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--- |
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license: apache-2.0 |
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datasets: |
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- AIAT/Kiddee-data1234 |
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language: |
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- th |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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pipeline_tag: table-question-answering |
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tags: |
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- code |
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--- |
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--- |
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## license: apache-2.0 |
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![KIDDEE](https://media.discordapp.net/attachments/1226897965927497818/1235837202945151016/KIDDEE-Logoo.png?ex=6635d295&is=66348115&hm=8ea3f9706dcdc7b459919d03d5bdb59c06912425efcff8f3979efa93c9e7549e&=&format=webp&quality=lossless&width=437&height=437) |
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## Datasets: |
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- AIAT/Kiddee-data1234 |
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- (https://huggingface.co/datasets/AIAT/Kiddee-data1234) |
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## language: |
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- th |
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- en |
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## metrics: |
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- accuracy 0.53 |
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- response time 2.440 |
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## pipeline_tag: |
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- table-question-answering |
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## tags: |
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- OpenthaiGPT-13b |
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- LLMModel |
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# KIDDEE STRONG MUSCLE LLM |
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This repository contains code and resources for building a Question Answering (QA) system using the Retrieval-Augmented Generation (RAG) approach with the Language Learning Model (LLM). |
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## Introduction |
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RAG-QA combines the power of retrieval-based models with generative models to provide accurate and diverse answers to a given question. LLM, a state-of-the-art language model, is used for generation within the RAG framework. |
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## Features |
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- **RAG architecture**: Integration of retrieval and generation models. |
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- **LLM**: Powerful language generation capabilities. |
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- **Question Answering**: Ability to answer questions based on given contexts. |
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- **Scalable**: Easily scalable for large datasets and complex questions. |
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- **Diverse Responses**: Provides diverse responses for a given question through generation. |
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## Setup |
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1. Clone this repository: |
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# I'm not going to tell you |
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# sponser |
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![image/png](https://media.discordapp.net/attachments/1226897965927497818/1235842881520930857/image.png?ex=6635d7df&is=6634865f&hm=be4eb57b51de9f52f0817a88fdd2461b5312d0a013bd022630b2a8dde717976f&=&format=webp&quality=lossless&width=687&height=402) |
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library_name: adapter-transformers |
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--- |