe, places we wanted to visit and it made our problems seem minuscule.</code> | <code>1</code> |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
</details>
<details><summary>glue/stsb</summary>
#### glue/stsb
* Dataset: [glue/stsb](https://huggingface.co/datasets/glue) at [bcdcba7](https://huggingface.co/datasets/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
* Size: 17,247 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.23 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.39 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.73</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|:--------------------------------|
| <code>China's anger at N. Korea overcomes worry over US</code> | <code>China's anger at North Korea overcomes worry over U.S. stealth flights</code> | <code>3.200000047683716</code> |
| <code>Declining issues outnumbered advancers nearly 2 to 1 on the New York Stock Exchange.</code> | <code>Advancers outnumbered decliners by nearly 8 to 3 on the NYSE and more than 11 to 5 on Nasdaq.</code> | <code>1.7999999523162842</code> |
| <code>The computers were reportedly located in the U.S., Canada and South Korea.</code> | <code>The PCs are scattered across the United States, Canada and South Korea.</code> | <code>4.75</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>sick/relatedness</summary>
#### sick/relatedness
* Dataset: sick/relatedness
* Size: 13,317 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 12.39 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.16 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 3.48</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------------------------------------------------|:------------------------------------------------------------|:-------------------------------|
| <code>Someone is cutting some paper with scissors</code> | <code>The piece of paper is being cut</code> | <code>4.5</code> |
| <code>A man is hanging up the phone</code> | <code>A man is making a phone call</code> | <code>3.799999952316284</code> |
| <code>A person is pouring olive oil into a pot on the stove</code> | <code>A person is pouring oil for cooking into a pot</code> | <code>4.300000190734863</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>sts-companion</summary>
#### sts-companion
* Dataset: [sts-companion](https://huggingface.co/datasets/tasksource/sts-companion) at [fd8beff](https://huggingface.co/datasets/tasksource/sts-companion/tree/fd8beffb788df5f6673bc688e6dcbe3690a3acc6)
* Size: 14,280 training samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | label | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | float | string | string |
| details | <ul><li>min: 0.0</li><li>mean: 3.16</li><li>max: 5.0</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.29 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 17.45 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:------------------|:------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|
| <code>1.0</code> | <code>How do I wire a bathroom exhaust fan/light to two switches?</code> | <code>How do I wire a combo with two supplies?</code> |
| <code>4.2</code> | <code>How an all-American hero fell to earth - . (Where have all the REAL heroes gone?)</code> | <code>How all-American hero fell to earth</code> |
| <code>3.75</code> | <code>Be larger in number, quantity, power, status, or importance, without personally having sovereign power.</code> | <code>be larger in number, quantity, power, status or importance.</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>zero-shot-label-nli</summary>
#### zero-shot-label-nli
* Dataset: [zero-shot-label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli) at [ee693db](https://huggingface.co/datasets/tasksource/zero-shot-label-nli/tree/ee693dba923b5d5484aa9232b7357c5e45dd39b8)
* Size: 1,090,333 training samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | label | sentence1 | sentence2 |
|:--------|:------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~50.70%</li><li>1: ~49.30%</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 68.51 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 7.95 tokens</li><li>max: 17 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:---------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------|
| <code>0</code> | <code>Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .<br>Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .</code> | <code>This example is not_equivalent.</code> |
| <code>1</code> | <code>Do science and religion conflict with each other?<br>Does science conflict with the Bible?</code> | <code>This example is not_duplicate.</code> |
| <code>0</code> | <code>do iran and afghanistan speak the same language</code> | <code>This example is False.</code> |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
</details>
<details><summary>tomaarsen/natural-questions-hard-negatives</summary>
#### tomaarsen/natural-questions-hard-negatives
* Dataset: tomaarsen/natural-questions-hard-negatives
* Size: 96,658 training samples
* Columns: <code>query</code>, <code>answer</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, and <code>negative_5</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string | string | string | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 12.52 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 137.85 tokens</li><li>max: 556 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 144.1 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 142.73 tokens</li><li>max: 832 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 146.37 tokens</li><li>max: 649 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 145.79 tokens</li><li>max: 549 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 142.01 tokens</li><li>max: 574 tokens</li></ul> |
* Samples:
| query | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 |
|:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>when did richmond last play in a preliminary final</code> | <code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tig...</code> | <code>Brisbane Bears However, the club was still struggling off-field. One of the Bears' biggest problems was its lack of support (both on and off the field) in Melbourne, the location of most of its away matches. In mid-1996, the struggling Fitzroy Football Club collapsed due to financial pressures and was seeking to merge its assets with another club. When a merger with North Melbourne in forming the North Fitzroy Kangaroos failed to win the support of the other AFL clubs, a deal for a merger was done between Fitzroy and the Bears. The new team was known as the Brisbane Lions, based at the Gabba, with Northey as the coach of the merged club. As such, the history of the Brisbane Bears as an individual entity ended after the 1996 season, with ten seasons of competition and the third-place finish in 1996 as its best performance. The Bears last match as a separate entity was a preliminary final on Saturday 21 September 1996 at the Melbourne Cricket Ground (where the Bears played their first VF...</code> | <code>Virginia Tech–West Virginia football rivalry Virginia Tech held the trophy in six of the nine years in which it was contested, but West Virginia leads the all-time series 28–23–1. The last game was played on September 3, 2017 at FedEx Field in Landover, MD; Virginia Tech won 31–24.</code> | <code>Martin Truex Jr. To start off the Round of 12, Truex scored his 6th win of the season at Charlotte after leading 91 out of 334 laps to secure a spot for the Round of 8. Just two weeks later, he scored another win at Kansas despite having a restart violation early in the race.</code> | <code>Adelaide Football Club Star midfielder for many years Patrick Dangerfield left the club at the end of the 2015 season (a season in which he won the club's best and fairest) and Don Pyke, a former premiership player and assistant coach with West Coast who had also been an assistant coach at Adelaide from 2005 to 2006, was appointed Adelaide's senior coach for at least three years.[9] Adelaide was widely tipped to slide out of the finals in 2016[27][28][29] but the Crows proved to be one of the successes of the season, comfortably qualifying for a home elimination final and defeating North Melbourne by 62 points, before being eliminated the next week by eventual beaten grand finalists, Sydney in the semi-finals. The club had a dominant 2017 season, winning their opening six games and never falling below second place for the entire season. Adelaide claimed their second McClelland Trophy as minor premiers.[30] The Adelaide Crows entered the 2017 finals series as favourites for the premiers...</code> | <code>Battle of Appomattox Court House The Battle of Appomattox Court House (Virginia, U.S.), fought on the morning of April 9, 1865, was one of the last battles of the American Civil War (1861–1865). It was the final engagement of Confederate States Army General-in-Chief, Robert E. Lee, and his Army of Northern Virginia before it surrendered to the Union Army of the Potomac under the Commanding General of the United States, Ulysses S. Grant. Lee, having abandoned the Confederate capital of Richmond, Virginia, after the nine and one-half month Siege of Petersburg and Richmond, retreated west, hoping to join his army with the remaining Confederate forces in North Carolina, the Army of Tennessee under Gen. Joseph E. Johnston. Union infantry and cavalry forces under Gen. Philip Sheridan pursued and cut off the Confederates' retreat at the central Virginia village of Appomattox Court House. Lee launched a last-ditch attack to break through the Union forces to his front, assuming the Union forc...</code> |
| <code>who sang what in the world's come over you</code> | <code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code> | <code>Lover, You Should've Come Over "Lover, You Should've Come Over" is the seventh track on Jeff Buckley's album Grace. Inspired by the ending of the relationship between Buckley and Rebecca Moore,[1] it concerns the despondency of a young man growing older, finding that his actions represent a perspective he feels that he should have outgrown. Biographer and critic David Browne describes the lyrics as "confused and confusing" and the music as "a languid beauty."[1]</code> | <code>It's Christmas (All Over The World) "It's Christmas (All Over The World)" is a song recorded by Scottish singer Sheena Easton. It was released in November 1985 as the theme song from the soundtrack of Santa Claus: The Movie. The song was written by Bill House and John Hobbs.</code> | <code>The End of the World (Skeeter Davis song) "The End of the World" is a country pop song written by Arthur Kent and Sylvia Dee, for American singer Skeeter Davis. It had success in the 1960s and spawned many covers.</code> | <code>Israel Kamakawiwoʻole His voice became famous outside Hawaii when his album Facing Future was released in 1993. His medley of "Somewhere Over the Rainbow/What a Wonderful World" was released on his albums Ka ʻAnoʻi and Facing Future. It was subsequently featured in several films, television programs, and television commercials.</code> | <code>Make the World Go Away "Make the World Go Away'" is a country-popular music song composed by Hank Cochran. It has become a Top 40 popular success three times: for Timi Yuro (during 1963), for Eddy Arnold (1965), and for the brother-sister duo Donny and Marie Osmond (1975). The original version of the song was recorded by Ray Price during 1963. It has remained a country crooner standard ever since.</code> |
| <code>who produces the most wool in the world</code> | <code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code> | <code>Baa, Baa, Black Sheep As with many nursery rhymes, attempts have been made to find origins and meanings for the rhyme, most which have no corroborating evidence.[1] Katherine Elwes Thomas in The Real Personages of Mother Goose (1930) suggested that the rhyme referred to resentment at the heavy taxation on wool.[5] This has particularly been taken to refer to the medieval English "Great" or "Old Custom" wool tax of 1275, which survived until the fifteenth century.[1] More recently the rhyme has been connected to the slave trade, particularly in the southern United States.[6] This explanation was advanced during debates over political correctness and the use and reform of nursery rhymes in the 1980s, but has no supporting historical evidence.[7] Rather than being negative, the wool of black sheep may have been prized as it could be made into dark cloth without dyeing.[6]</code> | <code>Raymond Group Raymond Group is an Indian branded fabric and fashion retailer, incorporated in 1925. It produces suiting fabric, with a capacity of producing 31 million meters of wool and wool-blended fabrics. Gautam Singhania is the chairman and managing director of the Raymond group.[3]</code> | <code>Silk in the Indian subcontinent Silk in the Indian subcontinent is a luxury good. In India, about 97% of the raw mulberry silk is produced in the five Indian states of Karnataka, Andhra Pradesh, Tamil Nadu, West Bengal and Jammu and Kashmir.[1] Mysore and North Bangalore, the upcoming site of a US$20 million "Silk City", contribute to a majority of silk production.[2] Another emerging silk producer is Tamil Nadu where mulberry cultivation is concentrated in Salem, Erode and Dharmapuri districts. Hyderabad, Andhra Pradesh and Gobichettipalayam, Tamil Nadu were the first locations to have automated silk reeling units.[3] yoyo quantity:::</code> | <code>F. W. Woolworth Company The two Woolworth brothers pioneered and developed merchandising, direct purchasing, sales, and customer service practices commonly used today. Despite its growing to be one of the largest retail chains in the world through most of the 20th century, increased competition led to its decline beginning in the 1980s, while its sporting goods division grew. The chain went out of business in July 1997, when the company decided to focus primarily on sporting goods and renamed itself Venator Group. By 2001, the company focused exclusively on the sporting goods market, changing its name to the present Foot Locker, Inc., changing its ticker symbol from its familiar Z in 2003 to its present ticker (NYSE:Â FL).</code> | <code>Silk Silk's absorbency makes it comfortable to wear in warm weather and while active. Its low conductivity keeps warm air close to the skin during cold weather. It is often used for clothing such as shirts, ties, blouses, formal dresses, high fashion clothes, lining, lingerie, pajamas, robes, dress suits, sun dresses and Eastern folk costumes. For practical use, silk is excellent as clothing that protects from many biting insects that would ordinarily pierce clothing, such as mosquitoes and horseflies.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
</details>
<details><summary>tomaarsen/gooaq-hard-negatives</summary>
#### tomaarsen/gooaq-hard-negatives
* Dataset: tomaarsen/gooaq-hard-negatives
* Size: 800,000 training samples
* Columns: <code>question</code>, <code>answer</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, and <code>negative_5</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string | string | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.99 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 57.82 tokens</li><li>max: 138 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 57.42 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 56.84 tokens</li><li>max: 120 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 57.08 tokens</li><li>max: 155 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 57.54 tokens</li><li>max: 129 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 58.23 tokens</li><li>max: 195 tokens</li></ul> |
* Samples:
| question | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 |
|:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>is toprol xl the same as metoprolol?</code> | <code>Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.</code> | <code>Secondly, metoprolol and metoprolol ER have different brand-name equivalents: Brand version of metoprolol: Lopressor. Brand version of metoprolol ER: Toprol XL.</code> | <code>Pill with imprint 1 is White, Round and has been identified as Metoprolol Tartrate 25 mg.</code> | <code>Interactions between your drugs No interactions were found between Allergy Relief and metoprolol. This does not necessarily mean no interactions exist. Always consult your healthcare provider.</code> | <code>Metoprolol is a type of medication called a beta blocker. It works by relaxing blood vessels and slowing heart rate, which improves blood flow and lowers blood pressure. Metoprolol can also improve the likelihood of survival after a heart attack.</code> | <code>Metoprolol starts to work after about 2 hours, but it can take up to 1 week to fully take effect. You may not feel any different when you take metoprolol, but this doesn't mean it's not working. It's important to keep taking your medicine.</code> |
| <code>are you experienced cd steve hoffman?</code> | <code>The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.</code> | <code>I Saw the Light. Showcasing the unique talent and musical influence of country-western artist Hank Williams, this candid biography also sheds light on the legacy of drug abuse and tormented relationships that contributes to the singer's legend.</code> | <code>(Read our ranking of his top 10.) And while Howard dresses the part of director, any notion of him as a tortured auteur or dictatorial taskmasker — the clichés of the Hollywood director — are tossed aside. He's very nice.</code> | <code>He was a music star too. Where're you people born and brought up? We 're born and brought up here in Anambra State at Nkpor town, near Onitsha.</code> | <code>At the age of 87 he has now retired from his live shows and all the traveling involved. And although he still picks up his Martin Guitar and does a show now and then, his life is now devoted to writing his memoirs.</code> | <code>The owner of the mysterious voice behind all these videos is a man who's seen a lot, visiting a total of 56 intimate celebrity spaces over the course of five years. His name is Joe Sabia — that's him in the photo — and he's currently the VP of creative development at Condé Nast Entertainment.</code> |
| <code>how are babushka dolls made?</code> | <code>Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.</code> | <code>A quick scan of the auction and buy-it-now listings on eBay finds porcelain doll values ranging from around $5 and $10 to several thousand dollars or more but no dolls listed above $10,000.</code> | <code>Japanese dolls are called as ningyō in Japanese and literally translates to 'human form'.</code> | <code>Matyoo: All Fresno Girl dolls come just as real children are born.</code> | <code>As of September 2016, there are over 100 characters. The main toy line includes 13-inch Dolls, the mini-series, and a variety of mini play-sets and plush dolls as well as Lalaloopsy Littles, smaller siblings of the 13-inch dolls. A spin-off known as "Lala-Oopsies" came out in late 2012.</code> | <code>LOL dolls are little baby dolls that come wrapped inside a surprise toy ball. Each ball has layers that contain stickers, secret messages, mix and match accessories–and finally–a doll. ... The doll on the ball is almost never the doll inside. Dolls are released in series, so not every doll is available all the time.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
</details>
<details><summary>bclavie/msmarco-500k-triplets</summary>
#### bclavie/msmarco-500k-triplets
* Dataset: bclavie/msmarco-500k-triplets
* Size: 500,000 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.31 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 82.19 tokens</li><li>max: 216 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 78.99 tokens</li><li>max: 209 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>the most important factor that influences k+ secretion is __________.</code> | <code>The regulation of K+ distribution between the intracellular and extracellular space is referred to as internal K+ balance. The most important factors regulating this movement under normal conditions are insulin and catecholamines (1).</code> | <code>They are both also important for secretion and flow of bile: 1 Cholecystokinin: The name of this hormone describes its effect on the biliary system-cholecysto = gallbladder and kinin = movement. 2 Secretin: This hormone is secreted in response to acid in the duodenum.</code> |
| <code>how much did the mackinac bridge cost to build</code> | <code>The cost to design the project was $3,500,000 (Steinman Company). The cost to construct the bridge was $70, 268,500. Two primary contractors were hired to build the bridge: American Bridge for superstructure - $44,532,900; and Merritt-Chapman and Scott of New York for the foundations - $25,735,600.</code> | <code>When your child needs a dental tooth bridge, you need to know the average cost so you can factor the price into your budget. Several factors affect the price of a bridge, which can run between $700 to $1,500 per tooth. If you have insurance or your child is covered by Medicaid, part of the cost may be covered.</code> |
| <code>when do concussion symptoms appear</code> | <code>Then you can get advice on what to do next. For milder symptoms, the doctor may recommend rest and ask you to watch your child closely for changes, such as a headache that gets worse. Symptoms of a concussion don't always show up right away, and can develop within 24 to 72 hours after an injury.</code> | <code>Concussion: A traumatic injury to soft tissue, usually the brain, as a result of a violent blow, shaking, or spinning. A brain concussion can cause immediate but temporary impairment of brain functions, such as thinking, vision, equilibrium, and consciousness. After a person has had a concussion, he or she is at increased risk for recurrence. Moreover, after a person has several concussions, less of a blow can cause injury, and the person can require more time to recover.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
</details>
<details><summary>sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1</summary>
#### sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
* Dataset: sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
* Size: 800,000 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 9.87 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 85.25 tokens</li><li>max: 211 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 81.18 tokens</li><li>max: 227 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:----------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what are the liberal arts?</code> | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> | <code>Rather than preparing students for a specific career, liberal arts programs focus on cultural literacy and hone communication and analytical skills. They often cover various disciplines, ranging from the humanities to social sciences. 1 Program Levels in Liberal Arts: Associate degree, Bachelor's degree, Master's degree.</code> |
| <code>what are the liberal arts?</code> | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> | <code>Artes Liberales: The historical basis for the modern liberal arts, consisting of the trivium (grammar, logic, and rhetoric) and the quadrivium (arithmetic, geometry, astronomy, and music). General Education: That part of a liberal education curriculum that is shared by all students.</code> |
| <code>what are the liberal arts?</code> | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> | <code>Liberal Arts. Upon completion of the Liberal Arts degree, students will be able to express ideas in coherent, creative, and appropriate forms, orally and in writing. Students will be able to apply their reading abilities in order to interconnect an understanding of resources to academic, professional, and personal interests.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
</details>
<details><summary>sentence-transformers/gooaq</summary>
#### sentence-transformers/gooaq
* Dataset: sentence-transformers/gooaq
* Size: 800,000 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 12.19 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 58.34 tokens</li><li>max: 124 tokens</li></ul> |
* Samples:
| question | answer |
|:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>is toprol xl the same as metoprolol?</code> | <code>Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.</code> |
| <code>are you experienced cd steve hoffman?</code> | <code>The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.</code> |
| <code>how are babushka dolls made?</code> | <code>Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
</details>
<details><summary>sentence-transformers/natural-questions</summary>
#### sentence-transformers/natural-questions
* Dataset: sentence-transformers/natural-questions
* Size: 100,231 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: 10 tokens</li><li>mean: 12.47 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 138.32 tokens</li><li>max: 556 tokens</li></ul> |
* Samples:
| query | answer |
|:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>when did richmond last play in a preliminary final</code> | <code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tig...</code> |
| <code>who sang what in the world's come over you</code> | <code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code> |
| <code>who produces the most wool in the world</code> | <code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
</details>
<details><summary>sentence-transformers/quora-duplicates</summary>
#### sentence-transformers/quora-duplicates
* Dataset: sentence-transformers/quora-duplicates
* Size: 101,762 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 13.85 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.63 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.68 tokens</li><li>max: 61 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|
| <code>Why in India do we not have one on one political debate as in USA?</code> | <code>Why cant we have a public debate between politicians in India like the one in US?</code> | <code>Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?</code> |
| <code>What is OnePlus One?</code> | <code>How is oneplus one?</code> | <code>Why is OnePlus One so good?</code> |
| <code>Does our mind control our emotions?</code> | <code>How do smart and successful people control their emotions?</code> | <code>How can I control my positive emotions for the people whom I love but they don't care about me?</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
</details>
<details><summary>sentence-transformers/s2orc</summary>
#### sentence-transformers/s2orc
* Dataset: sentence-transformers/s2orc
* Size: 800,000 training samples
* Columns: <code>title</code> and <code>abstract</code>
* Approximate statistics based on the first 1000 samples:
| | title | abstract |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 20.08 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 131.03 tokens</li><li>max: 332 tokens</li></ul> |
* Samples:
| title | abstract |
|:----------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Syntheses, Structures and Properties of Two Transition Metal-Flexible Ligand Coordination Polymers</code> | <code>Two coordination polymers based on 3,5-bis(4-carboxyphenylmethyloxy) benzoic acid (H3L), [M(HL)]·2H2O M = Mn(1), Co(2), have been synthesized under hydrothermal conditions. Their structures have been determined by single-crystal X-ray diffraction and further characterized by elemental analysis, IR spectra and TGA. The two complexes possess 3D framework with diamond channels resulting from the trans-configuration of the flexible ligand and three coordination modes, 3(η2, η1), 2(η1, η1), η1, of carboxyl groups in the ligand. The framework can be represented with Schlafli symbol of (48·66)(47·66). The wall of the channel consists of left- or right-handed helical polymeric chains. UV–visible–NIR and photoluminescence spectra, magnetic properties of 1 and 2 have also been discussed.</code> |
| <code>Discussion on the Influence and Development of Technical Aesthetics in Modern Landscape Design</code> | <code>The source of technical aesthetics was introduced and its meaning was explained.The relations between technical aesthetics and modern landscpae design were discussed.The embodiment of technical aesthetics in landscpae design was discussed in the aspects of new material,new technology,new structureand new apparatus.It was put forward that the the development direction of technical aesthetics were tending to sensibility, native land and zoology.</code> |
| <code>GRIN optics for dual-band IR sensors (Conference Presentation)</code> | <code>Graded index (GRIN) optics offer potential for both weight savings and increased performance but have until recently been limited to visible and NIR bands (wavelengths shorter than about 0.9 µm). NRL has developed glass-based IR-GRIN lenses compatible with SWIR-LWIR wavebands. Recent designs show the potential for significant SWaP reduction benefits and improved performance using IR-GRIN lens elements in dual-band, MWIR-LWIR sensors. The SWaP and performance advantages of IR-GRIN lenses in platform-relevant dual-band imagers will be presented.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
</details>
<details><summary>sentence-transformers/codesearchnet</summary>
#### sentence-transformers/codesearchnet
* Dataset: sentence-transformers/codesearchnet
* Size: 800,000 training samples
* Columns: <code>comment</code> and <code>code</code>
* Approximate statistics based on the first 1000 samples:
| | comment | code |
|:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 28.98 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 166.72 tokens</li><li>max: 1024 tokens</li></ul> |
* Samples:
| comment | code |
|:-----------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Computes the new parent id for the node being moved.<br><br>@return int</code> | <code>protected function parentId()<br> {<br> switch ( $this->position )<br> {<br> case 'root':<br> return null;<br><br> case 'child':<br> return $this->target->getKey();<br><br> default:<br> return $this->target->getParentId();<br> }<br> }</code> |
| <code>// SetWinSize overwrites the playlist's window size.</code> | <code>func (p *MediaPlaylist) SetWinSize(winsize uint) error {<br> if winsize > p.capacity {<br> return errors.New("capacity must be greater than winsize or equal")<br> }<br> p.winsize = winsize<br> return nil<br>}</code> |
| <code>Show the sidebar and squish the container to make room for the sidebar.<br>If hideOthers is true, hide other open sidebars.</code> | <code>function() {<br> var options = this.options;<br><br> if (options.hideOthers) {<br> this.secondary.each(function() {<br> var sidebar = $(this);<br><br> if (sidebar.hasClass('is-expanded')) {<br> sidebar.toolkit('offCanvas', 'hide');<br> }<br> });<br> }<br><br> this.fireEvent('showing');<br><br> this.container.addClass('move-' + this.opposite);<br><br> this.element<br> .reveal()<br> .addClass('is-expanded')<br> .aria('expanded', true);<br><br> if (options.stopScroll) {<br> $('body').addClass('no-scroll');<br> }<br><br> this.fireEvent('shown');<br> }</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
</details>
<details><summary>sentence-transformers/stackexchange-duplicates</summary>
#### sentence-transformers/stackexchange-duplicates
* Dataset: sentence-transformers/stackexchange-duplicates
* Size: 250,460 training samples
* Columns: <code>body1</code> and <code>body2</code>
* Approximate statistics based on the first 1000 samples:
| | body1 | body2 |
|:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 13 tokens</li><li>mean: 174.01 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 156.88 tokens</li><li>max: 1024 tokens</li></ul> |
* Samples:
| body1 | body2 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>I've been wondering about this for years. It seems like a pretty obvious question, so I'm surprised not to have found it addressed among the other Tolkien minutiae on this site. Hopefully I haven't missed it, but anyway, here goes... In Tolkien's Middle-Earth writings, Evil cannot create things, only twist and warp what already exists. Thus, Orcs are twisted Elves, Trolls are twisted Ents, etc. So then, what's the original source for Dragons? They look pretty original to me! The only template that seems even remotely possible is the Eagles, as they're both powerful fliers, but the connection seems very remote indeed. Also, as twisted copies Orcs and Trolls are markedly inferior to Elves and Ents respectively, but I'm not aware of any text describing Dragons as inferior to Eagles.</code> | <code>All that I know of Smaug is that he (she?) came out of nowhere to attack and conquer Erebor. Where exactly did he come from? In fact, what are the origins of dragons? Did Ilúvatar create them or did they come from somewhere else?</code> |
| <code>Hi i have some data which coming out from database in form of table like this, first i match some data with searching and then display it on page now i need to download it as csv file format please help me check my code and i'm new in php. please check image too for the reference and please please help me //import.php // echo "<pre>"; //print_r($_POST);die(); $keyword = $_POST['keyword']; $csvname = $_POST['csv_file']; ?> <table border ="1"> <thead> <tr> <th>id</th> <th>title</th> <th>count</th> </tr> </thead> <?php $row = 0; if (($handle = fopen("idata.csv", "r",)) !== FALSE) { while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) { $num = count($data); // echo "<p> $num fields in line $row: <br /></p>\n"; $row++; for ($c=0; $c < $num; $c++) { // echo $data[$c] . "<br...</code> | <code>What is the most efficient way to convert a MySQL query to CSV in PHP please? It would be best to avoid temp files as this reduces portability (dir paths and setting file-system permissions required). The CSV should also include one top line of field names.</code> |
| <code>Following along in tutorials I see the blur filter being used. I am using Blender 2.69 and I can't locate it visually or even with a search. Actually, there is no "Filters" category at all. Do I have to download something to get it?</code> | <code>I have been following tutorial until I started adding nodes. The problem is that he has completely different nodes than I have. Even nodes that are created at start are different (I have Material and Output and he has Render Layers and Composite). Have I missed something or should I use different nodes than he?</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 384
- `learning_rate`: 1.0
- `weight_decay`: 6e-05
- `num_train_epochs`: 1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 384
- `per_device_eval_batch_size`: 8
- `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`: 1.0
- `weight_decay`: 6e-05
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `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
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0267 | 500 | 4.3558 |
| 0.0535 | 1000 | 3.0724 |
| 0.0802 | 1500 | 2.979 |
| 0.1070 | 2000 | 2.9205 |
| 0.1337 | 2500 | 3.0679 |
| 0.1604 | 3000 | 2.837 |
| 0.1872 | 3500 | 3.2635 |
| 0.2139 | 4000 | 2.7602 |
| 0.2407 | 4500 | 2.6911 |
| 0.2674 | 5000 | 2.6963 |
| 0.2941 | 5500 | 2.8504 |
| 0.3209 | 6000 | 2.7501 |
| 0.3476 | 6500 | 2.6315 |
| 0.3744 | 7000 | 2.5372 |
| 0.4011 | 7500 | 2.8814 |
| 0.4278 | 8000 | 2.2826 |
| 0.4546 | 8500 | 2.764 |
| 0.4813 | 9000 | 2.4418 |
| 0.5080 | 9500 | 2.3762 |
| 0.5348 | 10000 | 2.5542 |
| 0.5615 | 10500 | 2.2653 |
| 0.5883 | 11000 | 2.5098 |
| 0.6150 | 11500 | 2.3009 |
| 0.6417 | 12000 | 2.4029 |
| 0.6685 | 12500 | 2.1538 |
| 0.6952 | 13000 | 2.6398 |
| 0.7220 | 13500 | 2.3101 |
| 0.7487 | 14000 | 2.8489 |
| 0.7754 | 14500 | 2.3822 |
| 0.8022 | 15000 | 2.3035 |
| 0.8289 | 15500 | 2.4212 |
| 0.8557 | 16000 | 2.1447 |
| 0.8824 | 16500 | 1.985 |
| 0.9091 | 17000 | 2.1427 |
| 0.9359 | 17500 | 2.3002 |
| 0.9626 | 18000 | 2.2671 |
| 0.9894 | 18500 | 2.3033 |
### Framework Versions
- Python: 3.12.10
- Sentence Transformers: 5.1.2
- Transformers: 4.57.3
- PyTorch: 2.7.1+cu126
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.22.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### AnglELoss
```bibtex
@inproceedings{li-li-2024-aoe,
title = "{A}o{E}: Angle-optimized Embeddings for Semantic Textual Similarity",
author = "Li, Xianming and Li, Jing",
year = "2024",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.101/",
doi = "10.18653/v1/2024.acl-long.101"
}
```
#### CoSENTLoss
```bibtex
@article{10531646,
author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title={CoSENT: Consistent Sentence Embedding via Similarity Ranking},
year={2024},
doi={10.1109/TASLP.2024.3402087}
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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