readme: ---
  language: es
  tags:
  - "spanish"
  metrics:
  - accuracy
  widget:
   - text: "Eres mas pequeño que un pitufo!"
   - text: "Eres muy feo!"
   - text: "Odio tu forma de hablar!"
   - text: "Eres tan fea que cuando eras pequeña te echaban de comer por debajo de la puerta."
  
  ---
  
  # roberta-base-bne-finetuned-ciberbullying-spanish
  
  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.
  
  It achieves the following results on the evaluation set:
  
  - Loss: 0.1657
  - Accuracy: 0.9607
  
  ## Training and evaluation data
  
  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 360k sentences.
  
  ## Training procedure
  
  <details>
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Accuracy | Validation Loss |
  |:-------------:|:-----:|:-----:|:--------:|:---------------:|
  | 0.1512        | 1.0   | 22227 | 0.9501   | 0.1418          |
  | 0.1253        | 2.0   | 44454 | 0.9567   | 0.1499          |
  | 0.0973        | 3.0   | 66681 | 0.9594   | 0.1397          |
  | 0.0658        | 4.0   | 88908 | 0.9607   | 0.1657          |
  
  </details>
  
  ### Model in action 🚀
  
  Fast usage with **pipelines**:
  
  ```python
  from transformers import pipeline
  
  model_path = "JonatanGk/roberta-base-bne-finetuned-ciberbullying-spanish"
  bullying_analysis = pipeline("text-classification", model=model_path, tokenizer=model_path)
  
  bullying_analysis(
      "Desde que te vi me enamoré de ti."
      )
  
  # Output:
  [{'label': 'Not_bullying', 'score': 0.9995710253715515}]
  
  bullying_analysis(
      "Eres tan fea que cuando eras pequeña te echaban de comer por debajo de la puerta."
      )
  # Output:
  [{'label': 'Bullying', 'score': 0.9918262958526611}] 
      
  ```
  
  [![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_(SPANISH).ipynb)
  
  ### Framework versions
  
  - Transformers 4.10.3
  - Pytorch 1.9.0+cu102
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  
  
  > Special thx to [Manuel Romero/@mrm8488](https://huggingface.co/mrm8488) as my mentor & R.C.
  
  > Created by [Jonatan Luna](https://JonatanGk.github.io) | [LinkedIn](https://www.linkedin.com/in/JonatanGk/)