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---
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license: mit
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base_model: intfloat/multilingual-e5-base
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datasets:
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- E-FAQ
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language:
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- pt
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- es
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library_name: sentence-transformers
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metrics:
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- cosine_accuracy@1
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- cosine_accuracy@10
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- cosine_precision@1
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- cosine_precision@10
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- cosine_recall@1
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- cosine_recall@10
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- cosine_ndcg@10
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- cosine_mrr@10
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- cosine_map@1
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- cosine_map@10
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- dot_accuracy@1
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- dot_accuracy@10
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- dot_precision@1
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- dot_precision@10
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- dot_recall@1
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- dot_recall@10
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- dot_ndcg@10
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- dot_mrr@10
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- dot_map@1
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- dot_map@10
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- euclidean_accuracy@1
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- euclidean_accuracy@10
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- euclidean_precision@1
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- euclidean_precision@10
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- euclidean_recall@1
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- euclidean_recall@10
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- euclidean_ndcg@10
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- euclidean_mrr@10
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- euclidean_map@1
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- euclidean_map@10
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:119448
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- loss:CompositionLoss
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widget:
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- source_sentence: Tem mandril com outras medidas
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sentences:
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- Bom dia vem tudo no kit conforme a foto?maquina de solda ,esquadro,máscara, 2
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rolos de arame é isso?
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- Você tem da magneti Marelli código 40421702 PARATI BOLA G2 96 MONOPONTO AP 1.6
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GASOLINA
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- 'Hola buenas. Es compatible para NEW Mitsubishi Montero cr 4x4 3.2 N. Chasis:
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JMBMNV88W8J000791'
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- source_sentence: Hola tienes disponible de mono talla 12 a 18 meses?
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sentences:
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- Hola buen dia! Necesito una malla sombra como la de esta publicación pero de 4
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x 3.40 mts, en cuanto sale?
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- Serve na Duster automática 2.0
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- Lo que pasa es que no me deja agregar más de 1
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- source_sentence: Viene con kit de instalacion y tornillería?
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sentences:
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- Bom dia. Tem como fixar no chão. Na grama?
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- La base para conectar ese foco la tendrá???
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- Pod ser usado para instalação de farol d milha ?
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- source_sentence: corsa 2004 1.8 con ultimos 8 digitos NIV 4C210262
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sentences:
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- Le queda a un Derby 2007 1.8?
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- Serve no Corsa clacic 97 sedã
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- Boa tarde vc so tem.um ?
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- source_sentence: Buenos días, es compatible con las apps bancarias?
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sentences:
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- Hola....el bulon de q diámetro es?
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- Se le puede quitar el microfono?
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- Serve para cachorrinha que está no cio?
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model-index:
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- name: SentenceTransformer based on intfloat/multilingual-e5-base
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results:
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- task:
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type: information-retrieval
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name: Information Retrieval
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dataset:
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name: E-FAQ
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type: text-retrieval
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metrics:
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- type: cosine_accuracy@1
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value: 0.7941531042796866
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name: Cosine Accuracy@1
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- type: cosine_accuracy@10
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value: 0.9483875828812538
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name: Cosine Accuracy@10
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- type: cosine_precision@1
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value: 0.7941531042796866
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name: Cosine Precision@1
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- type: cosine_precision@10
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value: 0.17701928872814954
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name: Cosine Precision@10
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- type: cosine_recall@1
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value: 0.5563725301557428
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name: Cosine Recall@1
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- type: cosine_recall@10
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value: 0.9093050609545924
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name: Cosine Recall@10
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- type: cosine_ndcg@10
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value: 0.8420320427198602
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name: Cosine Ndcg@10
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- type: cosine_mrr@10
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value: 0.8476323229713864
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name: Cosine Mrr@10
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- type: cosine_map@1
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value: 0.7941531042796866
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name: Cosine Map@1
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- type: cosine_map@10
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value: 0.8004156235676744
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name: Cosine Map@10
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- type: dot_accuracy@1
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value: 0.7941531042796866
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name: Dot Accuracy@1
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- type: dot_accuracy@10
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value: 0.9483875828812538
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name: Dot Accuracy@10
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- type: dot_precision@1
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value: 0.7941531042796866
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name: Dot Precision@1
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- type: dot_precision@10
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value: 0.17701928872814954
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name: Dot Precision@10
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- type: dot_recall@1
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value: 0.5563725301557428
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name: Dot Recall@1
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- type: dot_recall@10
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value: 0.9093050609545924
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name: Dot Recall@10
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- type: dot_ndcg@10
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value: 0.8420320427198602
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name: Dot Ndcg@10
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- type: dot_mrr@10
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value: 0.8476323229713864
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name: Dot Mrr@10
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- type: dot_map@1
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value: 0.7941531042796866
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name: Dot Map@1
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- type: dot_map@10
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value: 0.8004156235676744
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name: Dot Map@10
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- type: euclidean_accuracy@1
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value: 0.7941531042796866
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name: Euclidean Accuracy@1
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- type: euclidean_accuracy@10
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value: 0.9483875828812538
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name: Euclidean Accuracy@10
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- type: euclidean_precision@1
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value: 0.7941531042796866
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name: Euclidean Precision@1
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- type: euclidean_precision@10
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value: 0.17701928872814954
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name: Euclidean Precision@10
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- type: euclidean_recall@1
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value: 0.5563725301557428
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name: Euclidean Recall@1
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- type: euclidean_recall@10
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value: 0.9093050609545924
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name: Euclidean Recall@10
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- type: euclidean_ndcg@10
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value: 0.8420320427198602
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name: Euclidean Ndcg@10
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- type: euclidean_mrr@10
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value: 0.8476323229713864
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name: Euclidean Mrr@10
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- type: euclidean_map@1
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value: 0.7941531042796866
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name: Euclidean Map@1
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- type: euclidean_map@10
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value: 0.8004156235676744
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name: Euclidean Map@10
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---
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# Multilingual E5 Base Self-Distilled on E-FAQ
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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.
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
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(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})
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(2): Normalize()
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)
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```
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### Framework Versions
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- Python: 3.12.4
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- Sentence Transformers: 3.0.1
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- Transformers: 4.42.4
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- PyTorch: 2.3.1+cu121
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- Accelerate: 0.32.1
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- Datasets: 2.20.0
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- Tokenizers: 0.19.1
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## Citation
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### BibTeX
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#### Sentence Transformers
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```bibtex
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@inproceedings{reimers-2019-sentence-bert,
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2019",
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publisher = "Association for Computational Linguistics",
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url = "https://arxiv.org/abs/1908.10084",
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}
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```
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