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.
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) |
| T5-Base | L=12/H=768 (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):
>>> 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 is used as training data.
Training procedure
The model is pre-trained by UER-py on Tencent Cloud. 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 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 --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}
}