
# Mengzi-T5-MT model
This is a Multi-Task model trained on the multitask mixture of 27 datasets and 301 prompts, based on [Mengzi-T5-base](https://huggingface.co/Langboat/mengzi-t5-base).

[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)

## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("Langboat/mengzi-t5-base-mt")
model = T5ForConditionalGeneration.from_pretrained("Langboat/mengzi-t5-base-mt")
```

## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
```
@misc{zhang2021mengzi,
      title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese}, 
      author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo and Jingyun Hua and Yulong Wang and Ming Zhou},
      year={2021},
      eprint={2110.06696},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": ["zh"], "license": "apache-2.0", "widget": [{"text": "\u201c\u623f\u95f4\u5f88\u4e00\u822c\uff0c\u5c0f\uff0c\u4e14\u8ba9\u4eba\u611f\u89c9\u810f\uff0c\u9694\u97f3\u6548\u679c\u5dee\uff0c\u80fd\u542c\u5230\u8d70\u5eca\u7684\u4eba\u8bb2\u8bdd\uff0c\u8d70\u5eca\u5149\u7ebf\u660f\u6697\uff0c\u65c1\u8fb9\u6ca1\u6709\u4ec0\u4e48\u53ef\u5403\u201d \u8fd9\u6761\u8bc4\u8bba\u7684\u6001\u5ea6\u662f\u4ec0\u4e48\uff1f"}]}
transformers pytorch t5 text2text-generation zh arxiv:2110.06696 doi:10.57967/hf/0026 license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #zh #arxiv-2110.06696 #doi-10.57967/hf/0026 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# Mengzi-T5-MT model
This is a Multi-Task model trained on the multitask mixture of 27 datasets and 301 prompts, based on Mengzi-T5-base.

Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese

## Usage


If you find the technical report or resource is useful, please cite the following technical report in your paper.

# Mengzi-T5-MT model
This is a Multi-Task model trained on the multitask mixture of 27 datasets and 301 prompts, based on Mengzi-T5-base.

Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese ## Usage


If you find the technical report or resource is useful, please cite the following technical report in your paper.
TAGS
#transformers #pytorch #t5 #text2text-generation #zh #arxiv-2110.06696 #doi-10.57967/hf/0026 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # Mengzi-T5-MT model
This is a Multi-Task model trained on the multitask mixture of 27 datasets and 301 prompts, based on Mengzi-T5-base.

Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese ## Usage


If you find the technical report or resource is useful, please cite the following technical report in your paper.
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #zh #arxiv-2110.06696 #doi-10.57967/hf/0026 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# Mengzi-T5-MT model
This is a Multi-Task model trained on the multitask mixture of 27 datasets and 301 prompts, based on Mengzi-T5-base.

Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese## Usage


If you find the technical report or resource is useful, please cite the following technical report in your paper.