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tags:
- torch_molecule
- molecular-property-prediction
library_name: torch_molecule
---
# EdgePredMolecularEncoder Model
## Model Description
- **Model Type**: EdgePredMolecularEncoder
- **Framework**: torch_molecule
- **Last Updated**: 2025-04-11
## Task Summary
| Task | Version | Last Updated | Parameters | Metrics |
|------|---------|--------------|------------|----------|
| default | 0.0.2 | 2025-04-11 | 3,340,505 | |
## Usage
```python
from torch_molecule import EdgePredMolecularEncoder
# Load model for specific task
model = EdgePredMolecularEncoder()
model.load(
"local_model_dir/EdgePredMolecularEncoder.pt",
repo="Einae/EdgePred"
)
# For predictor: Make predictions
# predictions = model.predict(smiles_list)
# For generator: Make generations
# generations = model.generate(n_samples)
# For encoder: Make encodings
# encodings = model.encode(smiles_list)
```
## Tasks Details
### default Task
- **Current Version**: 0.0.2
- **Last Updated**: 2025-04-11
- **Parameters**: 3,340,505
- **Configuration**:
```python
{
"encoder_type": "gin-virtual",
"readout": "sum",
"num_layer": 5,
"hidden_size": 300,
"drop_ratio": 0.5,
"norm_layer": "batch_norm",
"batch_size": 128,
"epochs": 100,
"learning_rate": 0.001,
"weight_decay": 0.0,
"grad_clip_value": null,
"use_lr_scheduler": false,
"scheduler_factor": 0.5,
"scheduler_patience": 5,
"fitting_epoch": 99,
"device": {
"_type": "unknown",
"repr": "cuda:0"
},
"verbose": false,
"model_name": "EdgePredMolecularEncoder"
}
```
|