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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- loss:MatryoshkaLoss
- loss:CoSENTLoss
base_model: distilbert/distilbert-base-uncased
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: The gate is yellow.
sentences:
- The gate is blue.
- The person is starting a fire.
- A woman is bungee jumping.
- source_sentence: A plane in the sky.
sentences:
- Two airplanes in the sky.
- A man is standing in the rain.
- There are two men near a wall.
- source_sentence: A woman is reading.
sentences:
- A woman is writing something.
- A woman is applying eye shadow.
- A dog and a red ball in the air.
- source_sentence: A baby is laughing.
sentences:
- The baby laughed in his car seat.
- Suicide bomber strikes in Syria
- Bangladesh Islamist execution upheld
- source_sentence: A woman is dancing.
sentences:
- A woman is dancing in railway station.
- The flag was moving in the air.
- three dogs growling On one another
pipeline_tag: sentence-similarity
co2_eq_emissions:
emissions: 7.871164130493101
energy_consumed: 0.020249867843471606
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.112
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SentenceTransformer based on distilbert/distilbert-base-uncased
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 768
type: sts-dev-768
metrics:
- type: pearson_cosine
value: 0.8647737221000229
name: Pearson Cosine
- type: spearman_cosine
value: 0.8747521728687471
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8627734228763478
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8657556253211545
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.862712112144467
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8657615257280495
name: Spearman Euclidean
- type: pearson_dot
value: 0.7442745641899206
name: Pearson Dot
- type: spearman_dot
value: 0.7513830366520415
name: Spearman Dot
- type: pearson_max
value: 0.8647737221000229
name: Pearson Max
- type: spearman_max
value: 0.8747521728687471
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: 0.8628378541768764
name: Pearson Cosine
- type: spearman_cosine
value: 0.8741345340758229
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8619744745534216
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8651450292937584
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8622841683977804
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8653280682431165
name: Spearman Euclidean
- type: pearson_dot
value: 0.746359236761633
name: Pearson Dot
- type: spearman_dot
value: 0.7540849763868891
name: Spearman Dot
- type: pearson_max
value: 0.8628378541768764
name: Pearson Max
- type: spearman_max
value: 0.8741345340758229
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.8588975886507025
name: Pearson Cosine
- type: spearman_cosine
value: 0.8714341050301952
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8590790006287132
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8634123185807864
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8591861535833625
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8628587088112977
name: Spearman Euclidean
- type: pearson_dot
value: 0.7185871795192371
name: Pearson Dot
- type: spearman_dot
value: 0.7288595287151053
name: Spearman Dot
- type: pearson_max
value: 0.8591861535833625
name: Pearson Max
- type: spearman_max
value: 0.8714341050301952
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: 0.8528583626543365
name: Pearson Cosine
- type: spearman_cosine
value: 0.8687502864484896
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8509433708242649
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.857615159782176
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8531616082767298
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8580823134153918
name: Spearman Euclidean
- type: pearson_dot
value: 0.697019210549756
name: Pearson Dot
- type: spearman_dot
value: 0.705924438927243
name: Spearman Dot
- type: pearson_max
value: 0.8531616082767298
name: Pearson Max
- type: spearman_max
value: 0.8687502864484896
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: 0.8340115410608493
name: Pearson Cosine
- type: spearman_cosine
value: 0.858682843519445
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8351566362279711
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8445869885309296
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.838674217877368
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8460894143343873
name: Spearman Euclidean
- type: pearson_dot
value: 0.6579249229659768
name: Pearson Dot
- type: spearman_dot
value: 0.6712615573330701
name: Spearman Dot
- type: pearson_max
value: 0.838674217877368
name: Pearson Max
- type: spearman_max
value: 0.858682843519445
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.833720870548252
name: Pearson Cosine
- type: spearman_cosine
value: 0.8469501140979906
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8484755252691695
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8470024066861298
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8492651445573072
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8475238481800537
name: Spearman Euclidean
- type: pearson_dot
value: 0.6701649984837568
name: Pearson Dot
- type: spearman_dot
value: 0.6526285131648061
name: Spearman Dot
- type: pearson_max
value: 0.8492651445573072
name: Pearson Max
- type: spearman_max
value: 0.8475238481800537
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.8325595554355977
name: Pearson Cosine
- type: spearman_cosine
value: 0.8467500241650668
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8474378528408064
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8462571021525837
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.848182316243596
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8466275072216626
name: Spearman Euclidean
- type: pearson_dot
value: 0.6736686039338646
name: Pearson Dot
- type: spearman_dot
value: 0.6572299516736647
name: Spearman Dot
- type: pearson_max
value: 0.848182316243596
name: Pearson Max
- type: spearman_max
value: 0.8467500241650668
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.8225923032714455
name: Pearson Cosine
- type: spearman_cosine
value: 0.8403145699624681
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8420998942805191
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8419520394692916
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8434867831513
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8428522494561291
name: Spearman Euclidean
- type: pearson_dot
value: 0.6230179114374444
name: Pearson Dot
- type: spearman_dot
value: 0.6061595939729718
name: Spearman Dot
- type: pearson_max
value: 0.8434867831513
name: Pearson Max
- type: spearman_max
value: 0.8428522494561291
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.8149976807930366
name: Pearson Cosine
- type: spearman_cosine
value: 0.8349547446101432
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8351661617446753
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8360899024374612
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8375785243041524
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8375574347771609
name: Spearman Euclidean
- type: pearson_dot
value: 0.5958381414366161
name: Pearson Dot
- type: spearman_dot
value: 0.5793444545861678
name: Spearman Dot
- type: pearson_max
value: 0.8375785243041524
name: Pearson Max
- type: spearman_max
value: 0.8375574347771609
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.7981336004264228
name: Pearson Cosine
- type: spearman_cosine
value: 0.8269913105115189
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8238799955007295
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8289121477853545
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8278657744625194
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8314643517951371
name: Spearman Euclidean
- type: pearson_dot
value: 0.5206433480609991
name: Pearson Dot
- type: spearman_dot
value: 0.5067194535547845
name: Spearman Dot
- type: pearson_max
value: 0.8278657744625194
name: Pearson Max
- type: spearman_max
value: 0.8314643517951371
name: Spearman Max
---
# SentenceTransformer based on distilbert/distilbert-base-uncased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 6cdc0aad91f5ae2e6712e91bc7b65d1cf5c05411 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/distilbert-base-uncased-sts-matryoshka")
# Run inference
sentences = [
'A woman is dancing.',
'A woman is dancing in railway station.',
'The flag was moving in the air.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(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/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8648 |
| **spearman_cosine** | **0.8748** |
| pearson_manhattan | 0.8628 |
| spearman_manhattan | 0.8658 |
| pearson_euclidean | 0.8627 |
| spearman_euclidean | 0.8658 |
| pearson_dot | 0.7443 |
| spearman_dot | 0.7514 |
| pearson_max | 0.8648 |
| spearman_max | 0.8748 |
#### Semantic Similarity
* Dataset: `sts-dev-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8628 |
| **spearman_cosine** | **0.8741** |
| pearson_manhattan | 0.862 |
| spearman_manhattan | 0.8651 |
| pearson_euclidean | 0.8623 |
| spearman_euclidean | 0.8653 |
| pearson_dot | 0.7464 |
| spearman_dot | 0.7541 |
| pearson_max | 0.8628 |
| spearman_max | 0.8741 |
#### Semantic Similarity
* Dataset: `sts-dev-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8589 |
| **spearman_cosine** | **0.8714** |
| pearson_manhattan | 0.8591 |
| spearman_manhattan | 0.8634 |
| pearson_euclidean | 0.8592 |
| spearman_euclidean | 0.8629 |
| pearson_dot | 0.7186 |
| spearman_dot | 0.7289 |
| pearson_max | 0.8592 |
| spearman_max | 0.8714 |
#### Semantic Similarity
* Dataset: `sts-dev-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8529 |
| **spearman_cosine** | **0.8688** |
| pearson_manhattan | 0.8509 |
| spearman_manhattan | 0.8576 |
| pearson_euclidean | 0.8532 |
| spearman_euclidean | 0.8581 |
| pearson_dot | 0.697 |
| spearman_dot | 0.7059 |
| pearson_max | 0.8532 |
| spearman_max | 0.8688 |
#### Semantic Similarity
* Dataset: `sts-dev-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.834 |
| **spearman_cosine** | **0.8587** |
| pearson_manhattan | 0.8352 |
| spearman_manhattan | 0.8446 |
| pearson_euclidean | 0.8387 |
| spearman_euclidean | 0.8461 |
| pearson_dot | 0.6579 |
| spearman_dot | 0.6713 |
| pearson_max | 0.8387 |
| spearman_max | 0.8587 |
#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.8337 |
| **spearman_cosine** | **0.847** |
| pearson_manhattan | 0.8485 |
| spearman_manhattan | 0.847 |
| pearson_euclidean | 0.8493 |
| spearman_euclidean | 0.8475 |
| pearson_dot | 0.6702 |
| spearman_dot | 0.6526 |
| pearson_max | 0.8493 |
| spearman_max | 0.8475 |
#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8326 |
| **spearman_cosine** | **0.8468** |
| pearson_manhattan | 0.8474 |
| spearman_manhattan | 0.8463 |
| pearson_euclidean | 0.8482 |
| spearman_euclidean | 0.8466 |
| pearson_dot | 0.6737 |
| spearman_dot | 0.6572 |
| pearson_max | 0.8482 |
| spearman_max | 0.8468 |
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8226 |
| **spearman_cosine** | **0.8403** |
| pearson_manhattan | 0.8421 |
| spearman_manhattan | 0.842 |
| pearson_euclidean | 0.8435 |
| spearman_euclidean | 0.8429 |
| pearson_dot | 0.623 |
| spearman_dot | 0.6062 |
| pearson_max | 0.8435 |
| spearman_max | 0.8429 |
#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.815 |
| **spearman_cosine** | **0.835** |
| pearson_manhattan | 0.8352 |
| spearman_manhattan | 0.8361 |
| pearson_euclidean | 0.8376 |
| spearman_euclidean | 0.8376 |
| pearson_dot | 0.5958 |
| spearman_dot | 0.5793 |
| pearson_max | 0.8376 |
| spearman_max | 0.8376 |
#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.7981 |
| **spearman_cosine** | **0.827** |
| pearson_manhattan | 0.8239 |
| spearman_manhattan | 0.8289 |
| pearson_euclidean | 0.8279 |
| spearman_euclidean | 0.8315 |
| pearson_dot | 0.5206 |
| spearman_dot | 0.5067 |
| pearson_max | 0.8279 |
| spearman_max | 0.8315 |
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## 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: 10.0 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 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/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: 15.1 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 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/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`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `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`: False
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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`: 4
- `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
- `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`: None
- `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_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 |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.2778 | 100 | 23.266 | 21.5517 | 0.8305 | 0.8355 | 0.8361 | 0.8157 | 0.8366 | - | - | - | - | - |
| 0.5556 | 200 | 21.8736 | 21.6172 | 0.8327 | 0.8388 | 0.8446 | 0.8206 | 0.8453 | - | - | - | - | - |
| 0.8333 | 300 | 21.6241 | 22.0565 | 0.8475 | 0.8538 | 0.8556 | 0.8345 | 0.8565 | - | - | - | - | - |
| 1.1111 | 400 | 21.075 | 23.6719 | 0.8545 | 0.8581 | 0.8634 | 0.8435 | 0.8644 | - | - | - | - | - |
| 1.3889 | 500 | 20.4122 | 22.5926 | 0.8592 | 0.8624 | 0.8650 | 0.8436 | 0.8656 | - | - | - | - | - |
| 1.6667 | 600 | 20.6586 | 22.5999 | 0.8514 | 0.8563 | 0.8595 | 0.8389 | 0.8597 | - | - | - | - | - |
| 1.9444 | 700 | 20.3262 | 22.2965 | 0.8582 | 0.8631 | 0.8666 | 0.8465 | 0.8667 | - | - | - | - | - |
| 2.2222 | 800 | 19.7948 | 23.1844 | 0.8621 | 0.8659 | 0.8688 | 0.8499 | 0.8694 | - | - | - | - | - |
| 2.5 | 900 | 19.2826 | 23.1351 | 0.8653 | 0.8687 | 0.8703 | 0.8547 | 0.8710 | - | - | - | - | - |
| 2.7778 | 1000 | 19.1063 | 23.7141 | 0.8641 | 0.8672 | 0.8691 | 0.8531 | 0.8695 | - | - | - | - | - |
| 3.0556 | 1100 | 19.4575 | 23.0055 | 0.8673 | 0.8702 | 0.8726 | 0.8574 | 0.8728 | - | - | - | - | - |
| 3.3333 | 1200 | 18.0727 | 24.9288 | 0.8659 | 0.8692 | 0.8715 | 0.8565 | 0.8722 | - | - | - | - | - |
| 3.6111 | 1300 | 18.1698 | 25.3114 | 0.8675 | 0.8701 | 0.8728 | 0.8576 | 0.8734 | - | - | - | - | - |
| 3.8889 | 1400 | 18.2321 | 25.3777 | 0.8688 | 0.8714 | 0.8741 | 0.8587 | 0.8748 | - | - | - | - | - |
| 4.0 | 1440 | - | - | - | - | - | - | - | 0.8350 | 0.8403 | 0.8468 | 0.8270 | 0.8470 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.020 kWh
- **Carbon Emitted**: 0.008 kg of CO2
- **Hours Used**: 0.112 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.0.0.dev0
- Transformers: 4.41.0.dev0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- 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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