
# 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.
{"language": ["bo"], "license": "mit", "tags": ["tibetan", "pretrained causal language model", "roberta"], "widget": [{"text": "\u0f62\u0f72\u0f53\u0f0b"}, {"text": "\u0f62\u0fab\u0f7c\u0f42\u0f66\u0f0b\u0f54\u0f60\u0f72\u0f0b"}, {"text": "\u0f46\u0f7c\u0f66\u0f0b\u0f40\u0fb1\u0f72\u0f0b"}, {"text": "\u0f42\u0f44\u0f66\u0f0b\u0f62\u0f72\u0f60\u0f72\u0f0b"}, {"text": "\u0f56\u0f7c\u0f51\u0f0b\u0f40\u0fb1\u0f72\u0f0b\u0f66\u0fa8\u0f53\u0f0b"}]}
transformers pytorch roberta text-generation tibetan pretrained causal language model bo license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-generation #tibetan #pretrained causal language model #bo #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# A demo for generating text using 'Tibetan Roberta Causal Language Model'



# 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.
# A demo for generating text using 'Tibetan Roberta Causal Language Model' # 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.
TAGS
#transformers #pytorch #roberta #text-generation #tibetan #pretrained causal language model #bo #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # A demo for generating text using 'Tibetan Roberta Causal Language Model' # 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.
passage: TAGS
#transformers #pytorch #roberta #text-generation #tibetan #pretrained causal language model #bo #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# A demo for generating text using 'Tibetan Roberta Causal Language Model'# 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.