README.md

---
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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