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cambridgeltl/
simctg_lccc_dialogue

Name: simctg_lccc_dialogue
Availability: InStock

text-generation-inference

endpoints_compatible

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This model provides a Chinese GPT-2 language model trained with SimCTG on the LCCC benchmark (Wang et al., 2020) based on our paper A Contrastive Framework for Neural Text Generation.

We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our project repo. In the following, we illustrate a brief tutorial on how to use our approach to perform text generation.

1. Installation of SimCTG:

pip install simctg --upgrade

2. Initialize SimCTG Model:

python

import torch
# load SimCTG language model
from simctg.simctggpt import SimCTGGPT
model_name = r'cambridgeltl/simctg_lccc_dialogue'
model = SimCTGGPT(model_name)
model.eval()
tokenizer = model.tokenizer
eos_token = '[SEP]'
eos_token_id = tokenizer.convert_tokens_to_ids([eos_token])[0]

3. Prepare the Text Prefix:

python

context_list = ['刺猬很可爱！以前别人送了只没养，味儿太大！', '是很可爱但是非常臭', '是啊，没办法养', '那个怎么养哦不会扎手吗']
prefix_text = eos_token.join(context_list).strip(eos_token) + eos_token
print ('Prefix is: {}'.format(prefix_text))
tokens = tokenizer.tokenize(prefix_text)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = torch.LongTensor(input_ids).view(1,-1)

4. Generate Text with Contrastive Search:

python

beam_width, alpha, decoding_len = 5, 0.6, 64
output = model.fast_contrastive_search(input_ids=input_ids, beam_width=beam_width, alpha=alpha, 
                                       decoding_len=decoding_len, end_of_sequence_token_id=eos_token_id,
                                       early_stop=True)  
                                           
print("Output:\n" + 100 * '-')
print(''.join(tokenizer.decode(output)))
'''
  Prefix is: 刺猬很可爱！以前别人送了只没养，味儿太大！[SEP]是很可爱但是非常臭[SEP]是啊，没办法养[SEP]那个怎么养哦不会扎手吗[SEP]
  Output:
  ----------------------------------------------------------------------------------------------------
  刺猬很可爱！以前别人送了只没养，味儿太大！[SEP]是很可爱但是非常臭[SEP]是啊，没办法养[SEP]那个怎么养哦不会扎手吗[SEP]我觉得还好，就是有点臭
'''

For more details of our work, please refer to our main project repo.

5. Citation:

If you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!

bibtex

@article{su2022contrastive,
  title={A Contrastive Framework for Neural Text Generation},
  author={Su, Yixuan and Lan, Tian and Wang, Yan and Yogatama, Dani and Kong, Lingpeng and Collier, Nigel},
  journal={arXiv preprint arXiv:2202.06417},
  year={2022}
}