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--- |
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language: |
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- en |
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tags: |
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- formality |
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licenses: |
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- cc-by-nc-sa |
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--- |
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**Model Overview** |
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This is the model presented in the paper "Detecting Text Formality: A Study of Text Classification Approaches". |
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The original model is [DeBERTa (large)](https://huggingface.co/microsoft/deberta-v3-large). Then, it was fine-tuned on the English corpus for fomality classiication [GYAFC](https://arxiv.org/abs/1803.06535). |
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In our experiments, the model showed the best results within Transformer-based models for the task. More details, code and data can be found [here](https://github.com/s-nlp/formality). |
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**Evaluation Results** |
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Here, we provide several metrics of the best models from each category participated in the comparison to understand the ranks of values. |
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| | acc | f1-formal | f1-informal | |
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|------------------|------|-----------|-------------| |
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| bag-of-words | 79.1 | 81.8 | 75.6 | |
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| CharBiLSTM | 87.0 | 89.0 | 84.0 | |
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| DistilBERT-cased | 80.1 | 83.0 | 75.6 | |
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| DeBERTa-large | 87.8 | 89.0 | 86.1 | |
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**How to use** |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model_name = 'deberta-large-formality-ranker' |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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``` |
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**Citation** |
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``` |
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TBD |
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``` |
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## Licensing Information |
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[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. |
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[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] |
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[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png |
