README.md

---
language:
  - es
metrics:
  - f1
pipeline_tag: text-classification
---

## Contextualized, fine-grained hate speech detection

Try our [demo]((https://huggingface.co/spaces/piubamas/discurso-de-odio).


Model trained to detect hate speech comments in news articles. Base model is BETO, a Spanish BERT pre-trained model. The task the model was trained on is a multilabel classification problem, where each input have a label for each of the considered groups:

| Label      | Description                             |
| :--------- | :-------------------------------------- |
| WOMEN      | Against women                           |
| LGBTI      | Against LGBTI                           |
| RACISM     | Racist                                  |
| CLASS      | Classist                                |
| POLITICS   | Because of politics                     |
| DISABLED   | Against disabled                        |
| APPEARANCE | Against people because their appearance |
| CRIMINAL   | Against criminals                       |

There is an extra label `CALLS`, which represents whether a comment is a call to violent action or not.

## Input

The model was trained taking into account both the comment and the context. To feed this model, use the template

```python
TEXT [SEP] CONTEXT
```

where `[SEP]` is the special token used to separate the comment from the context.

### Example

If we want to analyze

```
Comment: Hay que matarlos a todos!!! Nos infectaron con su virus!
Context: China prohibió la venta de perros y gatos para consumo humano
```

The input should be

```python
Hay que matarlos a todos!!! Nos infectaron con su virus! [SEP] China prohibió la venta de perros y gatos para consumo humano
```

## Usage:

Sadly, the `huggingface` pipeline does not support multi-label classification, so this model cannot be tested directly in the side widget.

To use it, you can try our [demo](https://huggingface.co/spaces/piubamas/discurso-de-odio). If you want to use it with your own code, use the following snippet:

```python

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "piubamas/beto-contextualized-hate-speech"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

id2label = [model.config.id2label[k] for k in range(len(model.config.id2label))]

def predict(*args):
    encoding = tokenizer.encode_plus(*args)

    inputs = {
        k:torch.LongTensor(encoding[k]).reshape(1, -1) for k in {"input_ids", "attention_mask", "token_type_ids"}
    }

    output = model.forward(
        **inputs
    )

    chars = list(zip(id2label, list(output.logits[0].detach().cpu().numpy() > 0)))

    return [char for char, pred in chars if pred]

context = "China prohíbe la cría de perros para consumo humano")
text = "Chinos hdrmp hay que matarlos a todos"

prediction = predict(text, context)
```

## Citation

```bibtex
@article{perez2023assessing,
  title={Assessing the impact of contextual information in hate speech detection},
  author={P{\'e}rez, Juan Manuel and Luque, Franco M and Zayat, Demian and Kondratzky, Mart{\'\i}n and Moro, Agust{\'\i}n and Serrati, Pablo Santiago and Zajac, Joaqu{\'\i}n and Miguel, Paula and Debandi, Natalia and Gravano, Agust{\'\i}n and others},
  journal={IEEE Access},
  volume={11},
  pages={30575--30590},
  year={2023},
  publisher={IEEE}
}
```