File size: 1,540 Bytes
99907ef dcf5b2f 99907ef dcf5b2f 2cb28eb 6341760 38eaf08 6341760 2cb28eb 38eaf08 6341760 981e4f6 38eaf08 |
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 |
---
license: mit
language:
- my
pipeline_tag: text-generation
---
The Simbolo's Myanmar SAR 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
```python
!pip install transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Simbolo-Servicio/myanmar-sar-gpt")
model = AutoModelForCausalLM.from_pretrained("Simbolo-Servicio/myanmar-sar-gpt")
input_text = ""
input_ids = tokenizer.encode(input_text, return_tensors='pt')
output = model.generate(input_ids, max_length=100)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
### Data
The data utilized comprises 1 million sentences sourced from Wikipedia.
### 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 and Citations
1. 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.
2. Lin, N., Fu, Y., Chen, C., Yang, Z., & Jiang, S. (2021). LaoPLM: Pre-trained Language Models for Lao. ArXiv. /abs/2110.05896
|