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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- 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: A woman is dancing.
sentences:
- Women are dancing.
- A toddler walks down a hallway.
- Shinzo Abe is Japan's prime minister
- source_sentence: A man is spitting.
sentences:
- A man is crying.
- The girl is playing the guitar.
- A slow loris hanging on a cord.
- source_sentence: A man is speaking.
sentences:
- A man is talking.
- A man plays an acoustic guitar.
- The dogs are chasing a cat.
- source_sentence: A plane in the sky.
sentences:
- Two airplanes in the sky.
- A slow loris hanging on a cord.
- Turkey's PM Warns Against Protests
- source_sentence: A baby is laughing.
sentences:
- The baby laughed in his car seat.
- A brown horse in a green field.
- Bangladesh Islamist leader executed
pipeline_tag: sentence-similarity
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: 0.8597256789475689
name: Pearson Cosine
- type: spearman_cosine
value: 0.8704890959686488
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8577087236028236
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8613364457717408
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8573646665610765
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8611053939518858
name: Spearman Euclidean
- type: pearson_dot
value: 0.7230928823966007
name: Pearson Dot
- type: spearman_dot
value: 0.7292814320710974
name: Spearman Dot
- type: pearson_max
value: 0.8597256789475689
name: Pearson Max
- type: spearman_max
value: 0.8704890959686488
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: 0.8565849984058084
name: Pearson Cosine
- type: spearman_cosine
value: 0.8690380994355429
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8560989283234569
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8602048185493963
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8560319360006069
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8598344132114529
name: Spearman Euclidean
- type: pearson_dot
value: 0.7250593470322173
name: Pearson Dot
- type: spearman_dot
value: 0.7324935808414036
name: Spearman Dot
- type: pearson_max
value: 0.8565849984058084
name: Pearson Max
- type: spearman_max
value: 0.8690380994355429
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.8508677416837496
name: Pearson Cosine
- type: spearman_cosine
value: 0.8655671620679589
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8516296649395021
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8576372447474295
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8512958746883122
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8567348597207523
name: Spearman Euclidean
- type: pearson_dot
value: 0.691266333570308
name: Pearson Dot
- type: spearman_dot
value: 0.6983564197469347
name: Spearman Dot
- type: pearson_max
value: 0.8516296649395021
name: Pearson Max
- type: spearman_max
value: 0.8655671620679589
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: 0.8416379040782492
name: Pearson Cosine
- type: spearman_cosine
value: 0.8625866345174488
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8410105415496507
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8496221523132089
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8431760561066126
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8505697779445824
name: Spearman Euclidean
- type: pearson_dot
value: 0.677560950193549
name: Pearson Dot
- type: spearman_dot
value: 0.6864851260895027
name: Spearman Dot
- type: pearson_max
value: 0.8431760561066126
name: Pearson Max
- type: spearman_max
value: 0.8625866345174488
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: 0.823170809036498
name: Pearson Cosine
- type: spearman_cosine
value: 0.8523184158399918
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8255414664543136
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8358413125165197
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8292011526410756
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8385242101250404
name: Spearman Euclidean
- type: pearson_dot
value: 0.641639319620455
name: Pearson Dot
- type: spearman_dot
value: 0.6564088055361835
name: Spearman Dot
- type: pearson_max
value: 0.8292011526410756
name: Pearson Max
- type: spearman_max
value: 0.8523184158399918
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 32
type: sts-dev-32
metrics:
- type: pearson_cosine
value: 0.7903418859430655
name: Pearson Cosine
- type: spearman_cosine
value: 0.8327625705936669
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8031537655331857
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8168069966906343
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8078549989079483
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8195679102426064
name: Spearman Euclidean
- type: pearson_dot
value: 0.5951512690504269
name: Pearson Dot
- type: spearman_dot
value: 0.5992430550243973
name: Spearman Dot
- type: pearson_max
value: 0.8078549989079483
name: Pearson Max
- type: spearman_max
value: 0.8327625705936669
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.8259116102299048
name: Pearson Cosine
- type: spearman_cosine
value: 0.8420103291660583
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8417036739734224
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.839403978426242
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8416944892693242
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8392814362849023
name: Spearman Euclidean
- type: pearson_dot
value: 0.6531059298507882
name: Pearson Dot
- type: spearman_dot
value: 0.6395643411764597
name: Spearman Dot
- type: pearson_max
value: 0.8417036739734224
name: Pearson Max
- type: spearman_max
value: 0.8420103291660583
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.8243325623482549
name: Pearson Cosine
- type: spearman_cosine
value: 0.8417788357334501
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8405895269265039
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8387513037939833
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8405749756794761
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8386191956000736
name: Spearman Euclidean
- type: pearson_dot
value: 0.6577547074460394
name: Pearson Dot
- type: spearman_dot
value: 0.6453398362527448
name: Spearman Dot
- type: pearson_max
value: 0.8405895269265039
name: Pearson Max
- type: spearman_max
value: 0.8417788357334501
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.8128490933340125
name: Pearson Cosine
- type: spearman_cosine
value: 0.8343525276981816
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8349925426973063
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8339373046648948
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8349685334828352
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8342389147888624
name: Spearman Euclidean
- type: pearson_dot
value: 0.6010530472572276
name: Pearson Dot
- type: spearman_dot
value: 0.5827176472260001
name: Spearman Dot
- type: pearson_max
value: 0.8349925426973063
name: Pearson Max
- type: spearman_max
value: 0.8343525276981816
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.8037074044935162
name: Pearson Cosine
- type: spearman_cosine
value: 0.8297484250803338
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8282523311738189
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8292579770469635
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.828555014804415
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8294547431431344
name: Spearman Euclidean
- type: pearson_dot
value: 0.579341375708575
name: Pearson Dot
- type: spearman_dot
value: 0.5659659830073487
name: Spearman Dot
- type: pearson_max
value: 0.828555014804415
name: Pearson Max
- type: spearman_max
value: 0.8297484250803338
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.7861572380387101
name: Pearson Cosine
- type: spearman_cosine
value: 0.8221344542757412
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8179044736790866
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8218843830925717
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8199399298670013
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8240682904452457
name: Spearman Euclidean
- type: pearson_dot
value: 0.5115276911122266
name: Pearson Dot
- type: spearman_dot
value: 0.5024074247877125
name: Spearman Dot
- type: pearson_max
value: 0.8199399298670013
name: Pearson Max
- type: spearman_max
value: 0.8240682904452457
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 32
type: sts-test-32
metrics:
- type: pearson_cosine
value: 0.7616404560065974
name: Pearson Cosine
- type: spearman_cosine
value: 0.8126281001961144
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7995560120404742
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8084393007868024
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8024415842761214
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8115677983458126
name: Spearman Euclidean
- type: pearson_dot
value: 0.4646775610104062
name: Pearson Dot
- type: spearman_dot
value: 0.451018702626726
name: Spearman Dot
- type: pearson_max
value: 0.8024415842761214
name: Pearson Max
- type: spearman_max
value: 0.8126281001961144
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 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **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("mrm8488/distilbert-base-matryoshka-sts")
# Run inference
sentences = [
'A baby is laughing.',
'The baby laughed in his car seat.',
'A brown horse in a green field.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 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 | 0.8597 |
| **spearman_cosine** | **0.8705** |
| pearson_manhattan | 0.8577 |
| spearman_manhattan | 0.8613 |
| pearson_euclidean | 0.8574 |
| spearman_euclidean | 0.8611 |
| pearson_dot | 0.7231 |
| spearman_dot | 0.7293 |
| pearson_max | 0.8597 |
| spearman_max | 0.8705 |
#### 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 | 0.8566 |
| **spearman_cosine** | **0.869** |
| pearson_manhattan | 0.8561 |
| spearman_manhattan | 0.8602 |
| pearson_euclidean | 0.856 |
| spearman_euclidean | 0.8598 |
| pearson_dot | 0.7251 |
| spearman_dot | 0.7325 |
| pearson_max | 0.8566 |
| spearman_max | 0.869 |
#### 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 | 0.8509 |
| **spearman_cosine** | **0.8656** |
| pearson_manhattan | 0.8516 |
| spearman_manhattan | 0.8576 |
| pearson_euclidean | 0.8513 |
| spearman_euclidean | 0.8567 |
| pearson_dot | 0.6913 |
| spearman_dot | 0.6984 |
| pearson_max | 0.8516 |
| spearman_max | 0.8656 |
#### 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 | 0.8416 |
| **spearman_cosine** | **0.8626** |
| pearson_manhattan | 0.841 |
| spearman_manhattan | 0.8496 |
| pearson_euclidean | 0.8432 |
| spearman_euclidean | 0.8506 |
| pearson_dot | 0.6776 |
| spearman_dot | 0.6865 |
| pearson_max | 0.8432 |
| spearman_max | 0.8626 |
#### 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 | 0.8232 |
| **spearman_cosine** | **0.8523** |
| pearson_manhattan | 0.8255 |
| spearman_manhattan | 0.8358 |
| pearson_euclidean | 0.8292 |
| spearman_euclidean | 0.8385 |
| pearson_dot | 0.6416 |
| spearman_dot | 0.6564 |
| pearson_max | 0.8292 |
| spearman_max | 0.8523 |
#### Semantic Similarity
* Dataset: `sts-dev-32`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7903 |
| **spearman_cosine** | **0.8328** |
| pearson_manhattan | 0.8032 |
| spearman_manhattan | 0.8168 |
| pearson_euclidean | 0.8079 |
| spearman_euclidean | 0.8196 |
| pearson_dot | 0.5952 |
| spearman_dot | 0.5992 |
| pearson_max | 0.8079 |
| spearman_max | 0.8328 |
#### 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 | 0.8259 |
| **spearman_cosine** | **0.842** |
| pearson_manhattan | 0.8417 |
| spearman_manhattan | 0.8394 |
| pearson_euclidean | 0.8417 |
| spearman_euclidean | 0.8393 |
| pearson_dot | 0.6531 |
| spearman_dot | 0.6396 |
| pearson_max | 0.8417 |
| spearman_max | 0.842 |
#### 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 | 0.8243 |
| **spearman_cosine** | **0.8418** |
| pearson_manhattan | 0.8406 |
| spearman_manhattan | 0.8388 |
| pearson_euclidean | 0.8406 |
| spearman_euclidean | 0.8386 |
| pearson_dot | 0.6578 |
| spearman_dot | 0.6453 |
| pearson_max | 0.8406 |
| spearman_max | 0.8418 |
#### 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 | 0.8128 |
| **spearman_cosine** | **0.8344** |
| pearson_manhattan | 0.835 |
| spearman_manhattan | 0.8339 |
| pearson_euclidean | 0.835 |
| spearman_euclidean | 0.8342 |
| pearson_dot | 0.6011 |
| spearman_dot | 0.5827 |
| pearson_max | 0.835 |
| spearman_max | 0.8344 |
#### 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 | 0.8037 |
| **spearman_cosine** | **0.8297** |
| pearson_manhattan | 0.8283 |
| spearman_manhattan | 0.8293 |
| pearson_euclidean | 0.8286 |
| spearman_euclidean | 0.8295 |
| pearson_dot | 0.5793 |
| spearman_dot | 0.566 |
| pearson_max | 0.8286 |
| spearman_max | 0.8297 |
#### 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 | 0.7862 |
| **spearman_cosine** | **0.8221** |
| pearson_manhattan | 0.8179 |
| spearman_manhattan | 0.8219 |
| pearson_euclidean | 0.8199 |
| spearman_euclidean | 0.8241 |
| pearson_dot | 0.5115 |
| spearman_dot | 0.5024 |
| pearson_max | 0.8199 |
| spearman_max | 0.8241 |
#### Semantic Similarity
* Dataset: `sts-test-32`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7616 |
| **spearman_cosine** | **0.8126** |
| pearson_manhattan | 0.7996 |
| spearman_manhattan | 0.8084 |
| pearson_euclidean | 0.8024 |
| spearman_euclidean | 0.8116 |
| pearson_dot | 0.4647 |
| spearman_dot | 0.451 |
| pearson_max | 0.8024 |
| spearman_max | 0.8126 |
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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/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CoSENTLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64,
32
],
"matryoshka_weights": [
1,
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/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CoSENTLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64,
32
],
"matryoshka_weights": [
1,
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`: True
- `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
- `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
- `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-32_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-32_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.2778 | 100 | 28.2763 | 26.3514 | 0.8250 | 0.8306 | 0.7893 | 0.8308 | 0.8094 | 0.8314 | - | - | - | - | - | - |
| 0.5556 | 200 | 26.3731 | 26.0000 | 0.8373 | 0.8412 | 0.8026 | 0.8463 | 0.8267 | 0.8467 | - | - | - | - | - | - |
| 0.8333 | 300 | 26.0243 | 26.5062 | 0.8434 | 0.8495 | 0.8073 | 0.8534 | 0.8297 | 0.8556 | - | - | - | - | - | - |
| 1.1111 | 400 | 25.3448 | 28.1742 | 0.8496 | 0.8544 | 0.8157 | 0.8593 | 0.8361 | 0.8611 | - | - | - | - | - | - |
| 1.3889 | 500 | 24.7922 | 27.0245 | 0.8488 | 0.8529 | 0.8149 | 0.8574 | 0.8352 | 0.8589 | - | - | - | - | - | - |
| 1.6667 | 600 | 24.7596 | 26.9771 | 0.8516 | 0.8558 | 0.8199 | 0.8601 | 0.8389 | 0.8619 | - | - | - | - | - | - |
| 1.9444 | 700 | 24.7165 | 26.2923 | 0.8602 | 0.8634 | 0.8277 | 0.8665 | 0.8476 | 0.8681 | - | - | - | - | - | - |
| 2.2222 | 800 | 23.7934 | 27.9207 | 0.8570 | 0.8608 | 0.8263 | 0.8640 | 0.8460 | 0.8656 | - | - | - | - | - | - |
| 2.5 | 900 | 23.4618 | 27.5855 | 0.8583 | 0.8618 | 0.8257 | 0.8657 | 0.8456 | 0.8675 | - | - | - | - | - | - |
| 2.7778 | 1000 | 23.1831 | 29.9791 | 0.8533 | 0.8557 | 0.8232 | 0.8599 | 0.8411 | 0.8612 | - | - | - | - | - | - |
| 3.0556 | 1100 | 23.1935 | 28.7866 | 0.8612 | 0.8636 | 0.8329 | 0.8677 | 0.8504 | 0.8689 | - | - | - | - | - | - |
| 3.3333 | 1200 | 22.1447 | 30.0641 | 0.8597 | 0.8630 | 0.8285 | 0.8661 | 0.8488 | 0.8676 | - | - | - | - | - | - |
| 3.6111 | 1300 | 21.9271 | 30.9347 | 0.8613 | 0.8648 | 0.8309 | 0.8679 | 0.8509 | 0.8697 | - | - | - | - | - | - |
| 3.8889 | 1400 | 21.973 | 30.9209 | 0.8626 | 0.8656 | 0.8328 | 0.8690 | 0.8523 | 0.8705 | - | - | - | - | - | - |
| 4.0 | 1440 | - | - | - | - | - | - | - | - | 0.8297 | 0.8344 | 0.8126 | 0.8418 | 0.8221 | 0.8420 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.1
- 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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