SuperAi: Thai Table Question Answering Hackathon models
Collection
Models used in final submission model in Super AI Engineer Thai Table Question Answering Hackathon • 7 items • Updated
Table Question Answering
Transformers
Safetensors
Thai
English
llama
text-generation
code
openthaigpt
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("AIAT/Kiddee-qatable1")
model = AutoModelForCausalLM.from_pretrained("AIAT/Kiddee-qatable1")Quick Links
license: apache-2.0
Tag
- openthaigpt/openthaigpt-1.0.0-13b-chat
Datasets:
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.
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library_name: adapter-transformers
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="AIAT/Kiddee-qatable1")