Update README.md

README.md

@@ -9,33 +9,43 @@ language:
 
 tags:
 - autoencoder
+---
 
-model_card_data: |
-  VAE trained on Banking 77 Open Intent Classification Dataset
-
-  ### Architecture
-  - **input_dim**: 768
-  - **hidden_dim**: 256
-  - **latent_dim**: 64
-
-  #### Encoder
-  ```python
-  encoder = nn.Sequential(
-              nn.Linear(input_dim, hidden_dim),
-              nn.ReLU()
-          )
-
-  mu = nn.Linear(hidden_dim, latent_dim)
-  logvar = nn.Linear(hidden_dim, latent_dim)
-
-  #### Decoder
-  decoder = nn.Sequential(
-              nn.Linear(latent_dim, hidden_dim),
-              nn.ReLU(),
-              nn.Linear(hidden_dim, input_dim)
-          )
-
-  #### Metrics
-  1. Training set: VAE Loss (50% reconstruction loss between original input vs reconstructed output, and 50% KL divergence between Latent Z vs standard normal distribution)
-  2. Validation set: 100% reconstruction loss -> used to find the best model (with the lowest reconstruction loss)
----
+# VAE trained on Banking 77 Open Intent Classification Dataset
+This is a Variational Autoencoder (VAE) trained on the [PolyAI/banking77](https://huggingface.co/datasets/PolyAI/banking77) dataset.
+
+### Architecture
+- **input_dim**: 768
+- **hidden_dim**: 256
+- **latent_dim**: 64
+
+#### Encoder
+The encoder maps the input to a latent space distribution.
+
+```python
+encoder = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.ReLU()
+        )
+
+mu = nn.Linear(hidden_dim, latent_dim)
+logvar = nn.Linear(hidden_dim, latent_dim)
+```
+
+#### Decoder
+The decoder reconstructs the input from a sample of the latent space.
+
+```python
+decoder = nn.Sequential(
+            nn.Linear(latent_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, input_dim)
+        )
+```
+
+#### Metrics
+The model was trained and evaluated using the following metrics:
+1. Training set: VAE Loss
+   * 50% reconstruction loss between original input vs reconstructed output
+   * 50% KL divergence between Latent Z vs standard normal distribution
+2. Validation set: 100% reconstruction loss -> used to find the best model (with the lowest reconstruction loss)