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roberta-base-bne-finetuned-ciberbullying-spanish

This model is a fine-tuned version of 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

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

Model in action 🚀

Fast usage with pipelines:

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

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 as my mentor & R.C.

Created by Jonatan Luna | LinkedIn

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