chore: update readme with trainer informations

README.md

@@ -1,3 +1,219 @@
 ---
 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](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",
+}
+```