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--- |
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language: es |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: roberta-base-bne-finetuned-ciberbullying-spanish |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9607097303206997 |
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--- |
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# roberta-base-bne-finetuned-ciberbullying-spanish |
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This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect ciberbullying on Spanish. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1657 |
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- Accuracy: 0.9607 |
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## Training and evaluation data |
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We 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 360k sentences. |
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## Training procedure |
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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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### Training results |
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<details> |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:-----:|:--------:|:---------------:| |
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| 0.1512 | 1.0 | 22227 | 0.9501 | 0.1418 | |
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| 0.1253 | 2.0 | 44454 | 0.9567 | 0.1499 | |
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| 0.0973 | 3.0 | 66681 | 0.9594 | 0.1397 | |
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| 0.0658 | 4.0 | 88908 | 0.9607 | 0.1657 | |
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</details> |
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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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