model documentation

README.md

@@ -1,58 +1,223 @@
----
-language:
-- zh
-- ja
-- en
-
-tags:
-- translation
-
-widget:
-- text: "ja2zh: 吾輩は猫である。名前はまだ無い。"
-
-license: cc-by-nc-sa-4.0
----
-
-This model is finetuned from [mt5-base](https://huggingface.co/google/mt5-base).
-
-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).
-
-Usage:
-```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']
-```
-
-Training data:
-```
-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
-```
-
-License: [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
-
-[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
-[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
+---
+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>