JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish

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

<details>

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 2e-05
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 4

Training results

</details>

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}] 
    

.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 as my mentor & R.C.

Created by Jonatan Luna | LinkedIn