Update README.md

README.md

@@ -25,7 +25,7 @@ base_model:
 
 **Council Topics Classifier** is an ensemble machine learning system specialized in **multi-label topic classification** for Portuguese municipal council meeting minutes subjects. The model combines Gradient Boosting with Active Learning and BERTimbau embeddings to identify multiple simultaneous topics within municipal discussion subjects, making it particularly effective for categorizing complex governmental content.
 
-🚀 **Try out the model:** [Hugging Face Space Demo](https://huggingface.co/spaces/anonymous12321/GB_CouncilTopics-PT)
+🚀 **Try out the model:** [Demo Council Topics Classifier PT](https://huggingface.co/spaces/anonymous12321/Council_Topics_Classifier_PT)
 
 ## Key Features
 
@@ -100,18 +100,18 @@ bert_model = AutoModel.from_pretrained("neuralmind/bert-base-portuguese-cased").
 
 # Preprocess text
 text = "A Câmara Municipal aprovou o orçamento de 2024..."
-# (apply smart_preprocess function - see app.py)
+# (apply smart_preprocess function - see demo source code)
 
 # Extract features
 tfidf_features = tfidf.transform([text])
-# (extract BERT embeddings - see app.py)
+# (extract BERT embeddings - see demo source code)
 
 # Combine features and predict
 X_combined = np.hstack([tfidf_features.toarray(), bert_embeddings])
 
 # Get ensemble predictions
 logistic_proba = logistic_model.predict_proba(X_combined)
-# (apply GB models and adaptive weighting - see app.py)
+# (apply GB models and adaptive weighting - see demo source code)
 
 # Apply optimal thresholds
 predictions = (ensemble_proba >= optimal_thresholds).astype(int)
@@ -125,7 +125,7 @@ print(f"Predicted Topics: {predicted_labels}")
 
 The model was trained on a curated dataset of Portuguese municipal council meeting minutes:
 
-- **Documents**: 2,500+ meeting minutes subjects
+- **Documents**: 2,500+ meeting minutes discussion subjects
 - **Time Period**: 2021-2024
 - **Source**: Portuguese municipalities (anonymized)
 - **Labels**: 22 topic categories