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--- |
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language: ca |
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tags: |
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- "catalan" |
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metrics: |
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- accuracy |
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widget: |
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- text: "Ets més petita que un barrufet!!" |
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- text: "Ets tan lletja que et donaven de menjar per sota la porta." |
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--- |
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# roberta-base-ca-finetuned-cyberbullying-catalan |
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This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/roberta-base-ca) on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect cyberbullying on Catalan. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1508 |
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- Accuracy: 0.9665 |
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## Training and evaluation data |
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I use the concatenation from multiple datasets generated scrapping social networks (Twitter,Youtube,Discord...) to fine-tune this model. The total number of sentence pairs is above 410k sentences. Trained similar method at [roberta-base-bne-finetuned-cyberbullying-spanish](https://huggingface.co/JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish) |
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## Training procedure |
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<details> |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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</details> |
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### Model in action 🚀 |
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Fast usage with **pipelines**: |
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```python |
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from transformers import pipeline |
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model_path = "JonatanGk/roberta-base-ca-finetuned-ciberbullying-catalan" |
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bullying_analysis = pipeline("text-classification", model=model_path, tokenizer=model_path) |
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bullying_analysis( |
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"Des que et vaig veure m'en vaig enamorar de tu." |
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) |
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# Output: |
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[{'label': 'Not_bullying', 'score': 0.9996786117553711}] |
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bullying_analysis( |
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"Ets tan lletja que et donaven de menjar per sota la porta." |
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) |
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# Output: |
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[{'label': 'Bullying', 'score': 0.9927878975868225}] |
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``` |
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[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JonatanGk/Shared-Colab/blob/master/Cyberbullying_detection_(CATALAN).ipynb) |
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### Framework versions |
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- Transformers 4.10.3 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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## Citation |
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```bibtex |
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@inproceedings{armengol-estape-etal-2021-multilingual, |
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title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", |
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author = "Armengol-Estap{\'e}, Jordi and |
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Carrino, Casimiro Pio and |
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Rodriguez-Penagos, Carlos and |
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de Gibert Bonet, Ona and |
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Armentano-Oller, Carme and |
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Gonzalez-Agirre, Aitor and |
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Melero, Maite and |
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Villegas, Marta", |
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booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", |
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month = aug, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.findings-acl.437", |
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doi = "10.18653/v1/2021.findings-acl.437", |
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pages = "4933--4946", |
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} |
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``` |
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> Special thx to [Manuel Romero/@mrm8488](https://huggingface.co/mrm8488) as my mentor & R.C. |
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> Created by [Jonatan Luna](https://JonatanGk.github.io) | [LinkedIn](https://www.linkedin.com/in/JonatanGk/) |
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