license: mit
language:
- my
pipeline_tag: text-generation
metrics:
- code_eval
library_name: transformers
tags:
- burmese
- gpt2
- pre-trained
The Simbolo's Myanmarsar-GPT symbol is trained on a dataset of 1 million Burmese data and pre-trained using the GPT-2 architecture. Its purpose is to serve as a foundational pre-trained model for the Burmese language, facilitating fine-tuning for specific applications of different tasks such as creative writing, chatbot, machine translation etc.
How to use
!pip install transformers
from transformers import pipeline
pipe = pipeline('text-generation',model='Simbolo-Servicio/myanmar-burmese-gpt', tokenizer='Simbolo-Servicio/myanmar-burmese-gpt',config={'max_length':500})
pipe('မြန်မာဘာသာစကား')
#
Data
The data utilized comprises 1 million sentences sourced from Wikipedia.
Contributors
Main Contributor: Sa Phyo Thu Htet (https://github.com/SaPhyoThuHtet) Wikipedia Data Crawling: Kaung Kaung Ko Ko, Phuu Pwint Thinzar Kyaing Releasing the Model: Eithandaraung, Ye Yint Htut, Thet Chit Su, Naing Phyo Aung
Limitations and bias
We have yet to thoroughly investigate the potential bias inherent in this model. Regarding transparency, it's important to note that the model is primarily trained on data from the Unicode Burmese(Myanmar) language.
References
- Jiang, Shengyi & Huang, Xiuwen & Cai, Xiaonan & Lin, Nankai. (2021). Pre-trained Models and Evaluation Data for the Myanmar Language. 10.1007/978-3-030-92310-5_52.
- Lin, N., Fu, Y., Chen, C., Yang, Z., & Jiang, S. (2021). LaoPLM: Pre-trained Language Models for Lao. ArXiv. /abs/2110.05896
- MinSithu, MyanmarGPT, https://huggingface.co/jojo-ai-mst/MyanmarGPT, 1.1-SweptWood
- Dr. Wai Yan Nyein Naing, WYNN747/Burmese-GPT, https://huggingface.co/WYNN747/Burmese-GPT
- Sai Htaung Kham,saihtaungkham/BurmeseRoBERTaCLM