---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:CoSENTLoss
base_model: intfloat/multilingual-e5-large
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: El hombre captura una pelota
  sentences:
  - Un hombre lanza una pelota en el aire.
  - Un hombre está acompañando a una mujer en el camino.
  - Dos mujeres están cantando una hermosa canción.
- source_sentence: La mujer está cortando papas.
  sentences:
  - Una mujer está cortando patatas.
  - Los patos blancos se encuentran parados en el suelo.
  - Hay una banda tocando en el escenario principal.
- source_sentence: Un hombre está buscando algo.
  sentences:
  - En un mercado de granjeros, se encuentra un hombre.
  - Romney filmó en una reunión privada de financiadores
  - Dos perros de color negro están jugando en la hierba.
- source_sentence: Un hombre saltando la cuerda.
  sentences:
  - Un hombre está saltando la cuerda.
  - La capital de Siria fue golpeada por dos explosiones
  - Los gatitos están comiendo de los platos.
- source_sentence: El avión está tocando tierra.
  sentences:
  - El avión animado se encuentra en proceso de aterrizaje.
  - Un pequeño niño montado en un columpio en el parque.
  - Una persona de sexo femenino está cortando una cebolla.
pipeline_tag: sentence-similarity
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: 0.8382359637067547
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8429605562993187
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8336600898033378
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8448900621318144
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8328580183902631
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8441561677427524
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8287262441829462
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8322746204974042
      name: Spearman Dot
    - type: pearson_max
      value: 0.8382359637067547
      name: Pearson Max
    - type: spearman_max
      value: 0.8448900621318144
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 512
      type: sts-dev-512
    metrics:
    - type: pearson_cosine
      value: 0.8334610747047482
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8405630189692351
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8316848819512679
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8426142019940397
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8305903222472721
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8415256700272777
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8172993617433827
      name: Pearson Dot
    - type: spearman_dot
      value: 0.823043401157181
      name: Spearman Dot
    - type: pearson_max
      value: 0.8334610747047482
      name: Pearson Max
    - type: spearman_max
      value: 0.8426142019940397
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 256
      type: sts-dev-256
    metrics:
    - type: pearson_cosine
      value: 0.8240056098321313
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8355774999921849
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8261458415991961
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8355100986320139
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.825647934422587
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8362336344962497
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7924886689283153
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7992788592975302
      name: Spearman Dot
    - type: pearson_max
      value: 0.8261458415991961
      name: Pearson Max
    - type: spearman_max
      value: 0.8362336344962497
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 128
      type: sts-dev-128
    metrics:
    - type: pearson_cosine
      value: 0.8098656853945027
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8304511476467773
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8208946291392102
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8308359029901535
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8195023110971954
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8302481276550623
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7412744037070784
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7489986968697009
      name: Spearman Dot
    - type: pearson_max
      value: 0.8208946291392102
      name: Pearson Max
    - type: spearman_max
      value: 0.8308359029901535
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 64
      type: sts-dev-64
    metrics:
    - type: pearson_cosine
      value: 0.7777717898212414
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8152005256760807
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8007095698339157
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8116493253806699
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8000905317852872
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8110794468804238
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6540905690432955
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6589924104221199
      name: Spearman Dot
    - type: pearson_max
      value: 0.8007095698339157
      name: Pearson Max
    - type: spearman_max
      value: 0.8152005256760807
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 32
      type: sts-dev-32
    metrics:
    - type: pearson_cosine
      value: 0.7276908730898617
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7805691037554072
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7659952363354546
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7751944660837697
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7674462214503804
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7773298298599879
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5395044219284906
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5341543426421572
      name: Spearman Dot
    - type: pearson_max
      value: 0.7674462214503804
      name: Pearson Max
    - type: spearman_max
      value: 0.7805691037554072
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 16
      type: sts-dev-16
    metrics:
    - type: pearson_cosine
      value: 0.6737235484120327
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7425360948217027
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7187007732867645
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7279621825071231
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7234911258158329
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7374355146279606
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.44701957007430754
      name: Pearson Dot
    - type: spearman_dot
      value: 0.44243975098384164
      name: Spearman Dot
    - type: pearson_max
      value: 0.7234911258158329
      name: Pearson Max
    - type: spearman_max
      value: 0.7425360948217027
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.8637130740455785
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8774757245850818
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8739327947840198
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8771247494149252
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8742964420051067
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8774039769000851
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8587248460103846
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8692624735733635
      name: Spearman Dot
    - type: pearson_max
      value: 0.8742964420051067
      name: Pearson Max
    - type: spearman_max
      value: 0.8774757245850818
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.8608902316971913
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8761454408181157
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8723366100239835
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8755119028724399
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8727143818945785
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8758699632438892
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8498181878456328
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8568165420931783
      name: Spearman Dot
    - type: pearson_max
      value: 0.8727143818945785
      name: Pearson Max
    - type: spearman_max
      value: 0.8761454408181157
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.8546354043013908
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.871536658256446
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8697716394077537
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8737030599161743
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.86989853825415
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8736845554686979
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8131428680674924
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8076436370339797
      name: Spearman Dot
    - type: pearson_max
      value: 0.86989853825415
      name: Pearson Max
    - type: spearman_max
      value: 0.8737030599161743
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.8387977115140051
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8645489592292456
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8611375341227384
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8667215229295422
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.862154474303328
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8680162798983022
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7492475609746636
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7363955675375832
      name: Spearman Dot
    - type: pearson_max
      value: 0.862154474303328
      name: Pearson Max
    - type: spearman_max
      value: 0.8680162798983022
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.8168102869303625
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8585329796388539
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8518107264951738
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8606717941407515
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8533959511853835
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8623753165991692
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6646337116783656
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6473141838302237
      name: Spearman Dot
    - type: pearson_max
      value: 0.8533959511853835
      name: Pearson Max
    - type: spearman_max
      value: 0.8623753165991692
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 32
      type: sts-test-32
    metrics:
    - type: pearson_cosine
      value: 0.7813945227753345
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8424823964509079
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8315336527432531
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8431756901550471
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8345328653107531
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8466076672836096
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5520860449837447
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5319238671245338
      name: Spearman Dot
    - type: pearson_max
      value: 0.8345328653107531
      name: Pearson Max
    - type: spearman_max
      value: 0.8466076672836096
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 16
      type: sts-test-16
    metrics:
    - type: pearson_cosine
      value: 0.7198004009567176
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8072120165730962
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7805727606105963
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7997833060148871
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7879106231813758
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8090073332632988
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.44957276876149327
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4411623904572447
      name: Spearman Dot
    - type: pearson_max
      value: 0.7879106231813758
      name: Pearson Max
    - type: spearman_max
      value: 0.8090073332632988
      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 an augmented version of `stsb_multi_es` 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-768-16-5e")
# Run inference
sentences = [
    'El avión está tocando tierra.',
    'El avión animado se encuentra en proceso de aterrizaje.',
    'Un pequeño niño montado en un columpio en el parque.',
]
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)

<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      | 0.8382    |
| **spearman_cosine** | **0.843** |
| pearson_manhattan   | 0.8337    |
| spearman_manhattan  | 0.8449    |
| pearson_euclidean   | 0.8329    |
| spearman_euclidean  | 0.8442    |
| pearson_dot         | 0.8287    |
| spearman_dot        | 0.8323    |
| pearson_max         | 0.8382    |
| spearman_max        | 0.8449    |

#### 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      | 0.8335     |
| **spearman_cosine** | **0.8406** |
| pearson_manhattan   | 0.8317     |
| spearman_manhattan  | 0.8426     |
| pearson_euclidean   | 0.8306     |
| spearman_euclidean  | 0.8415     |
| pearson_dot         | 0.8173     |
| spearman_dot        | 0.823      |
| pearson_max         | 0.8335     |
| spearman_max        | 0.8426     |

#### 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      | 0.824      |
| **spearman_cosine** | **0.8356** |
| pearson_manhattan   | 0.8261     |
| spearman_manhattan  | 0.8355     |
| pearson_euclidean   | 0.8256     |
| spearman_euclidean  | 0.8362     |
| pearson_dot         | 0.7925     |
| spearman_dot        | 0.7993     |
| pearson_max         | 0.8261     |
| spearman_max        | 0.8362     |

#### 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      | 0.8099     |
| **spearman_cosine** | **0.8305** |
| pearson_manhattan   | 0.8209     |
| spearman_manhattan  | 0.8308     |
| pearson_euclidean   | 0.8195     |
| spearman_euclidean  | 0.8302     |
| pearson_dot         | 0.7413     |
| spearman_dot        | 0.749      |
| pearson_max         | 0.8209     |
| spearman_max        | 0.8308     |

#### 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      | 0.7778     |
| **spearman_cosine** | **0.8152** |
| pearson_manhattan   | 0.8007     |
| spearman_manhattan  | 0.8116     |
| pearson_euclidean   | 0.8001     |
| spearman_euclidean  | 0.8111     |
| pearson_dot         | 0.6541     |
| spearman_dot        | 0.659      |
| pearson_max         | 0.8007     |
| spearman_max        | 0.8152     |

#### 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      | 0.7277     |
| **spearman_cosine** | **0.7806** |
| pearson_manhattan   | 0.766      |
| spearman_manhattan  | 0.7752     |
| pearson_euclidean   | 0.7674     |
| spearman_euclidean  | 0.7773     |
| pearson_dot         | 0.5395     |
| spearman_dot        | 0.5342     |
| pearson_max         | 0.7674     |
| spearman_max        | 0.7806     |

#### 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      | 0.6737     |
| **spearman_cosine** | **0.7425** |
| pearson_manhattan   | 0.7187     |
| spearman_manhattan  | 0.728      |
| pearson_euclidean   | 0.7235     |
| spearman_euclidean  | 0.7374     |
| pearson_dot         | 0.447      |
| spearman_dot        | 0.4424     |
| pearson_max         | 0.7235     |
| spearman_max        | 0.7425     |

#### 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      | 0.8637     |
| **spearman_cosine** | **0.8775** |
| pearson_manhattan   | 0.8739     |
| spearman_manhattan  | 0.8771     |
| pearson_euclidean   | 0.8743     |
| spearman_euclidean  | 0.8774     |
| pearson_dot         | 0.8587     |
| spearman_dot        | 0.8693     |
| pearson_max         | 0.8743     |
| spearman_max        | 0.8775     |

#### 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      | 0.8609     |
| **spearman_cosine** | **0.8761** |
| pearson_manhattan   | 0.8723     |
| spearman_manhattan  | 0.8755     |
| pearson_euclidean   | 0.8727     |
| spearman_euclidean  | 0.8759     |
| pearson_dot         | 0.8498     |
| spearman_dot        | 0.8568     |
| pearson_max         | 0.8727     |
| spearman_max        | 0.8761     |

#### 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      | 0.8546     |
| **spearman_cosine** | **0.8715** |
| pearson_manhattan   | 0.8698     |
| spearman_manhattan  | 0.8737     |
| pearson_euclidean   | 0.8699     |
| spearman_euclidean  | 0.8737     |
| pearson_dot         | 0.8131     |
| spearman_dot        | 0.8076     |
| pearson_max         | 0.8699     |
| spearman_max        | 0.8737     |

#### 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      | 0.8388     |
| **spearman_cosine** | **0.8645** |
| pearson_manhattan   | 0.8611     |
| spearman_manhattan  | 0.8667     |
| pearson_euclidean   | 0.8622     |
| spearman_euclidean  | 0.868      |
| pearson_dot         | 0.7492     |
| spearman_dot        | 0.7364     |
| pearson_max         | 0.8622     |
| spearman_max        | 0.868      |

#### 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      | 0.8168     |
| **spearman_cosine** | **0.8585** |
| pearson_manhattan   | 0.8518     |
| spearman_manhattan  | 0.8607     |
| pearson_euclidean   | 0.8534     |
| spearman_euclidean  | 0.8624     |
| pearson_dot         | 0.6646     |
| spearman_dot        | 0.6473     |
| pearson_max         | 0.8534     |
| spearman_max        | 0.8624     |

#### 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      | 0.7814     |
| **spearman_cosine** | **0.8425** |
| pearson_manhattan   | 0.8315     |
| spearman_manhattan  | 0.8432     |
| pearson_euclidean   | 0.8345     |
| spearman_euclidean  | 0.8466     |
| pearson_dot         | 0.5521     |
| spearman_dot        | 0.5319     |
| pearson_max         | 0.8345     |
| spearman_max        | 0.8466     |

#### 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      | 0.7198     |
| **spearman_cosine** | **0.8072** |
| pearson_manhattan   | 0.7806     |
| spearman_manhattan  | 0.7998     |
| pearson_euclidean   | 0.7879     |
| spearman_euclidean  | 0.809      |
| pearson_dot         | 0.4496     |
| spearman_dot        | 0.4412     |
| pearson_max         | 0.7879     |
| spearman_max        | 0.809      |

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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,
          32,
          16
      ],
      "matryoshka_weights": [
          1,
          1,
          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,
          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`: 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-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 |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.5917 | 100  | 30.7503       | 30.6172 | 0.8117                      | 0.7110                     | 0.8179                      | 0.7457                     | 0.8244                      | 0.7884                     | 0.8252                      | -                            | -                           | -                            | -                           | -                            | -                           | -                            |
| 1.1834 | 200  | 30.4696       | 32.6422 | 0.7952                      | 0.7198                     | 0.8076                      | 0.7491                     | 0.8125                      | 0.7813                     | 0.8142                      | -                            | -                           | -                            | -                           | -                            | -                           | -                            |
| 1.7751 | 300  | 29.9233       | 31.5469 | 0.8152                      | 0.7435                     | 0.8250                      | 0.7737                     | 0.8302                      | 0.8006                     | 0.8305                      | -                            | -                           | -                            | -                           | -                            | -                           | -                            |
| 2.3669 | 400  | 29.0716       | 31.8088 | 0.8183                      | 0.7405                     | 0.8248                      | 0.7758                     | 0.8299                      | 0.8057                     | 0.8324                      | -                            | -                           | -                            | -                           | -                            | -                           | -                            |
| 2.9586 | 500  | 28.7971       | 32.6032 | 0.8176                      | 0.7430                     | 0.8241                      | 0.7777                     | 0.8289                      | 0.8025                     | 0.8316                      | -                            | -                           | -                            | -                           | -                            | -                           | -                            |
| 3.5503 | 600  | 27.4766       | 34.7911 | 0.8241                      | 0.7400                     | 0.8314                      | 0.7730                     | 0.8369                      | 0.8061                     | 0.8394                      | -                            | -                           | -                            | -                           | -                            | -                           | -                            |
| 4.1420 | 700  | 27.0639       | 35.7418 | 0.8294                      | 0.7466                     | 0.8354                      | 0.7784                     | 0.8389                      | 0.8107                     | 0.8409                      | -                            | -                           | -                            | -                           | -                            | -                           | -                            |
| 4.7337 | 800  | 26.5119       | 36.2014 | 0.8305                      | 0.7425                     | 0.8356                      | 0.7806                     | 0.8406                      | 0.8152                     | 0.8430                      | -                            | -                           | -                            | -                           | -                            | -                           | -                            |
| 5.0    | 845  | -             | -       | -                           | -                          | -                           | -                          | -                           | -                          | -                           | 0.8645                       | 0.8072                      | 0.8715                       | 0.8425                      | 0.8761                       | 0.8585                      | 0.8775                       |


### 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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