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piuba-bigdata/
beto-contextualized-hate-speech

Name: beto-contextualized-hate-speech
Availability: InStock

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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

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

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. 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}
}

piuba-bigdata/beto-contextualized-hate-speech