|
--- |
|
language: zh |
|
widget: |
|
- text: "[CLS]国 色 天 香 , 姹 紫 嫣 红 , 碧 水 青 云 欣 共 赏 -" |
|
|
|
--- |
|
|
|
# Chinese Couplet GPT2 Model |
|
|
|
## Model description |
|
|
|
The model is used to generate Chinese couplets. 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-couplet][couplet]. |
|
|
|
Since the parameter skip_special_tokens is used in the pipelines.py, special tokens such as [SEP], [UNK] will be deleted, and the output results may not be neat. |
|
|
|
## How to use |
|
|
|
You can use the model directly with a pipeline for text generation: |
|
|
|
When the parameter skip_special_tokens is True: |
|
|
|
```python |
|
>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline |
|
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-couplet") |
|
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-couplet") |
|
>>> text_generator = TextGenerationPipeline(model, tokenizer) |
|
>>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True) |
|
[{'generated_text': '[CLS]丹 枫 江 冷 人 初 去 - 黄 叶 声 从 天 外 来 阅 旗'}] |
|
``` |
|
|
|
When the parameter skip_special_tokens is False: |
|
|
|
```python |
|
>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline |
|
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-poem") |
|
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem") |
|
>>> text_generator = TextGenerationPipeline(model, tokenizer) |
|
>>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True) |
|
[{'generated_text': '[CLS]丹 枫 江 冷 人 初 去 - 黄 叶 声 我 酒 不 辞 [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP]'}] |
|
``` |
|
|
|
## Training data |
|
|
|
Training data contains 700,000 Chinese couplets which are collected by [couplet-clean-dataset](https://github.com/v-zich/couplet-clean-dataset). |
|
|
|
## Training procedure |
|
|
|
Models are 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 25,000 steps with a sequence length of 64. |
|
|
|
``` |
|
python3 preprocess.py --corpus_path corpora/couplet.txt \ |
|
--vocab_path models/google_zh_vocab.txt \ |
|
--dataset_path couplet_dataset.pt --processes_num 16 \ |
|
--seq_length 64 --target lm |
|
``` |
|
|
|
``` |
|
python3 pretrain.py --dataset_path couplet_dataset.pt \ |
|
--vocab_path models/google_zh_vocab.txt \ |
|
--output_model_path models/couplet_gpt2_base_model.bin \ |
|
--config_path models/bert_base_config.json \ |
|
--tie_weight --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ |
|
--total_steps 25000 --save_checkpoint_steps 5000 --report_steps 1000 \ |
|
--learning_rate 5e-4 --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 couplet_gpt2_base_model.bin-25000 \ |
|
--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} |
|
} |
|
``` |
|
|
|
[couplet]: https://huggingface.co/uer/gpt2-chinese-couplet |
|
|
|
|