sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction dataset_size:1K<n<10K loss:MatryoshkaLoss loss:CoSENTLoss arxiv:1908.10084 arxiv:2205.13147 base_model:intfloat/multilingual-e5-large base_model:finetune:intfloat/multilingual-e5-large model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: intfloat/multilingual-e5-large
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:CoSENTLoss
widget:
- source_sentence: El hombre captura una pelota
  sentences:
  - Un hombre lanza una pelota en el aire.
  - Un hombre se encuentra tocando una flauta de madera.
  - La mujer está maquillándose usando sombra de ojos.
- source_sentence: Un hombre está buscando algo.
  sentences:
  - En un mercado de granjeros, se encuentra un hombre.
  - Se acerca a la pista un avión suizo de color blanco.
  - dos chicas jóvenes se abrazan en la hierba.
- source_sentence: El avión está tocando tierra.
  sentences:
  - El avión animado se encuentra en proceso de aterrizaje.
  - La capital de Siria fue golpeada por dos explosiones
  - Violentos incidentes afectan a estudiantes chinos en Francia
- source_sentence: Un hombre saltando la cuerda.
  sentences:
  - Un hombre está saltando la cuerda.
  - Una mujer entrena a su perro para saltar en el aire.
  - Los gatitos están comiendo de los platos.
- source_sentence: tres perros gruñendo entre sí
  sentences:
  - Dos perros se aproximan uno al otro en el pasto.
  - Una mujer sonriente brinda cariño a un pequeño bebé.
  - Una mujer está montando a caballo en el campo.
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-large
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 768
      type: sts-dev-768
    metrics:
    - type: pearson_cosine
      value: 0.8279951103268512
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8342643795984531
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8228439538329566
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.834870903153992
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8231076969394738
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8349270059177344
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8196281042113861
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8248683461954115
      name: Spearman Dot
    - type: pearson_max
      value: 0.8279951103268512
      name: Pearson Max
    - type: spearman_max
      value: 0.8349270059177344
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 512
      type: sts-dev-512
    metrics:
    - type: pearson_cosine
      value: 0.8236357426336446
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8332692872015282
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8217552769156274
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8331746060276878
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8217859136681092
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8334069456110773
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8101789790612713
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8179205607773823
      name: Spearman Dot
    - type: pearson_max
      value: 0.8236357426336446
      name: Pearson Max
    - type: spearman_max
      value: 0.8334069456110773
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 256
      type: sts-dev-256
    metrics:
    - type: pearson_cosine
      value: 0.816222860848086
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8303708513421737
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8178715987143794
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8301047046554985
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8183826652089494
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8301804247624904
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7878741921967743
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7904844114269662
      name: Spearman Dot
    - type: pearson_max
      value: 0.8183826652089494
      name: Pearson Max
    - type: spearman_max
      value: 0.8303708513421737
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 128
      type: sts-dev-128
    metrics:
    - type: pearson_cosine
      value: 0.794202606017138
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8198385906414491
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8088714046889546
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8222921243120748
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8092312345267045
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8220266161646009
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7341586721030032
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7351749794310246
      name: Spearman Dot
    - type: pearson_max
      value: 0.8092312345267045
      name: Pearson Max
    - type: spearman_max
      value: 0.8222921243120748
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 64
      type: sts-dev-64
    metrics:
    - type: pearson_cosine
      value: 0.7727295051414095
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8076629783565549
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7976419723073269
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8147883308842346
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7979124462870892
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8123832197697319
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6725844492342726
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6673162832940408
      name: Spearman Dot
    - type: pearson_max
      value: 0.7979124462870892
      name: Pearson Max
    - type: spearman_max
      value: 0.8147883308842346
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.8630482725201897
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8813284718659181
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8770818288812614
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8810971983428288
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8770132070253477
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8812162173545179
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8581811981775829
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8707402246720045
      name: Spearman Dot
    - type: pearson_max
      value: 0.8770818288812614
      name: Pearson Max
    - type: spearman_max
      value: 0.8813284718659181
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.8589909139210625
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8799604919891442
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8744468387217347
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8791142262015441
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8747974723064821
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8795698184784307
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8464185524060444
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8549652098582826
      name: Spearman Dot
    - type: pearson_max
      value: 0.8747974723064821
      name: Pearson Max
    - type: spearman_max
      value: 0.8799604919891442
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.8528262537030415
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8762917275750132
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8715060008387856
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8780718380107112
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.87251419758469
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8788770265821976
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.801980870958869
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8007112694661982
      name: Spearman Dot
    - type: pearson_max
      value: 0.87251419758469
      name: Pearson Max
    - type: spearman_max
      value: 0.8788770265821976
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.8392066286150661
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8692426944903685
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8631603748425567
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8715673768304316
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8643871758114816
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8724091426441261
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7461565194503229
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7403017354497338
      name: Spearman Dot
    - type: pearson_max
      value: 0.8643871758114816
      name: Pearson Max
    - type: spearman_max
      value: 0.8724091426441261
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.8213671607347727
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8621003145087452
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8530869243121955
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8631973638935834
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.854140567169475
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8632627342101252
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6853599968011839
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6726454086764928
      name: Spearman Dot
    - type: pearson_max
      value: 0.854140567169475
      name: Pearson Max
    - type: spearman_max
      value: 0.8632627342101252
      name: Spearman Max
---

# SentenceTransformer based on intfloat/multilingual-e5-large

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on the clibrain/stsb_multi_es_aug_gpt3.5-turbo_2 dataset. It maps sentences & paragraphs to a 1024-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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - stsb_multi_es_aug
<!-- - **Language:** Unknown -->
<!-- - **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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (2): Normalize()
)
```

## 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/multilingual-e5-large-ft-sts-spanish-matryoshka-768-64-5e")
# Run inference
sentences = [
    'tres perros gruñendo entre sí',
    'Dos perros se aproximan uno al otro en el pasto.',
    'Una mujer sonriente brinda cariño a un pequeño bebé.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

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</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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### Out-of-Scope Use

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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      | 0.828      |
| **spearman_cosine** | **0.8343** |
| pearson_manhattan   | 0.8228     |
| spearman_manhattan  | 0.8349     |
| pearson_euclidean   | 0.8231     |
| spearman_euclidean  | 0.8349     |
| pearson_dot         | 0.8196     |
| spearman_dot        | 0.8249     |
| pearson_max         | 0.828      |
| spearman_max        | 0.8349     |

#### 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      | 0.8236     |
| **spearman_cosine** | **0.8333** |
| pearson_manhattan   | 0.8218     |
| spearman_manhattan  | 0.8332     |
| pearson_euclidean   | 0.8218     |
| spearman_euclidean  | 0.8334     |
| pearson_dot         | 0.8102     |
| spearman_dot        | 0.8179     |
| pearson_max         | 0.8236     |
| spearman_max        | 0.8334     |

#### 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      | 0.8162     |
| **spearman_cosine** | **0.8304** |
| pearson_manhattan   | 0.8179     |
| spearman_manhattan  | 0.8301     |
| pearson_euclidean   | 0.8184     |
| spearman_euclidean  | 0.8302     |
| pearson_dot         | 0.7879     |
| spearman_dot        | 0.7905     |
| pearson_max         | 0.8184     |
| spearman_max        | 0.8304     |

#### 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      | 0.7942     |
| **spearman_cosine** | **0.8198** |
| pearson_manhattan   | 0.8089     |
| spearman_manhattan  | 0.8223     |
| pearson_euclidean   | 0.8092     |
| spearman_euclidean  | 0.822      |
| pearson_dot         | 0.7342     |
| spearman_dot        | 0.7352     |
| pearson_max         | 0.8092     |
| spearman_max        | 0.8223     |

#### 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      | 0.7727     |
| **spearman_cosine** | **0.8077** |
| pearson_manhattan   | 0.7976     |
| spearman_manhattan  | 0.8148     |
| pearson_euclidean   | 0.7979     |
| spearman_euclidean  | 0.8124     |
| pearson_dot         | 0.6726     |
| spearman_dot        | 0.6673     |
| pearson_max         | 0.7979     |
| spearman_max        | 0.8148     |

#### 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      | 0.863      |
| **spearman_cosine** | **0.8813** |
| pearson_manhattan   | 0.8771     |
| spearman_manhattan  | 0.8811     |
| pearson_euclidean   | 0.877      |
| spearman_euclidean  | 0.8812     |
| pearson_dot         | 0.8582     |
| spearman_dot        | 0.8707     |
| pearson_max         | 0.8771     |
| spearman_max        | 0.8813     |

#### 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      | 0.859    |
| **spearman_cosine** | **0.88** |
| pearson_manhattan   | 0.8744   |
| spearman_manhattan  | 0.8791   |
| pearson_euclidean   | 0.8748   |
| spearman_euclidean  | 0.8796   |
| pearson_dot         | 0.8464   |
| spearman_dot        | 0.855    |
| pearson_max         | 0.8748   |
| spearman_max        | 0.88     |

#### 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      | 0.8528     |
| **spearman_cosine** | **0.8763** |
| pearson_manhattan   | 0.8715     |
| spearman_manhattan  | 0.8781     |
| pearson_euclidean   | 0.8725     |
| spearman_euclidean  | 0.8789     |
| pearson_dot         | 0.802      |
| spearman_dot        | 0.8007     |
| pearson_max         | 0.8725     |
| spearman_max        | 0.8789     |

#### 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      | 0.8392     |
| **spearman_cosine** | **0.8692** |
| pearson_manhattan   | 0.8632     |
| spearman_manhattan  | 0.8716     |
| pearson_euclidean   | 0.8644     |
| spearman_euclidean  | 0.8724     |
| pearson_dot         | 0.7462     |
| spearman_dot        | 0.7403     |
| pearson_max         | 0.8644     |
| spearman_max        | 0.8724     |

#### 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      | 0.8214     |
| **spearman_cosine** | **0.8621** |
| pearson_manhattan   | 0.8531     |
| spearman_manhattan  | 0.8632     |
| pearson_euclidean   | 0.8541     |
| spearman_euclidean  | 0.8633     |
| pearson_dot         | 0.6854     |
| spearman_dot        | 0.6726     |
| pearson_max         | 0.8541     |
| spearman_max        | 0.8633     |

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## Training Details

### Training Dataset

#### stsb_multi_es_aug

* Dataset: stsb_multi_es_aug
* Size: 2,697 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: 8 tokens</li><li>mean: 22.25 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 22.01 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.67</li><li>max: 5.0</li></ul> |
* Samples:
  | sentence1                                                                                             | sentence2                                                                                                    | score                          |
  |:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:-------------------------------|
  | <code>El pájaro de tamaño reducido se posó con delicadeza en una rama cubierta de escarcha.</code>    | <code>Un ave de color amarillo descansaba tranquilamente en una rama.</code>                                 | <code>3.200000047683716</code> |
  | <code>Una chica está tocando la flauta en un parque.</code>                                           | <code>Un grupo de músicos está tocando en un escenario al aire libre.</code>                                 | <code>1.286</code>             |
  | <code>La aclamada escritora británica, Doris Lessing, galardonada con el premio Nobel, fallece</code> | <code>La destacada autora británica, Doris Lessing, reconocida con el prestigioso Premio Nobel, muere</code> | <code>4.199999809265137</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
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### stsb_multi_es_aug

* Dataset: stsb_multi_es_aug
* Size: 697 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: 8 tokens</li><li>mean: 22.76 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 22.26 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.3</li><li>max: 5.0</li></ul> |
* Samples:
  | sentence1                                                                                                                                                           | sentence2                                                                                                                                                     | score                          |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|
  | <code>Un incendio ocurrido en un hospital psiquiátrico ruso resultó en la trágica muerte de 38 personas.</code>                                                     | <code>Se teme que el incendio en un hospital psiquiátrico ruso cause la pérdida de la vida de 38 individuos.</code>                                           | <code>4.199999809265137</code> |
  | <code>"Street dijo que el otro individuo a veces se siente avergonzado de su fiesta, lo cual provoca risas en la multitud"</code>                                   | <code>"A veces, el otro tipo se encuentra avergonzado de su fiesta y no se le puede culpar."</code>                                                           | <code>3.5</code>               |
  | <code>El veterano diplomático de Malasia tuvo un encuentro con Suu Kyi el miércoles en la casa del lago en Yangon donde permanece bajo arresto domiciliario.</code> | <code>Razali Ismail tuvo una reunión de 90 minutos con Suu Kyi, quien ganó el Premio Nobel de la Paz en 1991, en su casa del lago donde está recluida.</code> | <code>3.691999912261963</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
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `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`: 5
- `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-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 |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.5917 | 100  | 21.7032       | 21.7030 | 0.8030                      | 0.8124                      | 0.8205                      | 0.7839                     | 0.8215                      | -                            | -                            | -                            | -                           | -                            |
| 1.1834 | 200  | 21.4019       | 24.0898 | 0.7839                      | 0.7972                      | 0.8038                      | 0.7680                     | 0.8062                      | -                            | -                            | -                            | -                           | -                            |
| 1.7751 | 300  | 21.2168       | 22.5421 | 0.7909                      | 0.8027                      | 0.8058                      | 0.7786                     | 0.8068                      | -                            | -                            | -                            | -                           | -                            |
| 2.3669 | 400  | 20.7049       | 23.6522 | 0.7938                      | 0.8049                      | 0.8108                      | 0.7873                     | 0.8123                      | -                            | -                            | -                            | -                           | -                            |
| 2.9586 | 500  | 20.5077       | 23.6100 | 0.8017                      | 0.8116                      | 0.8155                      | 0.7893                     | 0.8185                      | -                            | -                            | -                            | -                           | -                            |
| 3.5503 | 600  | 19.2725       | 24.7539 | 0.8133                      | 0.8254                      | 0.8291                      | 0.8032                     | 0.8314                      | -                            | -                            | -                            | -                           | -                            |
| 4.1420 | 700  | 19.0841       | 26.5286 | 0.8210                      | 0.8298                      | 0.8333                      | 0.8102                     | 0.8333                      | -                            | -                            | -                            | -                           | -                            |
| 4.7337 | 800  | 18.6847       | 26.8158 | 0.8198                      | 0.8304                      | 0.8333                      | 0.8077                     | 0.8343                      | -                            | -                            | -                            | -                           | -                            |
| 5.0    | 845  | -             | -       | -                           | -                           | -                           | -                          | -                           | 0.8692                       | 0.8763                       | 0.8800                       | 0.8621                      | 0.8813                       |


### 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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