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Browse files- .gitattributes +1 -0
- README.md +42 -0
- config.json +25 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +56 -0
- vocab.json +0 -0
- vocab.txt +0 -0
.gitattributes
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model.safetensors filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: Modelo de Preguntas y Respuestas en Español
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tags:
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- question-answering
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- español
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- transformers
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license: mit
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language: es
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datasets:
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- squad-es
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model: Biophin/mi-modelo-qa
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example_inputs:
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context: "Al repechaje se accede cuando en la nota de un examen se obtuvo un 3."
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question: "¿Cuándo se accede al repechaje?"
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---
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## Modelo de Preguntas y Respuestas en Español
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Este modelo está diseñado para responder a preguntas basadas en un contexto proporcionado en español.
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### Configuración del Modelo
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```python
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from transformers import pipeline
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# Crear una tubería de respuesta a preguntas
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qa_pipeline = pipeline("question-answering", model="Biophin/mi-modelo-qa")
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# Definir el contexto
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context = "Al repechaje se accede cuando en la nota de un examen se obtuvo un 3."
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# Formular la pregunta
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question = "¿Cuándo se accede al repechaje?"
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# Obtener la respuesta
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result = qa_pipeline(question=question, context=context)
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# Imprimir la pregunta y la respuesta
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print(f"Pregunta: {question}")
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print(f"Respuesta: {result['answer']}")
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config.json
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{
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"_name_or_path": "distilbert-base-cased-distilled-squad",
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"activation": "gelu",
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"architectures": [
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"DistilBertForQuestionAnswering"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"output_past": true,
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"pad_token_id": 0,
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": true,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.47.1",
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"vocab_size": 28996
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}
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merges.txt
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9a22511778fce35d5e360770ef55c98c73bbf06cd464fdce907c9026173fe559
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size 260782152
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"do_lower_case": false,
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "DistilBertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.json
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vocab.txt
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