Table Question Answering
Transformers
Safetensors
Thai
English
llama
text-generation
code
openthaigpt
text-generation-inference
Instructions to use AIAT/Kiddee-qatable1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIAT/Kiddee-qatable1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="AIAT/Kiddee-qatable1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AIAT/Kiddee-qatable1") model = AutoModelForCausalLM.from_pretrained("AIAT/Kiddee-qatable1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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 | |
|  | |
| ## 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 | |
|  | |
| library_name: adapter-transformers | |
| --- |