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README.md

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----
-license: mit
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+# MISO-BR Misogyny Classifier
+
+This model classifies text in Brazilian Portuguese as misogynistic or non-misogynistic. It's trained on the [MISO-BR dataset](https://huggingface.co/datasets/fabiopassos/miso-br).
+
+## Model Details
+
+- **Model Type**: TF-IDF + RandomForest classifier
+- **Language**: Portuguese (Brazil)
+- **Task**: Binary classification (misogynistic vs non-misogynistic content)
+- **Framework**: scikit-learn
+
+## Performance
+
+The model was evaluated on a test set and achieved:
+
+- **F1 Score (macro)**: 0.6758
+- **Accuracy**: 0.6778
+- **AUC**: 0.7314
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import joblib
+import spacy
+
+# Download the model from Hugging Face Hub
+model_path = hf_hub_download(repo_id="fabiopassos/miso-br-classifier", 
+                             filename="models/miso_br_rf_classifier.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Load spaCy for Portuguese
+nlp = spacy.load("pt_core_news_sm")
+
+# Preprocess function
+def preprocess_text(text):
+    doc = nlp(text)
+    tokens = [token.lemma_.lower() for token in doc 
+              if not token.is_stop and not token.is_punct and token.is_alpha]
+    return " ".join(tokens)
+
+# Example text
+text = "Seu texto para classificar aqui"
+processed_text = preprocess_text(text)
+
+# Predict
+prediction = model.predict([processed_text])[0]
+probability = model.predict_proba([processed_text])[0][1]
+
+print(f"Texto: {text}")
+print(f"É misógino: {'Sim' if prediction == 1 else 'Não'}")
+print(f"Probabilidade: {probability:.4f}")

models/miso_br_rf_classifier.joblib

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+size 1177125

models/preprocessing_config.pkl

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+size 3498

requirements.txt

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+scikit-learn==1.7.0
+spacy==3.7.2
+joblib>=1.3.0
+pt_core_news_sm @ https://github.com/explosion/spacy-models/releases/download/pt_core_news_sm-3.7.0/pt_core_news_sm-3.7.0-py3-none-any.whl