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tags: |
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- text-classification |
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- bert |
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--- |
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# Model Card for bleurt-tiny-512 |
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# Model Details |
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## Model Description |
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Pytorch version of the original BLEURT models from ACL paper |
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- **Developed by:** Elron Bandel, Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research |
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- **Shared by [Optional]:** Elron Bandel |
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- **Model type:** Text Classification |
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- **Language(s) (NLP):** More information needed |
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- **License:** More information needed |
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- **Parent Model:** BERT |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/google-research/bleurt/tree/master) |
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- [Associated Paper](https://aclanthology.org/2020.acl-main.704/) |
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- [Blog Post](https://ai.googleblog.com/2020/05/evaluating-natural-language-generation.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 Text Classification |
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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 authors note in the [associated paper](https://aclanthology.org/2020.acl-main.704.pdf): |
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> We use years 2017 to 2019 of the WMT Metrics Shared Task, to-English language pairs. For each year, we used the of- ficial WMT test set, which include several thou- sand pairs of sentences with human ratings from the news domain. The training sets contain 5,360, 9,492, and 147,691 records for each year. |
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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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The test sets for years 2018 and 2019 [of the WMT Metrics Shared Task, to-English language pairs.] are noisier, |
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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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@inproceedings{sellam2020bleurt, |
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title = {BLEURT: Learning Robust Metrics for Text Generation}, |
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author = {Thibault Sellam and Dipanjan Das and Ankur P Parikh}, |
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year = {2020}, |
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booktitle = {Proceedings of ACL} |
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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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Elron Bandel 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 AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("Elron/bleurt-tiny-512") |
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model = AutoModelForSequenceClassification.from_pretrained("Elron/bleurt-tiny-512") |
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model.eval() |
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references = ["hello world", "hello world"] |
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candidates = ["hi universe", "bye world"] |
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with torch.no_grad(): |
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scores = model(**tokenizer(references, candidates, return_tensors='pt'))[0].squeeze() |
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print(scores) # tensor([-0.9414, -0.5678]) |
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``` |
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See [this notebook](https://colab.research.google.com/drive/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing) for model conversion code. |
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</details> |
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