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README.md

@@ -1,85 +1,84 @@
----
-license: apache-2.0
-tags:
-- pytorch
-- candlestick
-- financial-analysis
-- multimodal
-- bert
-- vit
-- cross-attention
-- trading
-- forecasting
-datasets:
-- tuankg1028/btc-candlestick-dataset
----
-
-# CandleFusion Model
-
-A multimodal financial analysis model that combines textual market sentiment with visual candlestick patterns for enhanced trading signal prediction and price forecasting.
-
-## Links
-- 🔗 **GitHub Repository**: https://github.com/tuankg1028/CandleFusion
-- 🚀 **Demo on Hugging Face Spaces**: https://huggingface.co/spaces/tuankg1028/candlefusion
-
-## Training Results
-- **Best Epoch**: 18
-- **Best Validation Loss**: 463938.7056
-- **Training Epochs**: 23
-- **Early Stopping**: Yes
-
-## Architecture Overview
-
-### Core Components
-- **Text Encoder**: BERT (bert-base-uncased) for processing market sentiment and news
-- **Vision Encoder**: Vision Transformer (ViT-base-patch16-224) for candlestick pattern recognition
-- **Cross-Attention Fusion**: Multi-head attention mechanism (8 heads, 768 dim) for text-image integration
-- **Dual Task Heads**: 
-  - Classification head for trading signals (buy/sell/hold)
-  - Regression head for next closing price prediction
-
-### Data Flow
-1. **Text Processing**: Market sentiment -> BERT -> CLS token (768-dim)
-2. **Image Processing**: Candlestick charts -> ViT -> Patch embeddings (197 tokens, 768-dim each)
-3. **Cross-Modal Fusion**: Text CLS as query, Image patches as keys/values -> Fused representation
-4. **Dual Predictions**: 
-   - Fused features -> Classification head -> Trading signal logits
-   - Fused features -> Regression head -> Price forecast
-
-### Model Specifications
-- **Input Text**: Tokenized to max 64 tokens
-- **Input Images**: Resized to 224x224 RGB
-- **Hidden Dimension**: 768 (consistent across encoders)
-- **Output Classes**: 3 (buy/sell/hold)
-- **Dropout**: 0.3 in both heads
-
-## Training Details
-- **Epochs**: 100
-- **Learning Rate**: 2e-05
-- **Loss Function**: CrossEntropy (classification) + MSE (regression)
-- **Loss Weight (alpha)**: 0.5 for regression term
-- **Optimizer**: AdamW with linear scheduling
-- **Early Stopping Patience**: 5
-
-## Usage
-```python
-from model import CrossAttentionModel
-import torch
-
-# Load model
-model = CrossAttentionModel()
-model.load_state_dict(torch.load("pytorch_model.bin"))
-model.eval()
-
-# Inference
-outputs = model(input_ids, attention_mask, pixel_values)
-trading_signals = outputs["logits"]
-price_forecast = outputs["forecast"]
-```
-
-## Performance
-The model simultaneously optimizes for:
-- **Classification Task**: Trading signal accuracy
-- **Regression Task**: Price prediction MSE
-
-This dual-task approach enables the model to learn both categorical market direction and continuous price movements.
+
+---
+license: apache-2.0
+tags:
+- pytorch
+- candlestick
+- financial-analysis
+- multimodal
+- bert
+- vit
+- cross-attention
+- trading
+- forecasting
+---
+
+# CandleFusion Model
+
+A multimodal financial analysis model that combines textual market sentiment with visual candlestick patterns for enhanced trading signal prediction and price forecasting.
+
+## Links
+- 🔗 **GitHub Repository**: https://github.com/tuankg1028/CandleFusion
+- 🚀 **Demo on Hugging Face Spaces**: https://huggingface.co/spaces/tuankg1028/candlefusion
+
+## Training Results
+- **Best Epoch**: 18
+- **Best Validation Loss**: 316165.5985
+- **Training Epochs**: 23
+- **Early Stopping**: Yes
+
+## Architecture Overview
+
+### Core Components
+- **Text Encoder**: BERT (bert-base-uncased) for processing market sentiment and news
+- **Vision Encoder**: Vision Transformer (ViT-base-patch16-224) for candlestick pattern recognition
+- **Cross-Attention Fusion**: Multi-head attention mechanism (8 heads, 768 dim) for text-image integration
+- **Dual Task Heads**: 
+  - Classification head for trading signals (buy/sell/hold)
+  - Regression head for next closing price prediction
+
+### Data Flow
+1. **Text Processing**: Market sentiment -> BERT -> CLS token (768-dim)
+2. **Image Processing**: Candlestick charts -> ViT -> Patch embeddings (197 tokens, 768-dim each)
+3. **Cross-Modal Fusion**: Text CLS as query, Image patches as keys/values -> Fused representation
+4. **Dual Predictions**: 
+   - Fused features -> Classification head -> Trading signal logits
+   - Fused features -> Regression head -> Price forecast
+
+### Model Specifications
+- **Input Text**: Tokenized to max 64 tokens
+- **Input Images**: Resized to 224x224 RGB
+- **Hidden Dimension**: 768 (consistent across encoders)
+- **Output Classes**: 3 (buy/sell/hold)
+- **Dropout**: 0.3 in both heads
+
+## Training Details
+- **Epochs**: 100
+- **Learning Rate**: 2e-05
+- **Loss Function**: CrossEntropy (classification) + MSE (regression)
+- **Loss Weight (alpha)**: 0.5 for regression term
+- **Optimizer**: AdamW with linear scheduling
+- **Early Stopping Patience**: 5
+
+## Usage
+```python
+from model import CrossAttentionModel
+import torch
+
+# Load model
+model = CrossAttentionModel()
+model.load_state_dict(torch.load("pytorch_model.bin"))
+model.eval()
+
+# Inference
+outputs = model(input_ids, attention_mask, pixel_values)
+trading_signals = outputs["logits"]
+price_forecast = outputs["forecast"]
+```
+
+## Performance
+The model simultaneously optimizes for:
+- **Classification Task**: Trading signal accuracy
+- **Regression Task**: Price prediction MSE
+
+This dual-task approach enables the model to learn both categorical market direction and continuous price movements.