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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 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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