Upload README.md with huggingface_hub

README.md

@@ -1,45 +1,19 @@
+
 ---
-language: en
-license: mit
+language: id
 tags:
-- audio-classification
 - math-rock
-- midwest-emo
 - mbti
-- tensorflow
-- keras
-metrics:
-- accuracy
-datasets:
-- anggars/neural-mathrock
+- multimodal
+- audio-classification
+- text-classification
 ---
 
-# Neural Mathrock Classifier (v1.0)
-
-This model is a multi-output Custom Convolutional Neural Network (CNN) designed to analyze audio characteristics specifically within the Math Rock and Midwest Emo genres.
-
-## Model Outputs
-The architecture consists of five dedicated output heads, providing simultaneous classification for:
-1. **MBTI**: Personality type association based on musical patterns.
-2. **Emotion**: Emotional state detection (e.g., Fear, Sadness, Happiness).
-3. **Audio Vibe**: General atmosphere and sonic texture.
-4. **Intensity**: Aggression and energy levels.
-5. **Tempo**: Rhythmic speed classification (Slow, Medium, Fast).
-
-## Technical Specifications
-- **Input Shape**: 128x128x3 (Mel-Spectrograms)
-- **Framework**: TensorFlow 2.x / Keras 3
-- **Architecture**: Sequential CNN with Batch Normalization, Global Average Pooling, and Dropout layers for regularization.
-- **Optimization**: Adam Optimizer with Sparse Categorical Crossentropy loss for all heads.
-
-## Accuracy Performance
-Based on the final training logs (Epoch 20):
-- **Intensity/Tempo**: ~75-77%
-- **MBTI/Emotion**: ~55-63% (Outperforming baseline random classification for 16-class MBTI).
-
-## Files
-- `neural_mathrock_model.keras`: Trained weights and model architecture.
-- `neural_mathrock_labels.pkl`: Pickle file containing label mappings for decoding predictions.
+# Neural Mathrock MBTI Classifier
+Model multimodal (XLM-RoBERTa + CNN) buat klasifikasi MBTI dari lirik dan audio lagu Math Rock.
 
-## Usage
-Preprocessing involves converting raw audio to Mel-Spectrograms at a sample rate of 22050 Hz, normalized to a 128x128 resolution. Use the provided pickle file to map the integer outputs to their respective categorical strings.
+### Status Model (Weighted 30 Epochs):
+- **Macro F1:** 0.45
+- **INTJ:** 1.00
+- **INTP:** 0.28
+- **ISTP:** 0.53