---
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 plane in the sky.
  sentences:
  - Two airplanes in the sky.
  - Two women are sitting in a cafe.
  - Turkey's PM Warns Against Protests
- source_sentence: A man jumping rope
  sentences:
  - A man climbs a rope.
  - Blast on Indian train kills one
  - Israel expands subsidies to settlements
- source_sentence: A baby is laughing.
  sentences:
  - The baby laughed in his car seat.
  - The girl is playing the guitar.
  - Bangladesh Islamist leader executed
- source_sentence: A plane is landing.
  sentences:
  - A animated airplane is landing.
  - A man plays an acoustic guitar.
  - Obama urges no new sanctions on Iran
- source_sentence: A boy is vacuuming.
  sentences:
  - A little boy is vacuuming the floor.
  - Suicide bomber strikes in Syria
  - 32 die in Bangladesh protest
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: 0.8580007118837358
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.871820299536176
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8579597824452743
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8611676230134329
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8584693242993966
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8617539394714434
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6259192943899555
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6245849846631494
      name: Spearman Dot
    - type: pearson_max
      value: 0.8584693242993966
      name: Pearson Max
    - type: spearman_max
      value: 0.871820299536176
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 512
      type: sts-dev-512
    metrics:
    - type: pearson_cosine
      value: 0.855328467168775
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8708546925464771
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8571701704416792
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8609603329646862
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8577665956034857
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8611867637483455
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6301839390729895
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6312551259723912
      name: Spearman Dot
    - type: pearson_max
      value: 0.8577665956034857
      name: Pearson Max
    - type: spearman_max
      value: 0.8708546925464771
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 256
      type: sts-dev-256
    metrics:
    - type: pearson_cosine
      value: 0.8534192140857989
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8684742287834586
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8550376893582918
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8595873940460774
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.855243500036296
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8595389790366662
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5692600956239565
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5631798664802073
      name: Spearman Dot
    - type: pearson_max
      value: 0.855243500036296
      name: Pearson Max
    - type: spearman_max
      value: 0.8684742287834586
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 128
      type: sts-dev-128
    metrics:
    - type: pearson_cosine
      value: 0.8437376978373121
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8634082420330794
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8454596574177755
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.85188111210432
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8479887421152008
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8537259447832961
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5513203019384504
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5500687993669725
      name: Spearman Dot
    - type: pearson_max
      value: 0.8479887421152008
      name: Pearson Max
    - type: spearman_max
      value: 0.8634082420330794
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 64
      type: sts-dev-64
    metrics:
    - type: pearson_cosine
      value: 0.8272184719216283
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8541030591238341
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8307462071466211
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8406982840852595
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8342382781891662
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8427338906559259
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.494520518114596
      name: Pearson Dot
    - type: spearman_dot
      value: 0.49218360841938574
      name: Spearman Dot
    - type: pearson_max
      value: 0.8342382781891662
      name: Pearson Max
    - type: spearman_max
      value: 0.8541030591238341
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 32
      type: sts-dev-32
    metrics:
    - type: pearson_cosine
      value: 0.795037446434113
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8337679875014413
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8120635303724889
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8249212312847407
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8157607542813738
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8262833782950811
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.44442829473227297
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4333209339301445
      name: Spearman Dot
    - type: pearson_max
      value: 0.8157607542813738
      name: Pearson Max
    - type: spearman_max
      value: 0.8337679875014413
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 16
      type: sts-dev-16
    metrics:
    - type: pearson_cosine
      value: 0.7402920507586056
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7953398971914366
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7661819958789702
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7806209887724272
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7753319460863385
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.788448392758016
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.2914268467178465
      name: Pearson Dot
    - type: spearman_dot
      value: 0.2731801701260987
      name: Spearman Dot
    - type: pearson_max
      value: 0.7753319460863385
      name: Pearson Max
    - type: spearman_max
      value: 0.7953398971914366
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.8355126555886146
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8474343771835785
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8477769261693708
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8440487632905719
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8482353907773731
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8443357402859023
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.575155372226532
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5645826036063977
      name: Spearman Dot
    - type: pearson_max
      value: 0.8482353907773731
      name: Pearson Max
    - type: spearman_max
      value: 0.8474343771835785
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.8345636179092932
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.847969741682177
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8471375569231226
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8432315278152519
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8475673449165414
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8438566473590643
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5890647647307824
      name: Pearson Dot
    - type: spearman_dot
      value: 0.579599198660516
      name: Spearman Dot
    - type: pearson_max
      value: 0.8475673449165414
      name: Pearson Max
    - type: spearman_max
      value: 0.847969741682177
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.8264268046184008
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8414784020776254
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8414377075419083
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8388634084489552
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8423455168447094
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8400797815114284
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5229860109488433
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5099269577284724
      name: Spearman Dot
    - type: pearson_max
      value: 0.8423455168447094
      name: Pearson Max
    - type: spearman_max
      value: 0.8414784020776254
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.8189773000477083
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.837625236881656
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8349887918183595
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8336489133404312
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8365085956274743
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8347627903646608
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.49799738412782535
      name: Pearson Dot
    - type: spearman_dot
      value: 0.48970409354637134
      name: Spearman Dot
    - type: pearson_max
      value: 0.8365085956274743
      name: Pearson Max
    - type: spearman_max
      value: 0.837625236881656
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.8062259318483077
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8292433269349447
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8236527010227455
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8243846152203906
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8273451113428331
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8269777736926925
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4318247709105578
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4325030690630689
      name: Spearman Dot
    - type: pearson_max
      value: 0.8273451113428331
      name: Pearson Max
    - type: spearman_max
      value: 0.8292433269349447
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 32
      type: sts-test-32
    metrics:
    - type: pearson_cosine
      value: 0.7769698706658718
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.813231133965274
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8040659399939705
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8083901845044422
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8089540323890078
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8126434700070444
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.3721968691924307
      name: Pearson Dot
    - type: spearman_dot
      value: 0.36359211044547146
      name: Spearman Dot
    - type: pearson_max
      value: 0.8089540323890078
      name: Pearson Max
    - type: spearman_max
      value: 0.813231133965274
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 16
      type: sts-test-16
    metrics:
    - type: pearson_cosine
      value: 0.7350580362911046
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7811480253828886
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7686995805327835
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7767016091591996
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7732639293607727
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7798783495241994
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.25479413300114095
      name: Pearson Dot
    - type: spearman_dot
      value: 0.24117846955339683
      name: Spearman Dot
    - type: pearson_max
      value: 0.7732639293607727
      name: Pearson Max
    - type: spearman_max
      value: 0.7811480253828886
      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-v2")
# Run inference
sentences = [
    'A boy is vacuuming.',
    'A little boy is vacuuming the floor.',
    'Suicide bomber strikes in Syria',
]
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      | 0.858      |
| **spearman_cosine** | **0.8718** |
| pearson_manhattan   | 0.858      |
| spearman_manhattan  | 0.8612     |
| pearson_euclidean   | 0.8585     |
| spearman_euclidean  | 0.8618     |
| pearson_dot         | 0.6259     |
| spearman_dot        | 0.6246     |
| pearson_max         | 0.8585     |
| spearman_max        | 0.8718     |

#### 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      | 0.8553     |
| **spearman_cosine** | **0.8709** |
| pearson_manhattan   | 0.8572     |
| spearman_manhattan  | 0.861      |
| pearson_euclidean   | 0.8578     |
| spearman_euclidean  | 0.8612     |
| pearson_dot         | 0.6302     |
| spearman_dot        | 0.6313     |
| pearson_max         | 0.8578     |
| spearman_max        | 0.8709     |

#### 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      | 0.8534     |
| **spearman_cosine** | **0.8685** |
| pearson_manhattan   | 0.855      |
| spearman_manhattan  | 0.8596     |
| pearson_euclidean   | 0.8552     |
| spearman_euclidean  | 0.8595     |
| pearson_dot         | 0.5693     |
| spearman_dot        | 0.5632     |
| pearson_max         | 0.8552     |
| spearman_max        | 0.8685     |

#### 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      | 0.8437     |
| **spearman_cosine** | **0.8634** |
| pearson_manhattan   | 0.8455     |
| spearman_manhattan  | 0.8519     |
| pearson_euclidean   | 0.848      |
| spearman_euclidean  | 0.8537     |
| pearson_dot         | 0.5513     |
| spearman_dot        | 0.5501     |
| pearson_max         | 0.848      |
| spearman_max        | 0.8634     |

#### 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      | 0.8272     |
| **spearman_cosine** | **0.8541** |
| pearson_manhattan   | 0.8307     |
| spearman_manhattan  | 0.8407     |
| pearson_euclidean   | 0.8342     |
| spearman_euclidean  | 0.8427     |
| pearson_dot         | 0.4945     |
| spearman_dot        | 0.4922     |
| pearson_max         | 0.8342     |
| spearman_max        | 0.8541     |

#### 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      | 0.795      |
| **spearman_cosine** | **0.8338** |
| pearson_manhattan   | 0.8121     |
| spearman_manhattan  | 0.8249     |
| pearson_euclidean   | 0.8158     |
| spearman_euclidean  | 0.8263     |
| pearson_dot         | 0.4444     |
| spearman_dot        | 0.4333     |
| pearson_max         | 0.8158     |
| spearman_max        | 0.8338     |

#### Semantic Similarity
* Dataset: `sts-dev-16`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7403     |
| **spearman_cosine** | **0.7953** |
| pearson_manhattan   | 0.7662     |
| spearman_manhattan  | 0.7806     |
| pearson_euclidean   | 0.7753     |
| spearman_euclidean  | 0.7884     |
| pearson_dot         | 0.2914     |
| spearman_dot        | 0.2732     |
| pearson_max         | 0.7753     |
| spearman_max        | 0.7953     |

#### 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      | 0.8355     |
| **spearman_cosine** | **0.8474** |
| pearson_manhattan   | 0.8478     |
| spearman_manhattan  | 0.844      |
| pearson_euclidean   | 0.8482     |
| spearman_euclidean  | 0.8443     |
| pearson_dot         | 0.5752     |
| spearman_dot        | 0.5646     |
| pearson_max         | 0.8482     |
| spearman_max        | 0.8474     |

#### 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      | 0.8346    |
| **spearman_cosine** | **0.848** |
| pearson_manhattan   | 0.8471    |
| spearman_manhattan  | 0.8432    |
| pearson_euclidean   | 0.8476    |
| spearman_euclidean  | 0.8439    |
| pearson_dot         | 0.5891    |
| spearman_dot        | 0.5796    |
| pearson_max         | 0.8476    |
| spearman_max        | 0.848     |

#### 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      | 0.8264     |
| **spearman_cosine** | **0.8415** |
| pearson_manhattan   | 0.8414     |
| spearman_manhattan  | 0.8389     |
| pearson_euclidean   | 0.8423     |
| spearman_euclidean  | 0.8401     |
| pearson_dot         | 0.523      |
| spearman_dot        | 0.5099     |
| pearson_max         | 0.8423     |
| spearman_max        | 0.8415     |

#### 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      | 0.819      |
| **spearman_cosine** | **0.8376** |
| pearson_manhattan   | 0.835      |
| spearman_manhattan  | 0.8336     |
| pearson_euclidean   | 0.8365     |
| spearman_euclidean  | 0.8348     |
| pearson_dot         | 0.498      |
| spearman_dot        | 0.4897     |
| pearson_max         | 0.8365     |
| spearman_max        | 0.8376     |

#### 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      | 0.8062     |
| **spearman_cosine** | **0.8292** |
| pearson_manhattan   | 0.8237     |
| spearman_manhattan  | 0.8244     |
| pearson_euclidean   | 0.8273     |
| spearman_euclidean  | 0.827      |
| pearson_dot         | 0.4318     |
| spearman_dot        | 0.4325     |
| pearson_max         | 0.8273     |
| spearman_max        | 0.8292     |

#### 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      | 0.777      |
| **spearman_cosine** | **0.8132** |
| pearson_manhattan   | 0.8041     |
| spearman_manhattan  | 0.8084     |
| pearson_euclidean   | 0.809      |
| spearman_euclidean  | 0.8126     |
| pearson_dot         | 0.3722     |
| spearman_dot        | 0.3636     |
| pearson_max         | 0.809      |
| spearman_max        | 0.8132     |

#### Semantic Similarity
* Dataset: `sts-test-16`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7351     |
| **spearman_cosine** | **0.7811** |
| pearson_manhattan   | 0.7687     |
| spearman_manhattan  | 0.7767     |
| pearson_euclidean   | 0.7733     |
| spearman_euclidean  | 0.7799     |
| pearson_dot         | 0.2548     |
| spearman_dot        | 0.2412     |
| pearson_max         | 0.7733     |
| spearman_max        | 0.7811     |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### sentence-transformers/stsb

* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                        | score                                                          |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | float                                                          |
  | details | <ul><li>min: 6 tokens</li><li>mean: 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,
          16
      ],
      "matryoshka_weights": [
          1,
          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,
          16
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `bf16`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `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`: True
- `fp16`: False
- `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-16_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-16_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 |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 2.2222 | 100  | 60.4066       | 60.8718 | 0.8634                      | 0.7953                     | 0.8685                      | 0.8338                     | 0.8709                      | 0.8541                     | 0.8718                      | -                            | -                           | -                            | -                           | -                            | -                           | -                            |
| 4.0    | 180  | -             | -       | -                           | -                          | -                           | -                          | -                           | -                          | -                           | 0.8376                       | 0.7811                      | 0.8415                       | 0.8132                      | 0.8480                       | 0.8292                      | 0.8474                       |


### 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},
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->