---
language: 
  - zh
license: apache-2.0
---


# Model Card for Mengzi-T5 model (Chinese)
 
# Model Details
 
## Model Description
 
More information needed  
 
- **Developed by:** Zhuosheng Zhang, Hanqing Zhang, Keming Chen3, Yuhang Guo, Jingyun Hua, Yulong Wang, Ming Zhou
- **Shared by [Optional]:** Langboat
- **Model type:** Text2text Generation
- **Language(s) (NLP):** Chinese
- **License:** Apache 2.0
- **Parent Model:** T5
- **Resources for more information:**
  - [GitHub Repo](https://github.com/Langboat/Mengzi)
   - [Associated Paper](https://arxiv.org/abs/2110.06696)
 	


# Uses
 

## Direct Use
This model can be used for the task of text2text generation.
 
## Downstream Use [Optional]
 
More information needed.
 
## Out-of-Scope Use
 
The model should not be used to intentionally create hostile or alienating environments for people. 
 
# Bias, Risks, and Limitations
 
 
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.



## Recommendations
 
 
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

# Training Details
 
## Training Data
Pretrained model on 300G Chinese corpus. 
The model authors also note in the [associated paper](https://arxiv.org/pdf/2110.06696.pdf):
> The pre-training corpus is derived from Chinese Wikipedia, Chinese News, and Common Crawl, with a 300GB data size in total.
> Our vocabulary contains 21,128 tokens. We limit the length of sentences in each batch to up to 512 tokens, and the batch size is 128. During pre-training, 15% words are randomly masked in each sequence for MLM prediction. 
 
 
## Training Procedure

 
### Preprocessing
The model authors note in the [associated paper](https://arxiv.org/pdf/2110.06696.pdf):
> We clean the data by using exploratory data analysis techniques, i.e., removing HTML tags, URLs, e-mails, emoji, etc. Since there are simplified and traditional Chinese tokens in the original corpus, we convert traditional tokens into the simplified form using OpenCC. Duplicatearticles are also removed.
 
 
 
### Speeds, Sizes, Times
The model authors note in the [associated paper](https://arxiv.org/pdf/2110.06696.pdf):
> RoBERTa (Liu et al., 2019) is leveraged as the initial backbone model for Mengzi pre-training. Our Mengzi architecture is based on the base size, where the model consists of 12 transformer layers, with the hidden size of 768, 12 attention heads, and 103M model parameters in total. 
 

 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
 
More information needed 
 
 
### Factors
More information needed
 
### Metrics
 
More information needed
 
 
## Results 
 
More information needed

 
# Model Examination
 
More information needed
 
# Environmental Impact
 
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
- **Hardware Type:** More information needed
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
 
# Technical Specifications [optional]
 
## Model Architecture and Objective

More information needed 
 
## Compute Infrastructure
 
More information needed 
 
### Hardware
 
 
More information needed
 
### Software
 
More information needed.
 
# Citation

 
**BibTeX:**
 If you find the technical report or resource is useful, please cite the following technical report in your paper.
 
```bibtex
@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}
}
```
 
 
 
 
# Glossary [optional]
More information needed 
 
# More Information [optional]
More information needed 

 
# Model Card Authors [optional]
 
 Langboat in collaboration with Ezi Ozoani and the Hugging Face team


# Model Card Contact
 
More information needed
 
# How to Get Started with the Model
 
Use the code below to get started with the model.
 
<details>
<summary> Click to expand </summary>

```python
 from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("Langboat/mengzi-t5-base")
model = T5ForConditionalGeneration.from_pretrained("Langboat/mengzi-t5-base")
 ```
</details>