---
license: mit
datasets:
- Silly-Machine/TuPyE-Dataset
language:
- pt

pipeline_tag: text-classification
base_model: neuralmind/bert-large-portuguese-cased
widget:
- text: 'Bom dia, flor do dia!!'

model-index:
  - name: Yi-34B
    results:
      - task:
          type: text-classfication
        dataset:
          name: TuPyE-Dataset
          type: Silly-Machine/TuPyE-Dataset
        metrics:
          - type: accuracy
            value: 0.907
            name: Accuracy
            verified: true
          - type: f1
            value: 0.903
            name: F1-score
            verified: true
          - type: precision
            value: 0.901
            name: Precision
            verified: true
          - type: recall
            value: 0.907
            name: Recall
            verified: true 
---

## Introduction


Tupy-BERT-Large-Multilabel is a fine-tuned BERT model designed specifically for multilabel classification of hate speech in Portuguese. 
Derived from the [BERTimbau large](https://huggingface.co/neuralmind/bert-large-portuguese-cased), 
TuPy-Large is a refined solution for addressing categorical hate speech concerns (ageism, aporophobia, body shame, capacitism, LGBTphobia, political, 
racism, religious intolerance, misogyny, and xenophobia).
For more details or specific inquiries, please refer to the [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/).

The efficacy of Language Models can exhibit notable variations when confronted with a shift in domain between training and test data. 
In the creation of a specialized Portuguese Language Model tailored for hate speech classification,
the original BERTimbau model underwent fine-tuning processe carried out on 
the [TuPy Hate Speech DataSet](https://huggingface.co/datasets/Silly-Machine/TuPyE-Dataset), sourced from diverse social networks.

## Available models

| Model                                    | Arch.      | #Layers | #Params |
| ---------------------------------------- | ---------- | ------- | ------- |
| `Silly-Machine/TuPy-Bert-Base-Binary-Classifier`  | BERT-Base	|12	|109M|
| `Silly-Machine/TuPy-Bert-Large-Binary-Classifier` | BERT-Large | 24      | 334M    |
| `Silly-Machine/TuPy-Bert-Base-Multilabel` | BERT-Base | 12      | 109M    |
| `Silly-Machine/TuPy-Bert-Large-Multilabel` | BERT-Large | 24      | 334M    |

## Example usage

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
import torch
import numpy as np
from scipy.special import softmax

def classify_hate_speech(model_name, text):
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    config = AutoConfig.from_pretrained(model_name)

    # Tokenize input text and prepare model input
    model_input = tokenizer(text, padding=True, return_tensors="pt")

    # Get model output scores
    with torch.no_grad():
        output = model(**model_input)
        scores = softmax(output.logits.numpy(), axis=1)
        ranking = np.argsort(scores[0])[::-1]

    # Print the results
    for i, rank in enumerate(ranking):
        label = config.id2label[rank]
        score = scores[0, rank]
        print(f"{i + 1}) Label: {label} Score: {score:.4f}")

# Example usage
model_name = "Silly-Machine/TuPy-Bert-Large-Multilabel"
text = "Bom dia, flor do dia!!"
classify_hate_speech(model_name, text)
```