metadata
license: mit
base_model: intfloat/multilingual-e5-base
datasets:
- E-FAQ
language:
- pt
- es
library_name: sentence-transformers
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 model finetuned from 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
@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",
}