inference:
  parameters:
    max_length: 500
    do_sample: True
    temperature: 0.8

# GPT-2

The GPT2 model is pre-trained on the writing style of Vu Trong Phung

# How to use the model

~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("Khoa/VN-Literature-Generation")
model = GPT2LMHeadModel.from_pretrained("Khoa/VN-Literature-Generation")


text = "Mùa thu lá vàng rơi"
input_ids = tokenizer.encode(text, return_tensors='pt')
max_length = 300
model.to('cpu')
sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   top_k=40,
                                   num_beams=5,
                                   early_stopping=True,
                                   no_repeat_ngram_size=2,
                                   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
`
Dong Dang Khoa
`
{"language": "vi", "tags": ["vi", "vietnamese", "gpt2", "text-generation", "lm", "nlp"], "datasets": ["VN-Literature"], "widget": [{"text": "H\u00f4m \u1ea5y, c\u1ee5 B\u00e1 \u00f4ng qu\u1ea3 quy\u1ebft m\u1edf v\u00ed ti\u1ec1n \u0111\u1ec3 tr\u1ea3 cho anh l\u00e1i ch\u00f3 c\u00e1i gi\u1ea5y b\u1ea1c m\u1ed9t \u0111\u1ed3ng."}]}
transformers pytorch gpt2 text-generation vi vietnamese lm nlp dataset:VN-Literature autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #vi #vietnamese #lm #nlp #dataset-VN-Literature #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

inference:
  parameters:
    max_length: 500
    do_sample: True
    temperature: 0.8

# GPT-2

The GPT2 model is pre-trained on the writing style of Vu Trong Phung

# How to use the model

~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("Khoa/VN-Literature-Generation")
model = GPT2LMHeadModel.from_pretrained("Khoa/VN-Literature-Generation")


text = "Mùa thu lá vàng rơi"
input_ids = URL(text, return_tensors='pt')
max_length = 300
URL('cpu')
sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   top_k=40,
                                   num_beams=5,
                                   early_stopping=True,
                                   no_repeat_ngram_size=2,
                                   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
'
Dong Dang Khoa
'
# GPT-2

The GPT2 model is pre-trained on the writing style of Vu Trong Phung # How to use the model

~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("Khoa/VN-Literature-Generation")
model = GPT2LMHeadModel.from_pretrained("Khoa/VN-Literature-Generation")


text = "Mùa thu lá vàng rơi"
input_ids = URL(text, return_tensors='pt')
max_length = 300
URL('cpu')
sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   top_k=40,
                                   num_beams=5,
                                   early_stopping=True,
                                   no_repeat_ngram_size=2,
                                   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
'
Dong Dang Khoa
'
TAGS
#transformers #pytorch #gpt2 #text-generation #vi #vietnamese #lm #nlp #dataset-VN-Literature #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # GPT-2

The GPT2 model is pre-trained on the writing style of Vu Trong Phung # How to use the model

~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("Khoa/VN-Literature-Generation")
model = GPT2LMHeadModel.from_pretrained("Khoa/VN-Literature-Generation")


text = "Mùa thu lá vàng rơi"
input_ids = URL(text, return_tensors='pt')
max_length = 300
URL('cpu')
sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   top_k=40,
                                   num_beams=5,
                                   early_stopping=True,
                                   no_repeat_ngram_size=2,
                                   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
'
Dong Dang Khoa
'
TAGS
#transformers #pytorch #gpt2 #text-generation #vi #vietnamese #lm #nlp #dataset-VN-Literature #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# GPT-2

The GPT2 model is pre-trained on the writing style of Vu Trong Phung# How to use the model

~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("Khoa/VN-Literature-Generation")
model = GPT2LMHeadModel.from_pretrained("Khoa/VN-Literature-Generation")


text = "Mùa thu lá vàng rơi"
input_ids = URL(text, return_tensors='pt')
max_length = 300
URL('cpu')
sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   top_k=40,
                                   num_beams=5,
                                   early_stopping=True,
                                   no_repeat_ngram_size=2,
                                   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
'
Dong Dang Khoa
'