---
license: cc-by-nc-sa-4.0
language:
- zh
- ja
- en

tags:
- translation

widget:
- text: "ja2zh: 吾輩は猫である。名前はまだ無い。"

---


# Model Card for mt5-zh-ja-en-trimmed
 
# Model Details
 
## Model Description
 
More information needed  
 
- **Developed by:** K024
- **Shared by [Optional]:** K024
- **Model type:** Translation 
- **Language(s) (NLP):** Japanese, Chinease, English
- **License:** [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png 
- **Parent Model:** [mt5-base](https://huggingface.co/google/mt5-base).
- **Resources for more information:**
  - [mT5 GitHub Repo](https://github.com/google-research/multilingual-t5)
   - [Associated Paper](https://arxiv.org/abs/2010.11934)
 	


# Uses
 

## Direct Use
This model can be used for the task of translation.
 
## 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
 
The model vocabulary is trimmed to ~1/3 by selecting top 85000 tokens in the training data. The code to trim the vocabulary can be found [here](https://gist.github.com/K024/4a100a0f4f4b07208958e0f3244da6ad).
 
```
wikimedia-en-ja
wikimedia-en-zh
wikimedia-ja-zh
wikititles-ja-en
wikititles-zh-en
wikimatrix-ja-zh
news-commentary-en-ja
news-commentary-en-zh
news-commentary-ja-zh
ted2020-en-ja
ted2020-en-zh
ted2020-ja-zh
```
 
 
## Training Procedure

 
### Preprocessing
 
More information needed 


 
### Speeds, Sizes, Times
This model is finetuned from [mt5-base](https://huggingface.co/google/mt5-base).

 
# 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:**
 
 
```bibtex
@misc{https://doi.org/10.48550/arxiv.2010.11934,
  doi = {10.48550/ARXIV.2010.11934},
  
  url = {https://arxiv.org/abs/2010.11934},
  
  author = {Xue, Linting and Constant, Noah and Roberts, Adam and Kale, Mihir and Al-Rfou, Rami and Siddhant, Aditya and Barua, Aditya and Raffel, Colin},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {mT5: A massively multilingual pre-trained text-to-text transformer},
  
  publisher = {arXiv},
  
  year = {2020},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```
 
 
 
 
# Glossary [optional]
More information needed 
 
# More Information [optional]
More information needed 

 
# Model Card Authors [optional]
 
K024 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,
  MT5ForConditionalGeneration,
  Text2TextGenerationPipeline,
)

path = "K024/mt5-zh-ja-en-trimmed"
pipe = Text2TextGenerationPipeline(
  model=MT5ForConditionalGeneration.from_pretrained(path),
  tokenizer=T5Tokenizer.from_pretrained(path),
)

sentence = "ja2zh: 吾輩は猫である。名前はまだ無い。"
res = pipe(sentence, max_length=100, num_beams=4)
res[0]['generated_text']
 ```
</details>