
# GPT-2

GPT-2, a language pretrained model with a causal language modeling (CLM) goal, is a transformer-based language model. 
This model was pre-trained and used to generate text on the Vietnamese Wikilingua dataset.

# How to use the model

~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained('minhtoan/vietnamese-gpt2-finetune')
model = GPT2LMHeadModel.from_pretrained('minhtoan/vietnamese-gpt2-finetune')

text = "Không phải tất cả các nguyên liệu lành mạnh đều đắt đỏ."
input_ids = tokenizer.encode(text, return_tensors='pt')
max_length = 100

sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   num_return_sequences=3)

for i, sample_output in enumerate(sample_outputs):
    print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist())))
    print('\n---')
~~~~


## Author
`
Phan Minh Toan 
`
{"language": "vi", "tags": ["vi", "vietnamese", "gpt2", "text-generation", "lm", "nlp"], "datasets": ["wikilinguage"], "widget": [{"text": "Kh\u00f4ng ph\u1ea3i t\u1ea5t c\u1ea3 c\u00e1c nguy\u00ean li\u1ec7u l\u00e0nh m\u1ea1nh \u0111\u1ec1u \u0111\u1eaft \u0111\u1ecf."}], "pipeline_tag": "text-generation", "inference": {"parameters": {"max_length": 120, "do_sample": true, "temperature": 0.8}}}
transformers pytorch gpt2 text-generation vi vietnamese lm nlp dataset:wikilinguage autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #vi #vietnamese #lm #nlp #dataset-wikilinguage #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# GPT-2

GPT-2, a language pretrained model with a causal language modeling (CLM) goal, is a transformer-based language model. 
This model was pre-trained and used to generate text on the Vietnamese Wikilingua dataset.

# How to use the model

~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained('minhtoan/vietnamese-gpt2-finetune')
model = GPT2LMHeadModel.from_pretrained('minhtoan/vietnamese-gpt2-finetune')

text = "Không phải tất cả các nguyên liệu lành mạnh đều đắt đỏ."
input_ids = URL(text, return_tensors='pt')
max_length = 100

sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   num_return_sequences=3)

for i, sample_output in enumerate(sample_outputs):
    print(">> Generated text {}\n\n{}".format(i+1, URL(sample_output.tolist())))
    print('\n---')
~~~~


## Author
'
Phan Minh Toan 
'
# GPT-2

GPT-2, a language pretrained model with a causal language modeling (CLM) goal, is a transformer-based language model. 
This model was pre-trained and used to generate text on the Vietnamese Wikilingua dataset. # How to use the model

~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained('minhtoan/vietnamese-gpt2-finetune')
model = GPT2LMHeadModel.from_pretrained('minhtoan/vietnamese-gpt2-finetune')

text = "Không phải tất cả các nguyên liệu lành mạnh đều đắt đỏ."
input_ids = URL(text, return_tensors='pt')
max_length = 100

sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   num_return_sequences=3)

for i, sample_output in enumerate(sample_outputs):
    print(">> Generated text {}\n\n{}".format(i+1, URL(sample_output.tolist())))
    print('\n---')
~~~~ ## Author
'
Phan Minh Toan 
'
TAGS
#transformers #pytorch #gpt2 #text-generation #vi #vietnamese #lm #nlp #dataset-wikilinguage #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # GPT-2

GPT-2, a language pretrained model with a causal language modeling (CLM) goal, is a transformer-based language model. 
This model was pre-trained and used to generate text on the Vietnamese Wikilingua dataset. # How to use the model

~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained('minhtoan/vietnamese-gpt2-finetune')
model = GPT2LMHeadModel.from_pretrained('minhtoan/vietnamese-gpt2-finetune')

text = "Không phải tất cả các nguyên liệu lành mạnh đều đắt đỏ."
input_ids = URL(text, return_tensors='pt')
max_length = 100

sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   num_return_sequences=3)

for i, sample_output in enumerate(sample_outputs):
    print(">> Generated text {}\n\n{}".format(i+1, URL(sample_output.tolist())))
    print('\n---')
~~~~ ## Author
'
Phan Minh Toan 
'
TAGS
#transformers #pytorch #gpt2 #text-generation #vi #vietnamese #lm #nlp #dataset-wikilinguage #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# GPT-2

GPT-2, a language pretrained model with a causal language modeling (CLM) goal, is a transformer-based language model. 
This model was pre-trained and used to generate text on the Vietnamese Wikilingua dataset.# How to use the model

~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained('minhtoan/vietnamese-gpt2-finetune')
model = GPT2LMHeadModel.from_pretrained('minhtoan/vietnamese-gpt2-finetune')

text = "Không phải tất cả các nguyên liệu lành mạnh đều đắt đỏ."
input_ids = URL(text, return_tensors='pt')
max_length = 100

sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   num_return_sequences=3)

for i, sample_output in enumerate(sample_outputs):
    print(">> Generated text {}\n\n{}".format(i+1, URL(sample_output.tolist())))
    print('\n---')
~~~~## Author
'
Phan Minh Toan 
'