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xlm-roberta-large-sts-matryoshka

Sentence Similarity
sentence-transformers
Safetensors
English
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:5749
loss:MatryoshkaLoss
loss:CoSENTLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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---
base_model: FacebookAI/xlm-roberta-large
datasets:
- sentence-transformers/stsb
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5749
- loss:MatryoshkaLoss
- loss:CoSENTLoss
widget:
- source_sentence: A chef is preparing some food.
sentences:
- Five birds stand on the snow.
- A chef prepared a meal.
- There is no 'still' that is not relative to some other object.
- source_sentence: A woman is adding oil on fishes.
sentences:
- Large cruise ship floating on the water.
- It refers to the maximum f-stop (which is defined as the ratio of focal length
to effective aperture diameter).
- The woman is cutting potatoes.
- source_sentence: The player shoots the winning points.
sentences:
- Minimum wage laws hurt the least skilled, least productive the most.
- The basketball player is about to score points for his team.
- Three televisions, on on the floor, the other two on a box.
- source_sentence: Stars form in star-formation regions, which itself develop from
molecular clouds.
sentences:
- Although I believe Searle is mistaken, I don't think you have found the problem.
- It may be possible for a solar system like ours to exist outside of a galaxy.
- A blond-haired child performing on the trumpet in front of a house while his younger
brother watches.
- source_sentence: While Queen may refer to both Queen regent (sovereign) or Queen
consort, the King has always been the sovereign.
sentences:
- At first, I thought this is a bit of a tricky question.
- A man plays the guitar.
- There is a very good reason not to refer to the Queen's spouse as "King" - because
they aren't the King.
model-index:
- name: SentenceTransformer based on FacebookAI/xlm-roberta-large
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 768
type: sts-dev-768
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: -0.038123417655342585
name: Pearson Manhattan
- type: spearman_manhattan
value: -0.030855987437062582
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.0742298464837288
name: Pearson Euclidean
- type: spearman_euclidean
value: -0.016119009479880368
name: Spearman Euclidean
- type: pearson_dot
value: -0.053239384921975864
name: Pearson Dot
- type: spearman_dot
value: -0.03860610142560432
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: -0.040766255073950965
name: Pearson Manhattan
- type: spearman_manhattan
value: -0.028106086435826655
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.076050553000047
name: Pearson Euclidean
- type: spearman_euclidean
value: -0.014573222092867504
name: Spearman Euclidean
- type: pearson_dot
value: -0.06110575151055097
name: Pearson Dot
- type: spearman_dot
value: -0.04818501881621991
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: -0.044210895435818166
name: Pearson Manhattan
- type: spearman_manhattan
value: -0.03253407490039325
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.0529355152933442
name: Pearson Euclidean
- type: spearman_euclidean
value: -0.0338167301189937
name: Spearman Euclidean
- type: pearson_dot
value: 0.0887169006335579
name: Pearson Dot
- type: spearman_dot
value: 0.06886250477710897
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: -0.05321620243744594
name: Pearson Manhattan
- type: spearman_manhattan
value: -0.026531903856252148
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.06064347235216407
name: Pearson Euclidean
- type: spearman_euclidean
value: -0.0270947004666721
name: Spearman Euclidean
- type: pearson_dot
value: 0.07199088437564892
name: Pearson Dot
- type: spearman_dot
value: 0.05552894816506978
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: -0.046922199302745354
name: Pearson Manhattan
- type: spearman_manhattan
value: -0.027530540631984835
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.04930495975336398
name: Pearson Euclidean
- type: spearman_euclidean
value: -0.02287953412697089
name: Spearman Euclidean
- type: pearson_dot
value: 0.05851507366090909
name: Pearson Dot
- type: spearman_dot
value: 0.044913605667507114
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: 0.0005203243269627229
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.007914891421418472
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.008479099839233263
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.0002449834909380018
name: Spearman Euclidean
- type: pearson_dot
value: 0.015253799995136243
name: Pearson Dot
- type: spearman_dot
value: -0.002544651953260673
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: -0.000985791968546407
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.009210170664121263
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.010968197464829785
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.0006366521814203481
name: Spearman Euclidean
- type: pearson_dot
value: 0.030903954394043587
name: Pearson Dot
- type: spearman_dot
value: 0.0214169911509498
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: -0.008347426706014351
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.008133437696668973
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.01284332508912676
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.006207692348050752
name: Spearman Euclidean
- type: pearson_dot
value: -0.10411841010392278
name: Pearson Dot
- type: spearman_dot
value: -0.10441611480429308
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: -0.007293947286825709
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.012461130559236479
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.013785631605643068
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.008355374230034162
name: Spearman Euclidean
- type: pearson_dot
value: -0.07790382803601184
name: Pearson Dot
- type: spearman_dot
value: -0.08277939304968172
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: -0.012731573411777072
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.003453137865023755
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.013710254571378023
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.0028389826642085166
name: Spearman Euclidean
- type: pearson_dot
value: -0.04900795414419644
name: Pearson Dot
- type: spearman_dot
value: -0.05520642056907742
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
---
# SentenceTransformer based on FacebookAI/xlm-roberta-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) <!-- at revision c23d21b0620b635a76227c604d44e43a9f0ee389 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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("dipteshkanojia/xlm-roberta-large-sts-matryoshka")
# Run inference
sentences = [
'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.',
'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.',
'A man plays the guitar.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | -0.0381 |
| spearman_manhattan | -0.0309 |
| pearson_euclidean | -0.0742 |
| spearman_euclidean | -0.0161 |
| pearson_dot | -0.0532 |
| spearman_dot | -0.0386 |
| pearson_max | nan |
| spearman_max | nan |
#### Semantic Similarity
* Dataset: `sts-dev-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | -0.0408 |
| spearman_manhattan | -0.0281 |
| pearson_euclidean | -0.0761 |
| spearman_euclidean | -0.0146 |
| pearson_dot | -0.0611 |
| spearman_dot | -0.0482 |
| pearson_max | nan |
| spearman_max | nan |
#### Semantic Similarity
* Dataset: `sts-dev-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | -0.0442 |
| spearman_manhattan | -0.0325 |
| pearson_euclidean | -0.0529 |
| spearman_euclidean | -0.0338 |
| pearson_dot | 0.0887 |
| spearman_dot | 0.0689 |
| pearson_max | nan |
| spearman_max | nan |
#### Semantic Similarity
* Dataset: `sts-dev-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | -0.0532 |
| spearman_manhattan | -0.0265 |
| pearson_euclidean | -0.0606 |
| spearman_euclidean | -0.0271 |
| pearson_dot | 0.072 |
| spearman_dot | 0.0555 |
| pearson_max | nan |
| spearman_max | nan |
#### Semantic Similarity
* Dataset: `sts-dev-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | -0.0469 |
| spearman_manhattan | -0.0275 |
| pearson_euclidean | -0.0493 |
| spearman_euclidean | -0.0229 |
| pearson_dot | 0.0585 |
| spearman_dot | 0.0449 |
| pearson_max | nan |
| spearman_max | nan |
#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | 0.0005 |
| spearman_manhattan | 0.0079 |
| pearson_euclidean | -0.0085 |
| spearman_euclidean | 0.0002 |
| pearson_dot | 0.0153 |
| spearman_dot | -0.0025 |
| pearson_max | nan |
| spearman_max | nan |
#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | -0.001 |
| spearman_manhattan | 0.0092 |
| pearson_euclidean | -0.011 |
| spearman_euclidean | 0.0006 |
| pearson_dot | 0.0309 |
| spearman_dot | 0.0214 |
| pearson_max | nan |
| spearman_max | nan |
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | -0.0083 |
| spearman_manhattan | 0.0081 |
| pearson_euclidean | -0.0128 |
| spearman_euclidean | 0.0062 |
| pearson_dot | -0.1041 |
| spearman_dot | -0.1044 |
| pearson_max | nan |
| spearman_max | nan |
#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | -0.0073 |
| spearman_manhattan | 0.0125 |
| pearson_euclidean | -0.0138 |
| spearman_euclidean | 0.0084 |
| pearson_dot | -0.0779 |
| spearman_dot | -0.0828 |
| pearson_max | nan |
| spearman_max | nan |
#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | -0.0127 |
| spearman_manhattan | 0.0035 |
| pearson_euclidean | -0.0137 |
| spearman_euclidean | 0.0028 |
| pearson_dot | -0.049 |
| spearman_dot | -0.0552 |
| pearson_max | nan |
| spearman_max | nan |
<!--
## 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 Dataset
#### sentence-transformers/stsb
* Dataset: [sentence-transformers/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: 11.08 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 11.05 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</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>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CoSENTLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### sentence-transformers/stsb
* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation 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: 5 tokens</li><li>mean: 16.55 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.5 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------|:------------------------------------------------------|:------------------|
| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CoSENTLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 6
- `per_device_eval_batch_size`: 6
- `num_train_epochs`: 8
- `warmup_ratio`: 0.1
- `fp16`: 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`: 6
- `per_device_eval_batch_size`: 6
- `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`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 8
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 1.0417 | 500 | 21.1353 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - |
| 2.0833 | 1000 | 20.7941 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - |
| 3.125 | 1500 | 20.7823 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - |
| 4.1667 | 2000 | 20.781 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - |
| 5.2083 | 2500 | 20.7707 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - |
| 6.25 | 3000 | 20.7661 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - |
| 7.2917 | 3500 | 20.7719 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - |
| 8.0 | 3840 | - | - | - | - | - | - | - | nan | nan | nan | nan | nan |
### Framework Versions
- Python: 3.9.19
- Sentence Transformers: 3.1.0.dev0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 2.21.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
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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