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---
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 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.
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: 0.8279951103268512
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)
<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 | 0.828 |
| **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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