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---
license: mit
language: es
tags:
- "spanish"
metrics:
- ROC-AUC
widget:
- text: "Sos pero bien imbécil!"
- text: "Tirate de un puente!"
- text: "sapo, gonorrea de mierda"
- text: "Esta perrita me las va pagar"
---
# colombian-spanish-cyberbullying-classifier
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on a dataset created by manually gathering posts from the social network Twitter to detect cyberbullying in Spanish.
## Training and evaluation data
The dataset used was a small one, consisting of 3570 tweets, which were manually labeled as cyberbullying or not cyberbullying. The distribution of tweets and of cyberbullying and non-cyberbullying was the same.
## Training procedure
<details>
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- weight_decay=0.01
- warmup_steps=500
- num_epochs: 2
### Training results
| Training Loss | Epoch | ROC-AUC | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| --- | 1.0 | 0.8756 | 0.4375 |
| 0.4945 | 2.0 | 0.9022 | 0.5060 |
</details>
### Model in action 🚀
Fast usage with **pipelines**:
```python
!pip install -q transformers
from transformers import pipeline
model_path = "FelipeGuerra/colombian-spanish-cyberbullying-classifier"
bullying_analysis = pipeline("text-classification", model=model_path, tokenizer=model_path)
bullying_analysis(
"Como dice mi mamá: va caer palo de agua"
)
# Output:
[{'label': 'Not_bullying', 'score': 0.977687656879425}]
bullying_analysis(
"Esta perrita me las va pagar"
)
# Output:
[{'label': 'Bullying', 'score': 0.9404164552688599}]
```
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Pandas 1.5.3
- scikit-learn 1.2.2
> Created by Felipe Guerra Sáenz| [LinkedIn](https://www.linkedin.com/in/felipe-guerra-s%C3%A1enz-58207126a/) |