Update README.md

README.md

@@ -1 +1,117 @@
-The model is coming soon.
+---
+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}
+}
+```
+