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---
inference: false
language: pt
datasets:
- ruanchaves/hatebr
---
# BERTimbau base for Offensive Language Detection
This is the [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model finetuned for
Offensive Language Detection with the [HateBR](https://huggingface.co/ruanchaves/hatebr) dataset.
This model is suitable for Portuguese.
- Git Repo: [Evaluation of Portuguese Language Models](https://github.com/ruanchaves/eplm).
- Demo: [Hugging Face Space: Offensive Language Detection](https://ruanchaves-portuguese-offensive-language-de-d4d0507.hf.space)
### **Labels**:
* 0 : The text is not offensive.
* 1 : The text is offensive.
## Full classification example
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
import numpy as np
import torch
from scipy.special import softmax
model_name = "ruanchaves/bert-base-portuguese-cased-hatebr"
s1 = "Quem não deve não teme!!"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
model_input = tokenizer(*([s1],), padding=True, return_tensors="pt")
with torch.no_grad():
output = model(**model_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = config.id2label[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) Label: {l} Score: {np.round(float(s), 4)}")
```
## Citation
Our research is ongoing, and we are currently working on describing our experiments in a paper, which will be published soon.
In the meanwhile, if you would like to cite our work or models before the publication of the paper, please cite our [GitHub repository](https://github.com/ruanchaves/eplm):
```
@software{Chaves_Rodrigues_eplm_2023,
author = {Chaves Rodrigues, Ruan and Tanti, Marc and Agerri, Rodrigo},
doi = {10.5281/zenodo.7781848},
month = {3},
title = {{Evaluation of Portuguese Language Models}},
url = {https://github.com/ruanchaves/eplm},
version = {1.0.0},
year = {2023}
}
``` |