README.md

---
language: Chinese
datasets: CLUECorpusSmall
widget: 
- text: "中国的首都是extra0京"



---


# Chinese T5-small Model

## Model description

The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. Based on this, we released this Chinese t5-small model. You can download the model via HuggingFace from the link [t5-small-chinese-cluecorpussmall](https://huggingface.co/uer/t5-small-chinese-cluecorpussmall).

## How to use

We provide two vocabs ( vocab.txt and google_zh_with_sentinel_vocab.txt ) for this model and use the google_zh_with_sentinel_vocab.txt to train this model. In order to use Hosted inference API, we replaced characters like [extra_id_0] in the google_zh_with_sentinel_vocab.txt with characters extra0 to prevent characters from being split .

You can use the model directly with a pipeline for text2text generation:

```python
>>> from transformers import BertTokenizer, T5ForConditionalGeneration,Text2TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("uer/t5-small-chinese-cluecorpussmall")
>>> model = T5ForConditionalGeneration.from_pretrained("uer/t5-small-chinese-cluecorpussmall")
>>> text2text_generator = Text2TextGenerationPipeline(model, tokenizer)  
>>> text2text_generator("中国的首都是extra0京", max_length=50, do_sample=False)
    
```



## Training data

[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. 

## 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 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. 

Stage1:

```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
                      --vocab_path models/google_zh_with_sentinel_vocab.txt \
                      --dataset_path cluecorpussmall_t5_seq128_dataset.pt \
                      --seq_length 128 --processes_num 32 \
                      --dynamic_masking --target t5 
```

```
python3 pretrain.py --dataset_path cluecorpussmall_t5_seq128_dataset.pt \
                    --vocab_path models/google_zh_with_sentinel_vocab.txt \
                    --output_model_path models/cluecorpussmall_t5_seq128_model.bin \
                    --config_path models/t5/small_config.json \
                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
                    --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
                    --learning_rate 1e-3 --batch_size 64 \
                    --embedding word --tgt_embedding word \
                    --remove_embedding_layernorm --relative_position_embedding \
                    --encoder transformer --decoder transformer \
                    --mask fully_visible --layernorm_positioning pre \
                    --target t5 --tie_weights \
                    --span_masking --span_max_length 5 --span_geo_prob 0.3

```

Stage2:

```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
                      --vocab_path models/google_zh_with_sentinel_vocab.txt \
                      --dataset_path cluecorpussmall_t5_seq512_dataset.pt \
                      --seq_length 512 --processes_num 32 --target t5 \
                      --dynamic_masking
```

```
python3 pretrain.py --dataset_path cluecorpussmall_t5_seq128_dataset.pt \
                    --pretrained_model_path models/cluecorpussmall_t5_seq128_model.bin-1000000 \
                    --vocab_path models/google_zh_with_sentinel_vocab.txt \
                    --output_model_path models/cluecorpussmall_t5_seq512_model.bin \
                    --config_path models/t5/small_config.json \
                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
                    --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
                    --learning_rate 1e-3 --batch_size 16 \
                    --embedding word --tgt_embedding word \
                    --remove_embedding_layernorm --relative_position_embedding \
                    --encoder transformer --decoder transformer \
                    --mask fully_visible --layernorm_positioning pre \
                    --target t5 --tie_weights \
                    --span_masking --span_max_length 5 --span_geo_prob 0.3
```

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

```
python3 scripts/convert_t5_from_uer_to_huggingface.py --input_model_path cluecorpussmall_t5_seq512_model.bin-250000 \
                                                      --output_model_path pytorch_model.bin \
                                                      --layers_num 12 \
                                                      --type t5
```

### 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}
}
```