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README.md

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+---
+language: Chinese
+datasets: CLUECorpusSmall
+widget: 
+- text: "作为电子为主的电商平台，京东绝对是extra0者。如今的刘强extra1已经是身价过extra2的老板。"
+
+
+---
+
+# Chinese T5
+
+## Model description
+
+This is the set of Chinese T5 models pre-trained by [UER-py](https://arxiv.org/abs/1909.05658).
+
+The Text-to-Text Transfer Transformer (T5) leverages a unified text-to-text format and attains state-of-the-art results on a wide variety of English-language NLP tasks. Following their work, we released a series of Chinese T5 models.
+
+|          |           Link           |
+| -------- | :-----------------------: |
+| **T5-Small**  | [**L=6/H=512 (Small)**][small] |
+| **T5-Base**  | [**L=12/H=768 (Base)**][base] |
+
+In T5, spans of the input sequence are masked by so-called sentinel token. Each sentinel token represents a unique mask token for the input sequence and should start with `<extra_id_0>`, `<extra_id_1>`, … up to `<extra_id_99>`. However, `<extra_id_xxx>` is separated into multiple parts in Huggingface's Hosted inference API. Therefore, we replace `<extra_id_xxx>` with `extraxxx` in vocabulary and BertTokenizer regards `extraxxx` as one sentinel token.
+
+## How to use
+
+You can use this model directly with a pipeline for text2text generation (take the case of T5-Small):
+
+```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)
+    [{'generated_text': 'extra0 北 extra1 extra2 extra3 extra4 extra5'}]
+```
+
+## 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](https://cloud.tencent.com/). 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. We use the same hyper-parameters on different model sizes.
+
+Taking the case of T5-Small
+
+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 \
+                      --processes_num 32 --seq_length 128 \
+                      --dynamic_masking --target t5 
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_t5_seq128_dataset.pt \
+                    --vocab_path models/google_zh_with_sentinel_vocab.txt \
+                    --config_path models/t5/small_config.json \
+                    --output_model_path models/cluecorpussmall_t5_seq128_model.bin \
+                    --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 \
+                    --span_masking --span_geo_prob 0.3 --span_max_length 5 \
+                    --embedding word --relative_position_embedding --remove_embedding_layernorm --tgt_embedding word \
+                    --encoder transformer --mask fully_visible --layernorm_positioning pre\
+                    --remove_transformer_bias --decoder transformer \
+                    --target t5 --tie_weights
+
+```
+
+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 \
+                    --config_path models/t5/small_config.json \
+                    --output_model_path models/cluecorpussmall_t5_seq512_model.bin \
+                    --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 \
+                    --span_masking --span_geo_prob 0.3 --span_max_length 5 \
+                    --embedding word --relative_position_embedding --remove_embedding_layernorm --tgt_embedding word \
+                    --encoder transformer --mask fully_visible --layernorm_positioning pre\
+                    --remove_transformer_bias --decoder transformer \
+                    --target t5 --tie_weights
+```
+
+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 6 \
+                                                      --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}
+}
+```
+
+[small]:https://huggingface.co/uer/t5-small-chinese-cluecorpussmall
+[base]:https://huggingface.co/uer/t5-base-chinese-cluecorpussmall