
## Introduction


TuPy-Bert-Base-Multilabel is a fine-tuned BERT model designed specifically for multilabel classification of hate speech in Portuguese. 
Derived from the [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased), 
TuPy-Bert-Base-Multilabel 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-Base-Multilabel"
text = "Bom dia, flor do dia!!"
classify_hate_speech(model_name, text)
 
```
{"language": ["pt"], "license": "mit", "datasets": ["Silly-Machine/TuPyE-Dataset"], "pipeline_tag": "text-classification", "base_model": "neuralmind/bert-base-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": "f1", "value": 0.84, "name": "F1-score", "verified": true}, {"type": "precision", "value": 0.85, "name": "Precision", "verified": true}, {"type": "recall", "value": 0.84, "name": "Recall", "verified": true}]}]}]}
transformers safetensors bert text-classification pt dataset:Silly-Machine/TuPyE-Dataset base_model:neuralmind/bert-base-portuguese-cased license:mit model-index autotrain_compatible endpoints_compatible region:us