--- 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 ``, ``, … up to ``. However, `` is separated into multiple parts in Huggingface's Hosted inference API. Therefore, we replace `` 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 --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 \ --processes_num 32 --seq_length 512 \ --dynamic_masking --target t5 ``` ``` python3 pretrain.py --dataset_path cluecorpussmall_t5_seq512_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 5e-4 --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 --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{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