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README.md
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---
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language:
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- es
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base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
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pipeline_tag: text-classification
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tags:
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- sentiment-analysis
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- text-classification
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- spanish
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- xlm-roberta
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widget:
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- text: "Me encanta este producto, es excelente!"
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example_title: "Ejemplo positivo"
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example_title: "Ejemplo neutro"
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- text: "Este servicio es terrible, nunca lo recomendaría."
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example_title: "Ejemplo negativo"
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---
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# BERT Bregman - Modelo de Análisis de Sentimientos en Español
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---
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language: es
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tags:
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- sentiment-analysis
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- text-classification
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- spanish
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- xlm-roberta
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license: mit
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datasets:
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- custom
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metrics:
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- accuracy
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- f1
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library_name: transformers
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pipeline_tag: text-classification
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widget:
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- text: "Me encanta este producto, es excelente!"
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example_title: "Ejemplo positivo"
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example_title: "Ejemplo neutro"
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- text: "Este servicio es terrible, nunca lo recomendaría."
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example_title: "Ejemplo negativo"
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model-index:
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- name: bert-bregman
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results:
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- task:
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type: text-classification
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name: Sentiment Analysis
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dataset:
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name: Custom Spanish Sentiment Dataset
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type: custom
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metrics:
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- type: accuracy
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value: 0.7432432432432432
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- type: f1
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value: 0.7330748170322471
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architectures:
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- XLMRobertaForSequenceClassification
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transformers_version: "4.41.2"
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base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
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inference:
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parameters:
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temperature: 1.0
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max_length: 512
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num_return_sequences: 1
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---
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# BERT Bregman - Modelo de Análisis de Sentimientos en Español
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