File size: 3,303 Bytes
b572e92 b78ef6e 82f9aa4 b78ef6e 17a180a b78ef6e 17a180a b78ef6e 17a180a b78ef6e 17a180a b78ef6e 17a180a b78ef6e 17a180a b78ef6e f3127fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
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
>>> from transformers import 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
>>> from transformers import 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
Contains 700,000 Chinese couplets 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.pt --processes_num 16 \
--seq_length 64 --target lm
```
```
python3 pretrain.py --dataset_path couplet.pt \
--vocab_path models/google_zh_vocab.txt \
--output_model_path models/couplet_gpt_base_model.bin \
--config_path models/bert_base_config.json --learning_rate 5e-4 \
--tie_weight --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--batch_size 64 --report_steps 1000 \
--save_checkpoint_steps 5000 --total_steps 25000 \
--embedding gpt --encoder gpt2 --target lm
```
### 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
|