xlm-roberta license:mit region:us
---
base_model: intfloat/multilingual-e5-base
datasets:
- E-FAQ
language:
- pt
- es
library_name: sentence-transformers
license: mit
metrics:
- cosine_accuracy@1
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@10
- cosine_recall@1
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@1
- cosine_map@10
- dot_accuracy@1
- dot_accuracy@10
- dot_precision@1
- dot_precision@10
- dot_recall@1
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@1
- dot_map@10
- euclidean_accuracy@1
- euclidean_accuracy@10
- euclidean_precision@1
- euclidean_precision@10
- euclidean_recall@1
- euclidean_recall@10
- euclidean_ndcg@10
- euclidean_mrr@10
- euclidean_map@1
- euclidean_map@10
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:119448
- loss:CompositionLoss
widget:
- source_sentence: Tem mandril com outras medidas
  sentences:
  - Bom dia vem tudo no kit conforme a foto?maquina de solda ,esquadro,máscara, 2
    rolos de arame é isso?
  - Você tem da magneti Marelli código 40421702 PARATI BOLA G2 96 MONOPONTO AP 1.6
    GASOLINA
  - 'Hola buenas. Es compatible para NEW Mitsubishi Montero cr 4x4 3.2 N. Chasis:
    JMBMNV88W8J000791'
- source_sentence: Hola tienes disponible de mono talla 12 a 18 meses?
  sentences:
  - Hola buen dia! Necesito una malla sombra como la de esta publicación pero de 4
    x 3.40 mts, en cuanto sale?
  - Serve na Duster automática 2.0
  - Lo que pasa es que no me deja agregar más de 1
- source_sentence: Viene con kit de instalacion y tornillería?
  sentences:
  - Bom dia. Tem como fixar no chão. Na grama?
  - La base para conectar ese foco la tendrá???
  - Pod ser usado para instalação de farol d milha ?
- source_sentence: corsa 2004 1.8 con ultimos 8 digitos NIV 4C210262
  sentences:
  - Le queda a un Derby 2007 1.8?
  - Serve no Corsa clacic 97 sedã
  - Boa tarde vc so tem.um ?
- source_sentence: Buenos días, es compatible con las apps bancarias?
  sentences:
  - Hola....el bulon de q diámetro es?
  - Se le puede quitar el microfono?
  - Serve para cachorrinha que está no cio?
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-base
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: E-FAQ
      type: text-retrieval
    metrics:
    - type: cosine_accuracy@1
      value: 0.7941531042796866
      name: Cosine Accuracy@1
    - type: cosine_accuracy@10
      value: 0.9483875828812538
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7941531042796866
      name: Cosine Precision@1
    - type: cosine_precision@10
      value: 0.17701928872814954
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5563725301557428
      name: Cosine Recall@1
    - type: cosine_recall@10
      value: 0.9093050609545924
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8420320427198602
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8476323229713864
      name: Cosine Mrr@10
    - type: cosine_map@1
      value: 0.7941531042796866
      name: Cosine Map@1
    - type: cosine_map@10
      value: 0.8004156235676744
      name: Cosine Map@10
    - type: dot_accuracy@1
      value: 0.7941531042796866
      name: Dot Accuracy@1
    - type: dot_accuracy@10
      value: 0.9483875828812538
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.7941531042796866
      name: Dot Precision@1
    - type: dot_precision@10
      value: 0.17701928872814954
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.5563725301557428
      name: Dot Recall@1
    - type: dot_recall@10
      value: 0.9093050609545924
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.8420320427198602
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.8476323229713864
      name: Dot Mrr@10
    - type: dot_map@1
      value: 0.7941531042796866
      name: Dot Map@1
    - type: dot_map@10
      value: 0.8004156235676744
      name: Dot Map@10
    - type: euclidean_accuracy@1
      value: 0.7941531042796866
      name: Euclidean Accuracy@1
    - type: euclidean_accuracy@10
      value: 0.9483875828812538
      name: Euclidean Accuracy@10
    - type: euclidean_precision@1
      value: 0.7941531042796866
      name: Euclidean Precision@1
    - type: euclidean_precision@10
      value: 0.17701928872814954
      name: Euclidean Precision@10
    - type: euclidean_recall@1
      value: 0.5563725301557428
      name: Euclidean Recall@1
    - type: euclidean_recall@10
      value: 0.9093050609545924
      name: Euclidean Recall@10
    - type: euclidean_ndcg@10
      value: 0.8420320427198602
      name: Euclidean Ndcg@10
    - type: euclidean_mrr@10
      value: 0.8476323229713864
      name: Euclidean Mrr@10
    - type: euclidean_map@1
      value: 0.7941531042796866
      name: Euclidean Map@1
    - type: euclidean_map@10
      value: 0.8004156235676744
      name: Euclidean Map@10
---

# Multilingual E5 Base Self-Distilled on E-FAQ

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

### Framework Versions
- Python: 3.12.4
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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",
}
```
