---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: l3cube-pune/indic-sentence-similarity-sbert
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Excuse me.
  sentences:
  - um pardon me
  - A man is opening mail.
  - The girls are indoors.
- source_sentence: Double pig.
  sentences:
  - Ah, triple pig!
  - a girl poses for camera
  - Girls dance together.
- source_sentence: People pose.
  sentences:
  - People are smiling.
  - I know a few old ones.
  - The boy fell off his bike.
- source_sentence: A man sings.
  sentences:
  - People singing
  - A man is playing golf.
  - The women are eating bread.
- source_sentence: Then he ran.
  sentences:
  - He then started to run.
  - A man plays the flute.
  - A couple sit on the couch
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on l3cube-pune/indic-sentence-similarity-sbert
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 768
      type: sts-dev-768
    metrics:
    - type: pearson_cosine
      value: 0.8608857207512975
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8662860178080238
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.858692209351004
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8612472945208892
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.858472048314985
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8611276457994067
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8258747949887901
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8259736371824636
      name: Spearman Dot
    - type: pearson_max
      value: 0.8608857207512975
      name: Pearson Max
    - type: spearman_max
      value: 0.8662860178080238
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 512
      type: sts-dev-512
    metrics:
    - type: pearson_cosine
      value: 0.8594405198312016
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8648571300070264
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8574291650964095
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8598780673781499
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8574540367546871
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8600722932569861
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.822340474813523
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8226609928783558
      name: Spearman Dot
    - type: pearson_max
      value: 0.8594405198312016
      name: Pearson Max
    - type: spearman_max
      value: 0.8648571300070264
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 256
      type: sts-dev-256
    metrics:
    - type: pearson_cosine
      value: 0.8506120561071212
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8575982860981437
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.852829777566948
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8552667517015687
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8526934293405145
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8551077930316164
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7943956137623474
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7963976287579885
      name: Spearman Dot
    - type: pearson_max
      value: 0.852829777566948
      name: Pearson Max
    - type: spearman_max
      value: 0.8575982860981437
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 128
      type: sts-dev-128
    metrics:
    - type: pearson_cosine
      value: 0.8410977354989039
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.850480817077266
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8461619224798919
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8490393633313068
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8458138708136093
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.848719989437845
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7755878071062363
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7755629190322909
      name: Spearman Dot
    - type: pearson_max
      value: 0.8461619224798919
      name: Pearson Max
    - type: spearman_max
      value: 0.850480817077266
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 64
      type: sts-dev-64
    metrics:
    - type: pearson_cosine
      value: 0.8176550213032908
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8307913870285043
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8291830276998975
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8320477651805375
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8311109004860973
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8333955109708812
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7153413665605783
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7181274999679498
      name: Spearman Dot
    - type: pearson_max
      value: 0.8311109004860973
      name: Pearson Max
    - type: spearman_max
      value: 0.8333955109708812
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.8491592809545866
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8568871215102605
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8572052385387118
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.856617432589014
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8568623186549655
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8567096295439565
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7968828934121807
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7879173370882538
      name: Spearman Dot
    - type: pearson_max
      value: 0.8572052385387118
      name: Pearson Max
    - type: spearman_max
      value: 0.8568871215102605
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.8507070298067174
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8575370129160172
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8564033014649287
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8560352984315738
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8561906595447021
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8560701630452845
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7973312469719326
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7873345752731498
      name: Spearman Dot
    - type: pearson_max
      value: 0.8564033014649287
      name: Pearson Max
    - type: spearman_max
      value: 0.8575370129160172
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.8467375811334358
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8523459221020806
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8515524299355154
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8516309696270962
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8505975029491393
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8504082169041302
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7756647219222156
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7687165011432322
      name: Spearman Dot
    - type: pearson_max
      value: 0.8515524299355154
      name: Pearson Max
    - type: spearman_max
      value: 0.8523459221020806
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.8377317518267889
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.84715184876888
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.846568244977152
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8487991796570058
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8456229087328332
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.847227591472
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7502527212449147
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7415962106597614
      name: Spearman Dot
    - type: pearson_max
      value: 0.846568244977152
      name: Pearson Max
    - type: spearman_max
      value: 0.8487991796570058
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.8173604263806156
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8315612974155435
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8319781289166863
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8347311175148256
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8334921243463637
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8350960592133633
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6935445265890855
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6843746062699552
      name: Spearman Dot
    - type: pearson_max
      value: 0.8334921243463637
      name: Pearson Max
    - type: spearman_max
      value: 0.8350960592133633
      name: Spearman Max
---

# SentenceTransformer based on l3cube-pune/indic-sentence-similarity-sbert

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [l3cube-pune/indic-sentence-similarity-sbert](https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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:** [l3cube-pune/indic-sentence-similarity-sbert](https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert) <!-- at revision b07ef91a96390f3e35ce94ddb42340861519bf07 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **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: BertModel 
  (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("ammumadhu/indic-bert-nli-matryoshka")
# Run inference
sentences = [
    'Then he ran.',
    'He then started to run.',
    'A man plays the flute.',
]
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.*
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## 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      | 0.8609     |
| **spearman_cosine** | **0.8663** |
| pearson_manhattan   | 0.8587     |
| spearman_manhattan  | 0.8612     |
| pearson_euclidean   | 0.8585     |
| spearman_euclidean  | 0.8611     |
| pearson_dot         | 0.8259     |
| spearman_dot        | 0.826      |
| pearson_max         | 0.8609     |
| spearman_max        | 0.8663     |

#### 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      | 0.8594     |
| **spearman_cosine** | **0.8649** |
| pearson_manhattan   | 0.8574     |
| spearman_manhattan  | 0.8599     |
| pearson_euclidean   | 0.8575     |
| spearman_euclidean  | 0.8601     |
| pearson_dot         | 0.8223     |
| spearman_dot        | 0.8227     |
| pearson_max         | 0.8594     |
| spearman_max        | 0.8649     |

#### 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      | 0.8506     |
| **spearman_cosine** | **0.8576** |
| pearson_manhattan   | 0.8528     |
| spearman_manhattan  | 0.8553     |
| pearson_euclidean   | 0.8527     |
| spearman_euclidean  | 0.8551     |
| pearson_dot         | 0.7944     |
| spearman_dot        | 0.7964     |
| pearson_max         | 0.8528     |
| spearman_max        | 0.8576     |

#### 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      | 0.8411     |
| **spearman_cosine** | **0.8505** |
| pearson_manhattan   | 0.8462     |
| spearman_manhattan  | 0.849      |
| pearson_euclidean   | 0.8458     |
| spearman_euclidean  | 0.8487     |
| pearson_dot         | 0.7756     |
| spearman_dot        | 0.7756     |
| pearson_max         | 0.8462     |
| spearman_max        | 0.8505     |

#### 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      | 0.8177     |
| **spearman_cosine** | **0.8308** |
| pearson_manhattan   | 0.8292     |
| spearman_manhattan  | 0.832      |
| pearson_euclidean   | 0.8311     |
| spearman_euclidean  | 0.8334     |
| pearson_dot         | 0.7153     |
| spearman_dot        | 0.7181     |
| pearson_max         | 0.8311     |
| spearman_max        | 0.8334     |

#### 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      | 0.8492     |
| **spearman_cosine** | **0.8569** |
| pearson_manhattan   | 0.8572     |
| spearman_manhattan  | 0.8566     |
| pearson_euclidean   | 0.8569     |
| spearman_euclidean  | 0.8567     |
| pearson_dot         | 0.7969     |
| spearman_dot        | 0.7879     |
| pearson_max         | 0.8572     |
| spearman_max        | 0.8569     |

#### 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      | 0.8507     |
| **spearman_cosine** | **0.8575** |
| pearson_manhattan   | 0.8564     |
| spearman_manhattan  | 0.856      |
| pearson_euclidean   | 0.8562     |
| spearman_euclidean  | 0.8561     |
| pearson_dot         | 0.7973     |
| spearman_dot        | 0.7873     |
| pearson_max         | 0.8564     |
| spearman_max        | 0.8575     |

#### 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      | 0.8467     |
| **spearman_cosine** | **0.8523** |
| pearson_manhattan   | 0.8516     |
| spearman_manhattan  | 0.8516     |
| pearson_euclidean   | 0.8506     |
| spearman_euclidean  | 0.8504     |
| pearson_dot         | 0.7757     |
| spearman_dot        | 0.7687     |
| pearson_max         | 0.8516     |
| spearman_max        | 0.8523     |

#### 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      | 0.8377     |
| **spearman_cosine** | **0.8472** |
| pearson_manhattan   | 0.8466     |
| spearman_manhattan  | 0.8488     |
| pearson_euclidean   | 0.8456     |
| spearman_euclidean  | 0.8472     |
| pearson_dot         | 0.7503     |
| spearman_dot        | 0.7416     |
| pearson_max         | 0.8466     |
| spearman_max        | 0.8488     |

#### 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      | 0.8174     |
| **spearman_cosine** | **0.8316** |
| pearson_manhattan   | 0.832      |
| spearman_manhattan  | 0.8347     |
| pearson_euclidean   | 0.8335     |
| spearman_euclidean  | 0.8351     |
| pearson_dot         | 0.6935     |
| spearman_dot        | 0.6844     |
| pearson_max         | 0.8335     |
| spearman_max        | 0.8351     |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### sentence-transformers/all-nli

* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 10,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 18.8 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.84 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.39 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
  | anchor                                                                                                    | positive                                          | negative                                               |
  |:----------------------------------------------------------------------------------------------------------|:--------------------------------------------------|:-------------------------------------------------------|
  | <code>Side view of a female triathlete during the run.</code>                                             | <code>A woman runs</code>                         | <code>A man sits</code>                                |
  | <code>Confused person standing in the middle of the trolley tracks trying to figure out the signs.</code> | <code>A person is on the tracks.</code>           | <code>A man sits in an airplane.</code>                |
  | <code>A woman in a black shirt, jean shorts and white tennis shoes is bowling.</code>                     | <code>A woman is bowling in casual clothes</code> | <code>A woman bowling wins an outfit of clothes</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### sentence-transformers/all-nli

* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 18.54 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.97 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.59 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### 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`: 1
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.3797 | 30   | 7.9432        | 4.2806 | 0.8509                      | 0.8570                      | 0.8633                      | 0.8311                     | 0.8644                      | -                            | -                            | -                            | -                           | -                            |
| 0.7595 | 60   | 6.1701        | 3.9498 | 0.8505                      | 0.8576                      | 0.8649                      | 0.8308                     | 0.8663                      | -                            | -                            | -                            | -                           | -                            |
| 1.0    | 79   | -             | -      | -                           | -                           | -                           | -                          | -                           | 0.8472                       | 0.8523                       | 0.8575                       | 0.8316                      | 0.8569                       |


### 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: 2.19.2
- 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}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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