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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- zh |
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- ja |
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- en |
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tags: |
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- translation |
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widget: |
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- text: "ja2zh: 吾輩は猫である。名前はまだ無い。" |
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--- |
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# Model Card for mt5-zh-ja-en-trimmed |
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# Model Details |
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## Model Description |
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More information needed |
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- **Developed by:** K024 |
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- **Shared by [Optional]:** K024 |
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- **Model type:** Translation |
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- **Language(s) (NLP):** Japanese, Chinease, English |
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- **License:** [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png |
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- **Parent Model:** [mt5-base](https://huggingface.co/google/mt5-base). |
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- **Resources for more information:** |
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- [mT5 GitHub Repo](https://github.com/google-research/multilingual-t5) |
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- [Associated Paper](https://arxiv.org/abs/2010.11934) |
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# Uses |
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## Direct Use |
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This model can be used for the task of translation. |
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## Downstream Use [Optional] |
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More information needed. |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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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. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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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). |
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``` |
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wikimedia-en-ja |
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wikimedia-en-zh |
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wikimedia-ja-zh |
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wikititles-ja-en |
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wikititles-zh-en |
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wikimatrix-ja-zh |
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news-commentary-en-ja |
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news-commentary-en-zh |
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news-commentary-ja-zh |
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ted2020-en-ja |
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ted2020-en-zh |
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ted2020-ja-zh |
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``` |
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## Training Procedure |
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### Preprocessing |
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More information needed |
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### Speeds, Sizes, Times |
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This model is finetuned from [mt5-base](https://huggingface.co/google/mt5-base). |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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More information needed |
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### Factors |
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More information needed |
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### Metrics |
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More information needed |
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## Results |
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More information needed |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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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). |
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- **Hardware Type:** More information needed |
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- **Hours used:** More information needed |
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- **Cloud Provider:** More information needed |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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More information needed |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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More information needed |
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### Software |
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More information needed. |
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# Citation |
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**BibTeX:** |
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```bibtex |
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@misc{https://doi.org/10.48550/arxiv.2010.11934, |
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doi = {10.48550/ARXIV.2010.11934}, |
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url = {https://arxiv.org/abs/2010.11934}, |
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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}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {mT5: A massively multilingual pre-trained text-to-text transformer}, |
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publisher = {arXiv}, |
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year = {2020}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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``` |
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# Glossary [optional] |
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More information needed |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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K024 in collaboration with Ezi Ozoani and the Hugging Face team |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import ( |
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T5Tokenizer, |
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MT5ForConditionalGeneration, |
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Text2TextGenerationPipeline, |
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) |
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path = "K024/mt5-zh-ja-en-trimmed" |
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pipe = Text2TextGenerationPipeline( |
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model=MT5ForConditionalGeneration.from_pretrained(path), |
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tokenizer=T5Tokenizer.from_pretrained(path), |
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) |
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sentence = "ja2zh: 吾輩は猫である。名前はまだ無い。" |
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res = pipe(sentence, max_length=100, num_beams=4) |
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res[0]['generated_text'] |
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``` |
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</details> |
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