metadata
license: apache-2.0
datasets:
- AIAT/Kiddee-data1234
language:
- th
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: table-question-answering
tags:
- code
license: apache-2.0
Datasets:
- AIAT/Kiddee-data1234
- (https://huggingface.co/datasets/AIAT/Kiddee-data1234)
language:
- th
- en
metrics:
- accuracy 0.53
- response time 2.440
pipeline_tag:
- table-question-answering
tags:
- OpenthaiGPT-13b
- LLMModel
KIDDEE STRONG MUSCLE LLM
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).
Introduction
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.
Features
- RAG architecture: Integration of retrieval and generation models.
- LLM: Powerful language generation capabilities.
- Question Answering: Ability to answer questions based on given contexts.
- Scalable: Easily scalable for large datasets and complex questions.
- Diverse Responses: Provides diverse responses for a given question through generation.
Setup
- Clone this repository:
