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README.md

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+---
+language: zh
+datasets: CLUECorpusSmall
+widget: 
+- text: "作为电子extra0的平台，京东绝对是领先者。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://github.com/dbiir/UER-py/), which is introduced in [this paper](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.
+
+You can download the set of Chinese T5 models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below:
+
+|          |           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 --data_processor 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_small_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
+
+```
+
+Stage2:
+
+```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --vocab_path models/google_zh_with_sentinel_vocab.txt \
+                      --dataset_path cluecorpussmall_t5_small_seq512_dataset.pt \
+                      --processes_num 32 --seq_length 512 \
+                      --dynamic_masking --data_processor t5
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_t5_seq512_dataset.pt \
+                    --vocab_path models/google_zh_with_sentinel_vocab.txt \
+                    --pretrained_model_path models/cluecorpussmall_t5_small_seq128_model.bin-1000000 \
+                    --config_path models/t5/small_config.json \
+                    --output_model_path models/cluecorpussmall_t5_small_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 5e-4 --batch_size 16 \
+                    --span_masking --span_geo_prob 0.3 --span_max_length 5
+```
+
+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_small_seq512_model.bin-250000 \
+                                                      --output_model_path pytorch_model.bin \
+                                                      --layers_num 6 \
+                                                      --type t5
+```
+
+
+### BibTeX entry and citation info
+
+```
+@article{2020t5,
+  title   = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
+  author  = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
+  journal = {Journal of Machine Learning Research},
+  pages   = {1-67},
+  year    = {2020}
+}
+
+@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

config.json

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 {
-"architectures": [
+  "architectures": [
     "T5ForConditionalGeneration"
   ],
   "d_ff": 2048,
   "d_kv": 64,
   "d_model": 512,
   "decoder_start_token_id": 101,
-  "dense_act_fn": "relu",
   "dropout_rate": 0.1,
-  "eos_token_id": 1,
-  "feed_forward_proj": "relu",
   "initializer_factor": 1.0,
   "is_encoder_decoder": true,
-  "is_gated_act": false,
   "layer_norm_epsilon": 1e-06,
   "model_type": "t5",
   "n_positions": 512,
-  "num_decoder_layers": 6,
   "num_heads": 8,
   "num_layers": 6,
   "pad_token_id": 0,
-  "relative_attention_max_distance": 128,
   "relative_attention_num_buckets": 32,
   "tokenizer_class": "BertTokenizer",
-  "transformers_version": "4.26.0",
-  "use_cache": true,
   "vocab_size": 21228
 }

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