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---
license: mit
datasets:
- FpOliveira/TuPi-Portuguese-Hate-Speech-Dataset-Binary
language:
- pt
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
base_model: neuralmind/bert-large-portuguese-cased
widget:
- text: 'Bom dia, flor do dia!!'
---
## Introduction
Tupi-BERT-Large is a fine-tuned BERT model designed specifically for binary classification of hate speech in Portuguese. Derived from the [BERTimbau large](https://huggingface.co/neuralmind/bert-large-portuguese-cased), TuPi-Large is refinde solution for addressing hate speech concerns.
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 [TuPi Hate Speech DataSet](https://huggingface.co/datasets/FpOliveira/TuPi-Portuguese-Hate-Speech-Dataset-Binary), sourced from diverse social networks.
## Available models
| Model | Arch. | #Layers | #Params |
| ---------------------------------------- | ---------- | ------- | ------- |
| `FpOliveira/tupi-bert-base-portuguese-cased` | BERT-Base |12 |109M|
| `FpOliveira/tupi-bert-large-portuguese-cased` | BERT-Large | 24 | 334M |
| `FpOliveira/tupi-bert-base-portuguese-cased-multiclass-multilabel` | BERT-Base | 12 | 109M |
| `FpOliveira/tupi-bert-large-portuguese-cased-multiclass-multilabel` | BERT-Large | 24 | 334M |
## Example usage 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 = "FpOliveira/tupi-bert-large-portuguese-cased"
text = "Bom dia, flor do dia!!"
classify_hate_speech(model_name, text)
``` |