VAE Banking77 OpenIntentClassification
This is a Variational Autoencoder (VAE) trained on the PolyAI/banking77 dataset.
Modellquelle
- Art
- VAE
- Dateigröße
- 1,70 MB
- Version
- vvae_best_state_dict
- Aktualisiert
- 20. Okt. 2026
- Downloads
- 13
- Quelle
- Hugging Face
Quellenbeschreibung
VAE trained on Banking 77 Open Intent Classification Dataset
This is a Variational Autoencoder (VAE) trained on the PolyAI/banking77 dataset.
Architecture
- input_dim: 768
- hidden_dim: 256
- latent_dim: 64
Encoder
The encoder maps the input to a latent space distribution.
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.
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:
- Training set: VAE Loss
- 50% reconstruction loss between original input vs reconstructed output
- 50% KL divergence between Latent Z vs standard normal distribution
- Validation set: 100% reconstruction loss -> used to find the best model (with the lowest reconstruction loss)
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 20. Okt.
Modellartefakte
1 Artefakt
Modellartefakte
1 ArtefaktQuellenauszüge
3 Auszüge
Quellenauszüge
3 Auszüge--- license: mit datasets: - PolyAI/banking77 language: - en tags: - autoencoder --- # 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)
Source context: 13 downloads · 0 likes · Repo KaiquanMah/VAE-Banking77-OpenIntentClassification
Source context: 5 downloads · 0 likes · Repo KaiquanMah/VAE-Banking77-OpenIntentClassification
