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--- |
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language: ko |
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license: apache-2.0 |
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tags: |
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- t5 |
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eos_token: </s> |
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widget: |
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- text: 아버지가 방에 들어가신다.</s> |
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--- |
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# Model Card for ke-t5-base-ko |
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# Model Details |
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## Model Description |
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- **Developed by:** Korea Electronics Technology Institute Artificial Intelligence Research Center |
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- **Shared by [Optional]:** More information needed |
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- **Model type:** Text2Text Generation |
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- **Language(s) (NLP):** More information needed |
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- **License:** More information needed |
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- **Related Models:** |
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- **Parent Model:** T5 |
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- **Resources for more information:** |
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- [Associated Paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) |
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- [Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) |
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# Uses |
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## Direct Use |
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This model can be used for the task of Text2Text Generation |
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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 is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5. |
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The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**. |
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See the [t5-base model card](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) for further information. |
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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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More information needed |
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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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### 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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@article{2020t5, |
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author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, |
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title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, |
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journal = {Journal of Machine Learning Research}, |
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year = {2020}, |
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volume = {21}, |
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number = {140}, |
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pages = {1-67}, |
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url = {http://jmlr.org/papers/v21/20-074.html} |
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} |
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``` |
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**APA:** |
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``` |
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- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67. |
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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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Korea Electronics Technology Institute Artificial Intelligence Research Center 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 AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("KETI-AIR/ke-t5-base-ko") |
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model = AutoModelForSeq2SeqLM.from_pretrained("KETI-AIR/ke-t5-base-ko") |
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``` |
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</details> |
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