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---
license: mit
datasets:
- Silly-Machine/TuPyE-Dataset
language:
- pt
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: accuracy
value: 0.901
name: Accuracy
verified: true
- type: f1
value: 0.899
name: F1-score
verified: true
- type: precision
value: 0.897
name: Precision
verified: true
- type: recall
value: 0.901
name: Recall
verified: true
---
## Introduction
TuPy-Bert-Base-Binary-Classifier is a fine-tuned BERT model designed specifically for binary classification of hate speech in Portuguese.
Derived from the [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased),
TuPy-Bert-Base-Binary-Classifier is a refined solution for addressing binary hate speech concerns (hate or not hate).
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-Binary-Classifier"
text = "Bom dia, flor do dia!!"
classify_hate_speech(model_name, text)
``` |