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--- |
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language: zh |
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datasets: CLUECorpusSmall |
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widget: |
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- 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。" |
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--- |
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# Chinese T5 |
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## Model description |
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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). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. |
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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. |
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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: |
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| | Link | |
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| -------- | :-----------------------: | |
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| **T5-Small** | [**L=6/H=512 (Small)**][small] | |
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| **T5-Base** | [**L=12/H=768 (Base)**][base] | |
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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. |
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## How to use |
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You can use this model directly with a pipeline for text2text generation (take the case of T5-Small): |
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```python |
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>>> from transformers import BertTokenizer, T5ForConditionalGeneration, Text2TextGenerationPipeline |
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>>> tokenizer = BertTokenizer.from_pretrained("uer/t5-small-chinese-cluecorpussmall") |
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>>> model = T5ForConditionalGeneration.from_pretrained("uer/t5-small-chinese-cluecorpussmall") |
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>>> text2text_generator = Text2TextGenerationPipeline(model, tokenizer) |
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>>> text2text_generator("中国的首都是extra0京", max_length=50, do_sample=False) |
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[{'generated_text': 'extra0 北 extra1 extra2 extra3 extra4 extra5'}] |
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``` |
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## Training data |
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[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. |
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## Training procedure |
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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. |
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Taking the case of T5-Small |
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Stage1: |
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``` |
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python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ |
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--vocab_path models/google_zh_with_sentinel_vocab.txt \ |
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--dataset_path cluecorpussmall_t5_seq128_dataset.pt \ |
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--processes_num 32 --seq_length 128 \ |
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--dynamic_masking --data_processor t5 |
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``` |
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``` |
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python3 pretrain.py --dataset_path cluecorpussmall_t5_seq128_dataset.pt \ |
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--vocab_path models/google_zh_with_sentinel_vocab.txt \ |
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--config_path models/t5/small_config.json \ |
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--output_model_path models/cluecorpussmall_t5_small_seq128_model.bin \ |
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--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ |
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--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \ |
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--learning_rate 1e-3 --batch_size 64 \ |
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--span_masking --span_geo_prob 0.3 --span_max_length 5 |
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``` |
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Stage2: |
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``` |
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python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ |
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--vocab_path models/google_zh_with_sentinel_vocab.txt \ |
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--dataset_path cluecorpussmall_t5_small_seq512_dataset.pt \ |
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--processes_num 32 --seq_length 512 \ |
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--dynamic_masking --data_processor t5 |
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``` |
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``` |
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python3 pretrain.py --dataset_path cluecorpussmall_t5_seq512_dataset.pt \ |
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--vocab_path models/google_zh_with_sentinel_vocab.txt \ |
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--pretrained_model_path models/cluecorpussmall_t5_small_seq128_model.bin-1000000 \ |
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--config_path models/t5/small_config.json \ |
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--output_model_path models/cluecorpussmall_t5_small_seq512_model.bin \ |
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--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ |
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--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \ |
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--learning_rate 5e-4 --batch_size 16 \ |
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--span_masking --span_geo_prob 0.3 --span_max_length 5 |
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``` |
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Finally, we convert the pre-trained model into Huggingface's format: |
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``` |
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python3 scripts/convert_t5_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_t5_small_seq512_model.bin-250000 \ |
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--output_model_path pytorch_model.bin \ |
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--layers_num 6 \ |
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--type t5 |
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``` |
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### BibTeX entry and citation info |
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``` |
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@article{2020t5, |
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title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, |
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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}, |
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journal = {Journal of Machine Learning Research}, |
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pages = {1-67}, |
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year = {2020} |
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} |
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@article{zhao2019uer, |
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title={UER: An Open-Source Toolkit for Pre-training Models}, |
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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}, |
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journal={EMNLP-IJCNLP 2019}, |
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pages={241}, |
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year={2019} |
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} |
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@article{zhao2023tencentpretrain, |
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title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities}, |
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author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others}, |
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journal={ACL 2023}, |
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pages={217}, |
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year={2023} |
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``` |
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[small]:https://huggingface.co/uer/t5-small-chinese-cluecorpussmall |
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[base]:https://huggingface.co/uer/t5-base-chinese-cluecorpussmall |
