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@@ -21,12 +21,10 @@ To address this issue, we are currently working on optimizing ways to integrate
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  This model is an open-source chat model fine-tuned with [LoRA](https://github.com/microsoft/LoRA) inspired by [Baize project](https://github.com/project-baize/baize-chatbot/tree/main/). It was trained with the Baize datasets and the ask2democracy-cfqa-salud-pension dataset, wich contains almost 4k instructions to answers questions based on a context relevant to citizen concerns and public debate in spanish.
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- Two model variations was trained during the Hackathon Somos NLP 2023:
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- - A conversational style focused model
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- - A generative context focused model
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- This model variation is focused in a more conversational way of asking questions. See Pre-proccessing dataset section.
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- There is other model variation more focused on source based augmented retrieval generation [Baizemocracy-RAGfocused](https://huggingface.co/hackathon-somos-nlp-2023/baizemocracy-lora-7B-cfqa).
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  Testing is a work in progress, we decide to share both model variations with community in order to invovle more people experimenting what it works better and find other possible use cases.
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  This model is an open-source chat model fine-tuned with [LoRA](https://github.com/microsoft/LoRA) inspired by [Baize project](https://github.com/project-baize/baize-chatbot/tree/main/). It was trained with the Baize datasets and the ask2democracy-cfqa-salud-pension dataset, wich contains almost 4k instructions to answers questions based on a context relevant to citizen concerns and public debate in spanish.
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+ - Two model variations was trained during the Hackathon Somos NLP 2023:
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+ - A conversational style focused model: focused in a more conversational way of asking questions, dee Pre-proccessing dataset section.
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+ - A generative context focused model: This model variation is more focused on source based augmented retrieval generation [Baizemocracy-RAGfocused](https://huggingface.co/hackathon-somos-nlp-2023/baizemocracy-lora-7B-cfqa).
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  Testing is a work in progress, we decide to share both model variations with community in order to invovle more people experimenting what it works better and find other possible use cases.
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