metadata
language:
- bo
tags:
- tibetan
- pretrained causal language model
- roberta
widget:
- text: རིན་
- text: རྫོགས་པའི་
- text: ཆོས་ཀྱི་
- text: གངས་རིའི་
- text: བོད་ཀྱི་སྨན་
license: mit
A demo for generating text using Tibetan Roberta Causal Language Model
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name = 'sangjeedondrub/tibetan-roberta-causal-base'
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
text_gen_pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer
)
init_text = 'རིན་'
outputs = text_gen_pipe(init_text,
do_sample=True,
max_new_tokens=200,
temperature=.9,
top_k=10,
top_p=0.92,
num_return_sequences=10,
truncate=True)
for idx, output in enumerate(outputs, start=1):
print(idx)
print(output['generated_text'])
About
This model is trained and released by Sangjee Dondrub [sangjeedondrub at live dot com], the mere purpose of conducting these experiments is to improve my familiarity with Transformers APIs.