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ModernBERT-small-sts

Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:2637346
loss:CachedMultipleNegativesSymmetricRankingLoss
loss:CachedMultipleNegativesRankingLoss
loss:CoSENTLoss
Eval Results
text-embeddings-inference
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 ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:2637346
- loss:CachedMultipleNegativesSymmetricRankingLoss
- loss:CachedMultipleNegativesRankingLoss
- loss:CoSENTLoss
widget:
- source_sentence: A modern bathtub in a bathroom is displayed.
sentences:
- Different types of tiles are on the walls, floor and tub.
- A man sitting on a park bench looking towards a fountain and sculpture.
- A bathroom with a shower and his and her sinks.
- source_sentence: The people are sleeping.
sentences:
- A white dog swims in the water while holding a red object in its mouth.
- A man and young boy asleep in a chair.
- A group of people sit in an open, plaza-like area with large bushes and victorian-styled
buildings in a row behind them, many of which are made indistinct by a heavy blur
on the right side of the picture.
- source_sentence: A man is playing the drums.
sentences:
- A man plays the drum.
- A woman is swimming in the water.
- The lady peeled the shrimp.
- source_sentence: who sings i'm so tired of being alone
sentences:
- Tree of life (biology) The term phylogeny for the evolutionary relationships of
species through time was coined by Ernst Haeckel, who went further than Darwin
in proposing phylogenic histories of life. In contemporary usage, tree of life
refers to the compilation of comprehensive phylogenetic databases rooted at the
last universal common ancestor of life on Earth. The Open Tree of Life, first
published 2015, is a project to compile such a database for free public access.
- The Thomas Crown Affair (1968 film) The Thomas Crown Affair is a 1968 film directed
and produced by Norman Jewison and starring Steve McQueen and Faye Dunaway. This
heist film was nominated for two Academy Awards, winning Best Original Song for
Michel Legrand's "Windmills of Your Mind". A remake was released in 1999 and a
second remake is currently in the development stages.
- 'Tired of Being Alone In addition to Texas, "Tired of Being Alone" has also been
covered by Michael Bolton, Tom Jones, the Subdudes and by Eran James. Graham Bonnet
of Rainbow, MSG, and Alcatrazz fame covered "Tired of Being Alone" on 1977''s
"Graham Bonnet". The soul group Quiet Elegance, who were stablemates at Hi Records
with Green and had toured with him, also released a cover of the song on their
albums You''ve Got My Mind Messed Up (1990) and The Complete Quiet Elegance (2003).
Tarja Turunen covered the song on her 2012 album Act I: Live in Rosario. American
singer Sybil released a cover as a non-album single in 1996, peaking at #53 in
the UK. The original Al Green version was featured in the 1995 film Dead Presidents.'
- source_sentence: A sleeping baby in a pink striped outfit.
sentences:
- Three young men and a young woman wearing sneakers are leaping in midair at the
top of a flight of concrete stairs.
- A little baby cradled in someones arms.
- A group of hikers traveling along a rock strewn creek bed.
datasets:
- sentence-transformers/all-nli
- sentence-transformers/quora-duplicates
- sentence-transformers/natural-questions
- sentence-transformers/stsb
- sentence-transformers/sentence-compression
- sentence-transformers/simple-wiki
- sentence-transformers/altlex
- sentence-transformers/coco-captions
- sentence-transformers/flickr30k-captions
- sentence-transformers/yahoo-answers
- sentence-transformers/stackexchange-duplicates
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- pearson_cosine
- spearman_cosine
model-index:
- name: ModernBERT-small for General Purpose Similarity
results:
- task:
type: triplet
name: Triplet
dataset:
name: all nli dev
type: all-nli-dev
metrics:
- type: cosine_accuracy
value: 0.8807715773582458
name: Cosine Accuracy
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.8290433363537696
name: Pearson Cosine
- type: spearman_cosine
value: 0.8276208329210781
name: Spearman Cosine
---
# ModernBERT-small for General Purpose Similarity
This is a [sentence-transformers](https://www.SBERT.net) model trained on the [nli](https://huggingface.co/datasets/sentence-transformers/all-nli), [quora](https://huggingface.co/datasets/sentence-transformers/quora-duplicates), [natural_questions](https://huggingface.co/datasets/sentence-transformers/natural-questions), [stsb](https://huggingface.co/datasets/sentence-transformers/stsb), [sentence_compression](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [simple_wiki](https://huggingface.co/datasets/sentence-transformers/simple-wiki), [altlex](https://huggingface.co/datasets/sentence-transformers/altlex), [coco_captions](https://huggingface.co/datasets/sentence-transformers/coco-captions), [flickr30k_captions](https://huggingface.co/datasets/sentence-transformers/flickr30k-captions), [yahoo_answers](https://huggingface.co/datasets/sentence-transformers/yahoo-answers) and [stack_exchange](https://huggingface.co/datasets/sentence-transformers/stackexchange-duplicates) 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.
This model is based on the wide architecture of [johnnyboycurtis/ModernBERT-small](https://huggingface.co/johnnyboycurtis/ModernBERT-small)
```
small_modernbert_config = ModernBertConfig(
hidden_size=384, # A common dimension for small embedding models
num_hidden_layers=12, # Significantly fewer layers than the base's 22
num_attention_heads=6, # Must be a divisor of hidden_size
intermediate_size=1536, # 4 * hidden_size -- VERY WIDE!!
max_position_embeddings=1024, # Max sequence length for the model; originally 8192
)
model = ModernBertModel(modernbert_small_config)
```
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- [quora](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- [natural_questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- [stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
- [sentence_compression](https://huggingface.co/datasets/sentence-transformers/sentence-compression)
- [simple_wiki](https://huggingface.co/datasets/sentence-transformers/simple-wiki)
- [altlex](https://huggingface.co/datasets/sentence-transformers/altlex)
- [coco_captions](https://huggingface.co/datasets/sentence-transformers/coco-captions)
- [flickr30k_captions](https://huggingface.co/datasets/sentence-transformers/flickr30k-captions)
- [yahoo_answers](https://huggingface.co/datasets/sentence-transformers/yahoo-answers)
- [stack_exchange](https://huggingface.co/datasets/sentence-transformers/stackexchange-duplicates)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 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})
)
```
## 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("sentence_transformers_model_id")
# Run inference
queries = [
"A sleeping baby in a pink striped outfit.",
]
documents = [
'A little baby cradled in someones arms.',
'A group of hikers traveling along a rock strewn creek bed.',
'Three young men and a young woman wearing sneakers are leaping in midair at the top of a flight of concrete stairs.',
]
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([[ 0.5804, 0.0193, -0.1261]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Dataset: `all-nli-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.8808** |
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.829 |
| **spearman_cosine** | **0.8276** |
<!--
## 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>nli</summary>
#### nli
* Dataset: [nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 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: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.91 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.49 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 64
}
```
</details>
<details><summary>quora</summary>
#### quora
* Dataset: [quora](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* 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>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 64
}
```
</details>
<details><summary>natural_questions</summary>
#### natural_questions
* Dataset: [natural_questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* 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": 64
}
```
</details>
<details><summary>stsb</summary>
#### stsb
* Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.16 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.12 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
| <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> |
| <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> |
| <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</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>sentence_compression</summary>
#### sentence_compression
* Dataset: [sentence_compression](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90)
* Size: 180,000 training samples
* Columns: <code>text</code> and <code>simplified</code>
* Approximate statistics based on the first 1000 samples:
| | text | simplified |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 33.95 tokens</li><li>max: 127 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.56 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| text | simplified |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|
| <code>The USHL completed an expansion draft on Monday as 10 players who were on the rosters of USHL teams during the 2009-10 season were selected by the League's two newest entries, the Muskegon Lumberjacks and Dubuque Fighting Saints.</code> | <code>USHL completes expansion draft</code> |
| <code>Major League Baseball Commissioner Bud Selig will be speaking at St. Norbert College next month.</code> | <code>Bud Selig to speak at St. Norbert College</code> |
| <code>It's fresh cherry time in Michigan and the best time to enjoy this delicious and nutritious fruit.</code> | <code>It's cherry time</code> |
* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 64
}
```
</details>
<details><summary>simple_wiki</summary>
#### simple_wiki
* Dataset: [simple_wiki](https://huggingface.co/datasets/sentence-transformers/simple-wiki) at [60fd9b4](https://huggingface.co/datasets/sentence-transformers/simple-wiki/tree/60fd9b4680642ace0e2604cc2de44d376df419a7)
* Size: 102,225 training samples
* Columns: <code>text</code> and <code>simplified</code>
* Approximate statistics based on the first 1000 samples:
| | text | simplified |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 35.55 tokens</li><li>max: 173 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 29.29 tokens</li><li>max: 135 tokens</li></ul> |
* Samples:
| text | simplified |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The greatest example has been in his present job ( then , Minister for Foreign Affairs ) , where he has perforce concentrated on Anglo-Irish relations and , in particular the North ( i.e. , Northern Ireland ) .</code> | <code>The greatest example has been in his present job ( then , Minister for Foreign Affairs ) , where he has perforce concentrated on Anglo-Irish relations and , in particular Northern Ireland ( .</code> |
| <code>His reputation rose further when opposition leaders under parliamentary privilege alleged that Taoiseach Charles Haughey , who in January 1982 had been Leader of the Opposition , had not merely rung the President 's Office but threatened to end the career of the army officer who took the call and who , on Hillery 's explicit instructions , had refused to put through the call to the President .</code> | <code>President Hillery refused to speak to any opposition party politicians , but when Charles Haughey , who was Leader of the Opposition , had rang the President 's Office he threatened to end the career of the army officer answered and refused on Hillery 's explicit orders to put the call through to the President .</code> |
| <code>He considered returning to medicine , perhaps moving with his wife , Maeve ( also a doctor ) to Africa .</code> | <code>He thought about returning to medicine , perhaps moving with his wife , Maeve ( also a doctor ) to Africa .</code> |
* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 64
}
```
</details>
<details><summary>altlex</summary>
#### altlex
* Dataset: [altlex](https://huggingface.co/datasets/sentence-transformers/altlex) at [97eb209](https://huggingface.co/datasets/sentence-transformers/altlex/tree/97eb20963455c361d5a81c107c3596cff9e0cd82)
* Size: 112,696 training samples
* Columns: <code>text</code> and <code>simplified</code>
* Approximate statistics based on the first 1000 samples:
| | text | simplified |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 32.19 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 26.81 tokens</li><li>max: 115 tokens</li></ul> |
* Samples:
| text | simplified |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>A set of 31 guns , cast 1729-1749 by the first master founder at the Royal Foundry , later the Royal Arsenal , Woolwich , were used to fire salutes until 1907 , often for Queen Victoria , who was a frequent visitor .</code> | <code>A set of 31 guns , cast 1729-1749 by the first master founder at the Royal Foundry , later the Royal Arsenal , Woolwich , were used to fire salutes until 1907 , often for Queen Victoria who was a frequent visitor .</code> |
| <code>In 1929 , the building became vacant , and was given to Prince Edward , Prince of Wales , by his father , King George V . This became the Prince 's chief residence and was used extensively by him for entertaining and as a country retreat .</code> | <code>In 1929 , the building became vacant , and was given to Prince Edward , the Prince of Wales by his father , King George V . This became the Prince 's chief residence , and was used extensively by the Prince for entertaining and as a country retreat .</code> |
| <code>Additions included an octagon room in the north-east side , in which the King regularly had dinner .</code> | <code>Additions included an octagon room in the North-East side , where the King regularly had dinner .</code> |
* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 64
}
```
</details>
<details><summary>coco_captions</summary>
#### coco_captions
* Dataset: [coco_captions](https://huggingface.co/datasets/sentence-transformers/coco-captions) at [bd26018](https://huggingface.co/datasets/sentence-transformers/coco-captions/tree/bd2601822b9af9a41656d678ffbd5c80d81e276a)
* Size: 414,010 training samples
* Columns: <code>caption1</code> and <code>caption2</code>
* Approximate statistics based on the first 1000 samples:
| | caption1 | caption2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 13.8 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 13.8 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
| caption1 | caption2 |
|:-------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------|
| <code>A clock that blends in with the wall hangs in a bathroom. </code> | <code>A very clean and well decorated empty bathroom</code> |
| <code>A very clean and well decorated empty bathroom</code> | <code>A bathroom with a border of butterflies and blue paint on the walls above it.</code> |
| <code>A bathroom with a border of butterflies and blue paint on the walls above it.</code> | <code>An angled view of a beautifully decorated bathroom.</code> |
* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 64
}
```
</details>
<details><summary>flickr30k_captions</summary>
#### flickr30k_captions
* Dataset: [flickr30k_captions](https://huggingface.co/datasets/sentence-transformers/flickr30k-captions) at [0ef0ce3](https://huggingface.co/datasets/sentence-transformers/flickr30k-captions/tree/0ef0ce31492fd8dc161ed483a40d3c4894f9a8c1)
* Size: 158,881 training samples
* Columns: <code>caption1</code> and <code>caption2</code>
* Approximate statistics based on the first 1000 samples:
| | caption1 | caption2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.41 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.41 tokens</li><li>max: 64 tokens</li></ul> |
* Samples:
| caption1 | caption2 |
|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
| <code>Two men in green shirts are standing in a yard.</code> | <code>Two young, White males are outside near many bushes.</code> |
| <code>Two young, White males are outside near many bushes.</code> | <code>Two young guys with shaggy hair look at their hands while hanging out in the yard.</code> |
| <code>Two young guys with shaggy hair look at their hands while hanging out in the yard.</code> | <code>A man in a blue shirt standing in a garden.</code> |
* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 64
}
```
</details>
<details><summary>yahoo_answers</summary>
#### yahoo_answers
* Dataset: [yahoo_answers](https://huggingface.co/datasets/sentence-transformers/yahoo-answers) at [93b3605](https://huggingface.co/datasets/sentence-transformers/yahoo-answers/tree/93b3605c508cf93e3666c9d3e34640b5fe62b507)
* Size: 599,417 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: 12 tokens</li><li>mean: 57.04 tokens</li><li>max: 309 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 115.16 tokens</li><li>max: 992 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>why doesn't an optical mouse work on a glass table? or even on some surfaces?</code> | <code>why doesn't an optical mouse work on a glass table? Optical mice use an LED and a camera to rapidly capture images of the surface beneath the mouse. The infomation from the camera is analyzed by a DSP (Digital Signal Processor) and used to detect imperfections in the underlying surface and determine motion. Some materials, such as glass, mirrors or other very shiny, uniform surfaces interfere with the ability of the DSP to accurately analyze the surface beneath the mouse. \nSince glass is transparent and very uniform, the mouse is unable to pick up enough imperfections in the underlying surface to determine motion. Mirrored surfaces are also a problem, since they constantly reflect back the same image, causing the DSP not to recognize motion properly. When the system is unable to see surface changes associated with movement, the mouse will not work properly.</code> |
| <code>What is the best off-road motorcycle trail ? long-distance trail throughout CA</code> | <code>What is the best off-road motorcycle trail ? i hear that the mojave road is amazing!<br />\nsearch for it online.</code> |
| <code>What is Trans Fat? How to reduce that? I heard that tras fat is bad for the body. Why is that? Where can we find it in our daily food?</code> | <code>What is Trans Fat? How to reduce that? Trans fats occur in manufactured foods during the process of partial hydrogenation, when hydrogen gas is bubbled through vegetable oil to increase shelf life and stabilize the original polyunsatured oil. The resulting fat is similar to saturated fat, which raises "bad" LDL cholesterol and can lead to clogged arteries and heart disease. \nUntil very recently, food labels were not required to list trans fats, and this health risk remained hidden to consumers. In early July, FDA regulations changed, and food labels will soon begin identifying trans fat content in processed foods.</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": 64
}
```
</details>
<details><summary>stack_exchange</summary>
#### stack_exchange
* Dataset: [stack_exchange](https://huggingface.co/datasets/sentence-transformers/stackexchange-duplicates) at [1c9657a](https://huggingface.co/datasets/sentence-transformers/stackexchange-duplicates/tree/1c9657aec12d9e101667bb9593efcc623c4a68ff)
* Size: 304,525 training samples
* Columns: <code>title1</code> and <code>title2</code>
* Approximate statistics based on the first 1000 samples:
| | title1 | title2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 14.71 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.48 tokens</li><li>max: 71 tokens</li></ul> |
* Samples:
| title1 | title2 |
|:----------------------------------------------------------------------------------|:-------------------------------------------------------------|
| <code>what is the advantage of using the GPU rendering options in Android?</code> | <code>Can anyone explain all these Developer Options?</code> |
| <code>Blank video when converting uncompressed AVI files with ffmpeg</code> | <code>FFmpeg lossy compression problems</code> |
| <code>URL Rewriting of a query string in php</code> | <code>How to create friendly URL in php?</code> |
* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 64
}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `learning_rate`: 0.0005
- `weight_decay`: 0.01
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.05
- `bf16`: True
- `bf16_full_eval`: True
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 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`: 0.0005
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `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
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: True
- `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`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: 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`: False
- `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`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | all-nli-dev_cosine_accuracy | sts-dev_spearman_cosine |
|:----------:|:---------:|:-------------:|:---------------------------:|:-----------------------:|
| 0.0243 | 500 | 2.0912 | - | - |
| 0.0485 | 1000 | 1.4267 | - | - |
| 0.0728 | 1500 | 1.2426 | - | - |
| 0.0970 | 2000 | 1.0654 | 0.8136 | 0.7436 |
| 0.1213 | 2500 | 0.8238 | - | - |
| 0.1456 | 3000 | 0.8801 | - | - |
| 0.1698 | 3500 | 0.7807 | - | - |
| 0.1941 | 4000 | 0.7651 | 0.8284 | 0.7611 |
| 0.2183 | 4500 | 0.6838 | - | - |
| 0.2426 | 5000 | 0.6796 | - | - |
| 0.2668 | 5500 | 0.6014 | - | - |
| 0.2911 | 6000 | 0.5967 | 0.8360 | 0.7741 |
| 0.3154 | 6500 | 0.6318 | - | - |
| 0.3396 | 7000 | 0.5821 | - | - |
| 0.3639 | 7500 | 0.5258 | - | - |
| 0.3881 | 8000 | 0.6353 | 0.8463 | 0.7951 |
| 0.4124 | 8500 | 0.5788 | - | - |
| 0.4367 | 9000 | 0.5956 | - | - |
| 0.4609 | 9500 | 0.5453 | - | - |
| 0.4852 | 10000 | 0.5218 | 0.8522 | 0.7960 |
| 0.5094 | 10500 | 0.4546 | - | - |
| 0.5337 | 11000 | 0.5363 | - | - |
| 0.5580 | 11500 | 0.5055 | - | - |
| 0.5822 | 12000 | 0.5157 | 0.8574 | 0.8133 |
| 0.6065 | 12500 | 0.4474 | - | - |
| 0.6307 | 13000 | 0.5242 | - | - |
| 0.6550 | 13500 | 0.4406 | - | - |
| 0.6792 | 14000 | 0.4766 | 0.8628 | 0.8055 |
| 0.7035 | 14500 | 0.5492 | - | - |
| 0.7278 | 15000 | 0.4667 | - | - |
| 0.7520 | 15500 | 0.401 | - | - |
| 0.7763 | 16000 | 0.4805 | 0.8662 | 0.8041 |
| 0.8005 | 16500 | 0.4524 | - | - |
| 0.8248 | 17000 | 0.5427 | - | - |
| 0.8491 | 17500 | 0.44 | - | - |
| 0.8733 | 18000 | 0.4774 | 0.8691 | 0.8126 |
| 0.8976 | 18500 | 0.3869 | - | - |
| 0.9218 | 19000 | 0.4031 | - | - |
| 0.9461 | 19500 | 0.409 | - | - |
| 0.9704 | 20000 | 0.3779 | 0.8706 | 0.8220 |
| 0.9946 | 20500 | 0.3703 | - | - |
| 1.0189 | 21000 | 0.3279 | - | - |
| 1.0431 | 21500 | 0.2885 | - | - |
| 1.0674 | 22000 | 0.2838 | 0.8786 | 0.8185 |
| 1.0917 | 22500 | 0.3564 | - | - |
| 1.1159 | 23000 | 0.2787 | - | - |
| 1.1402 | 23500 | 0.3007 | - | - |
| 1.1644 | 24000 | 0.3477 | 0.8759 | 0.8215 |
| 1.1887 | 24500 | 0.3176 | - | - |
| 1.2129 | 25000 | 0.2671 | - | - |
| 1.2372 | 25500 | 0.3309 | - | - |
| 1.2615 | 26000 | 0.3487 | 0.8744 | 0.8201 |
| 1.2857 | 26500 | 0.3497 | - | - |
| 1.3100 | 27000 | 0.2859 | - | - |
| 1.3342 | 27500 | 0.3018 | - | - |
| 1.3585 | 28000 | 0.2812 | 0.8767 | 0.8229 |
| 1.3828 | 28500 | 0.3071 | - | - |
| 1.4070 | 29000 | 0.2609 | - | - |
| 1.4313 | 29500 | 0.3083 | - | - |
| 1.4555 | 30000 | 0.3113 | 0.8782 | 0.8253 |
| 1.4798 | 30500 | 0.279 | - | - |
| 1.5041 | 31000 | 0.3082 | - | - |
| 1.5283 | 31500 | 0.2824 | - | - |
| 1.5526 | 32000 | 0.2987 | 0.8786 | 0.8256 |
| 1.5768 | 32500 | 0.3417 | - | - |
| 1.6011 | 33000 | 0.3075 | - | - |
| 1.6253 | 33500 | 0.2631 | - | - |
| 1.6496 | 34000 | 0.2642 | 0.8773 | 0.8249 |
| 1.6739 | 34500 | 0.2804 | - | - |
| 1.6981 | 35000 | 0.244 | - | - |
| 1.7224 | 35500 | 0.29 | - | - |
| 1.7466 | 36000 | 0.251 | 0.8785 | 0.8262 |
| 1.7709 | 36500 | 0.2476 | - | - |
| 1.7952 | 37000 | 0.2807 | - | - |
| 1.8194 | 37500 | 0.2558 | - | - |
| 1.8437 | 38000 | 0.2536 | 0.8777 | 0.8285 |
| 1.8679 | 38500 | 0.2779 | - | - |
| 1.8922 | 39000 | 0.2567 | - | - |
| 1.9165 | 39500 | 0.3665 | - | - |
| **1.9407** | **40000** | **0.27** | **0.8796** | **0.8299** |
| 1.9650 | 40500 | 0.2635 | - | - |
| 1.9892 | 41000 | 0.2477 | - | - |
| 2.0135 | 41500 | 0.2386 | - | - |
| 2.0377 | 42000 | 0.2477 | 0.8783 | 0.8284 |
| 2.0620 | 42500 | 0.2396 | - | - |
| 2.0863 | 43000 | 0.1781 | - | - |
| 2.1105 | 43500 | 0.1858 | - | - |
| 2.1348 | 44000 | 0.1812 | 0.8791 | 0.8278 |
| 2.1590 | 44500 | 0.2185 | - | - |
| 2.1833 | 45000 | 0.2431 | - | - |
| 2.2076 | 45500 | 0.1812 | - | - |
| 2.2318 | 46000 | 0.2301 | 0.8806 | 0.8282 |
| 2.2561 | 46500 | 0.2169 | - | - |
| 2.2803 | 47000 | 0.2074 | - | - |
| 2.3046 | 47500 | 0.2229 | - | - |
| 2.3289 | 48000 | 0.2257 | 0.8803 | 0.8276 |
| 2.3531 | 48500 | 0.1867 | - | - |
| 2.3774 | 49000 | 0.2276 | - | - |
| 2.4016 | 49500 | 0.214 | - | - |
| 2.4259 | 50000 | 0.2085 | 0.8808 | 0.8276 |
| 2.4501 | 50500 | 0.2198 | - | - |
| 2.4744 | 51000 | 0.231 | - | - |
| 2.4987 | 51500 | 0.2395 | - | - |
| 2.5229 | 52000 | 0.2204 | 0.8808 | 0.8276 |
| 2.5472 | 52500 | 0.1864 | - | - |
| 2.5714 | 53000 | 0.3129 | - | - |
| 2.5957 | 53500 | 0.2224 | - | - |
| 2.6200 | 54000 | 0.1839 | 0.8808 | 0.8276 |
| 2.6442 | 54500 | 0.2032 | - | - |
| 2.6685 | 55000 | 0.246 | - | - |
| 2.6927 | 55500 | 0.199 | - | - |
| 2.7170 | 56000 | 0.2089 | 0.8808 | 0.8276 |
| 2.7413 | 56500 | 0.2235 | - | - |
| 2.7655 | 57000 | 0.2168 | - | - |
| 2.7898 | 57500 | 0.2063 | - | - |
| 2.8140 | 58000 | 0.2202 | 0.8808 | 0.8276 |
| 2.8383 | 58500 | 0.2077 | - | - |
| 2.8625 | 59000 | 0.1876 | - | - |
| 2.8868 | 59500 | 0.2204 | - | - |
| 2.9111 | 60000 | 0.2248 | 0.8808 | 0.8276 |
| 2.9353 | 60500 | 0.1974 | - | - |
| 2.9596 | 61000 | 0.2084 | - | - |
| 2.9838 | 61500 | 0.2312 | - | - |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 5.0.0
- Transformers: 4.53.1
- PyTorch: 2.7.1+cu128
- Accelerate: 1.8.1
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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",
}
```
#### 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}
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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