File size: 1,507 Bytes
043067a fe74baa 043067a c431173 c77a7c6 49de547 77774bf bd0cdd9 49de547 c431173 84d6333 c431173 a6fae2a 13a0ef6 a6fae2a 13a0ef6 525e4de c431173 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
---
license: apache-2.0
datasets:
- AIAT/Kiddee-data1234
language:
- th
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: table-question-answering
tags:
- code
- openthaigpt
---
---
## license: apache-2.0
![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)
## Tag
- openthaigpt/openthaigpt-1.0.0-13b-chat
## Datasets:
- (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
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.
# sponser
![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)
library_name: adapter-transformers
--- |