|
--- |
|
language: Chinese |
|
widget: |
|
- text: "最美的不是下雨天，是曾与你躲过雨的屋檐" |
|
|
|
|
|
--- |
|
|
|
|
|
# Chinese GPT2 Lyric Model |
|
|
|
## Model description |
|
|
|
The model is used to generate Chinese lyrics. You can download the model either from the [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) |
|
|
|
## How to use |
|
|
|
You can use the model directly with a pipeline for text generation: |
|
|
|
```python |
|
>>> 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'}] |
|
``` |
|
|
|
## 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](https://github.com/liuhuanyong/MusicLyricChatbot). |
|
|
|
## Training procedure |
|
|
|
The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). 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 --target 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 \ |
|
--output_model_path models/lyric_gpt2_model.bin \ |
|
--config_path models/gpt2/config.json \ |
|
--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 \ |
|
--embedding word_pos --remove_embedding_layernorm \ |
|
--encoder transformer --mask causal --layernorm_positioning pre \ |
|
--target lm --tie_weight |
|
``` |
|
|
|
Finally, we convert the pre-trained model into Huggingface's format: |
|
|
|
``` |
|
python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path lyric_gpt2_model.bin-100000 \ |
|
--output_model_path pytorch_model.bin \ |
|
--layers_num 12 |
|
``` |
|
|
|
### BibTeX entry and citation info |
|
|
|
``` |
|
@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} |
|
} |
|
``` |
