Gpt2 Chinese Lyric

🚀 Chinese GPT2 Lyric Model

This model is designed to generate Chinese lyrics, leveraging pre - training techniques to offer high - quality lyric generation capabilities.

🚀 Quick Start

You can use the model directly with a pipeline for text generation:

>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-lyric")
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-lyric")
>>> text_generator = TextGenerationPipeline(model, tokenizer)   
>>> text_generator("最美的不是下雨天，是曾与你躲过雨的屋檐", max_length=100, do_sample=True)
    [{'generated_text': '最美的不是下雨天，是曾与你躲过雨的屋檐 ， 下 课 铃 声 响 起 的 瞬 间 ， 我 们 的 笑 脸 ， 有 太 多 回 忆 在 浮 现 ， 是 你 总 在 我 身 边 ， 不 知 道 会 不 会 再 见 ， 从 现 在 开 始 到 永 远 ， 想 说 的 语 言 凝 结 成 一 句 ， 不 管 我 们 是 否 能 够 兑 现 ， 想 说 的 语 言 凝 结'}]

✨ Features

• The model is pre - trained by [UER - py](https://github.com/dbiir/UER - py/), which is introduced in this paper. It can also be pre - trained by TencentPretrain introduced in this paper. TencentPretrain inherits UER - py to support models with parameters above one billion and extends it to a multimodal pre - training framework.
• It can generate Chinese lyrics. You can download the model from the [UER - py Modelzoo page](https://github.com/dbiir/UER - py/wiki/Modelzoo), or [GPT2 - Chinese Github page](https://github.com/Morizeyao/GPT2 - Chinese), or via HuggingFace from the link [gpt2 - chinese - lyric](https://huggingface.co/uer/gpt2 - chinese - lyric).

📦 Installation

There is no specific installation content in the original text, so this section is skipped.

💻 Usage Examples

Basic Usage

>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-lyric")
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-lyric")
>>> text_generator = TextGenerationPipeline(model, tokenizer)   
>>> text_generator("最美的不是下雨天，是曾与你躲过雨的屋檐", max_length=100, do_sample=True)
    [{'generated_text': '最美的不是下雨天，是曾与你躲过雨的屋檐 ， 下 课 铃 声 响 起 的 瞬 间 ， 我 们 的 笑 脸 ， 有 太 多 回 忆 在 浮 现 ， 是 你 总 在 我 身 边 ， 不 知 道 会 不 会 再 见 ， 从 现 在 开 始 到 永 远 ， 想 说 的 语 言 凝 结 成 一 句 ， 不 管 我 们 是 否 能 够 兑 现 ， 想 说 的 语 言 凝 结'}]

📚 Documentation

Training data

Training data contains 150,000 Chinese lyrics which are collected by [Chinese - Lyric - Corpus](https://github.com/gaussic/Chinese - Lyric - Corpus) and MusicLyricChatbot.

Training procedure

The model is pre - trained by [UER - py](https://github.com/dbiir/UER - py/) on Tencent Cloud. We pre - train 100,000 steps with a sequence length of 512 on the basis of the pre - trained model [gpt2 - base - chinese - cluecorpussmall](https://huggingface.co/uer/gpt2 - base - chinese - cluecorpussmall)

python3 preprocess.py --corpus_path corpora/lyric.txt \
                      --vocab_path models/google_zh_vocab.txt \
                      --dataset_path lyric_dataset.pt --processes_num 32 \
                      --seq_length 512 --data_processor lm

python3 pretrain.py --dataset_path lyric_dataset.pt \
                    --pretrained_model_path models/cluecorpussmall_gpt2_seq1024_model.bin-250000 \
                    --vocab_path models/google_zh_vocab.txt \
                    --config_path models/gpt2/config.json \
                    --output_model_path models/lyric_gpt2_model.bin \
                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
                    --total_steps 100000 --save_checkpoint_steps 10000 --report_steps 5000 \
                    --learning_rate 5e-5 --batch_size 64

Finally, we convert the pre - trained model into Huggingface's format:

python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path models/lyric_gpt2_model.bin-100000 \
                                                        --output_model_path pytorch_model.bin \
                                                        --layers_num 12

BibTeX entry and citation info

@article{radford2019language,
  title={Language Models are Unsupervised Multitask Learners},
  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
  year={2019}
}

@article{zhao2019uer,
  title={UER: An Open - Source Toolkit for Pre - training Models},
  author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
  journal={EMNLP - IJCNLP 2019},
  pages={241},
  year={2019}
}

@article{zhao2023tencentpretrain,
  title={TencentPretrain: A Scalable and Flexible Toolkit for Pre - training Models of Different Modalities},
  author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
  journal={ACL 2023},
  pages={217},
  year={2023}
}

🔧 Technical Details

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📄 License

There is no license information in the original text, so this section is skipped.