Facilitator weights README.md instructions

weights/Facilitator/README.md

@@ -14,12 +14,11 @@ This folder will contain the pre-trained weights for the **Facilitator** model.
 
 The Google Drive link for downloading the Facilitator pre-trained weights will be added here soon.
 
----
-
-## **File Details**
 
-- **File Name**: Facilitator pre-trained weights (TBD).
-- **Description**: Pre-trained weights for the Facilitator model.
+```bash
+pip install gdown # assuming gdown package is not already installed
+gdown --id 1_YWwILXDkx9MSoSA1kfS-y0jk3Vy4HJE -O BioM3_Facilitator_epoch20.bin
+```
 
 ---
 
@@ -28,8 +27,50 @@ The Google Drive link for downloading the Facilitator pre-trained weights will b
 Once available, the pre-trained weights can be loaded as follows:
 
 ```python
+import json
 import torch
-model = YourFacilitatorModel()  # Replace with your model class
-model.load_state_dict(torch.load("weights/Facilitator/Facilitator_weights.bin", map_location="cpu"))
-model.eval()
+from argparse import Namespace
+import Stage1_source.model as mod
+  
+# Step 1: Load JSON Configuration
+def load_json_config(json_path):
+    """
+    Load a JSON configuration file and return it as a dictionary.
+    """
+    with open(json_path, "r") as f:
+        config = json.load(f)
+    return config
+
+# Step 2: Convert JSON Dictionary to Namespace
+def convert_to_namespace(config_dict):
+    """
+    Recursively convert a dictionary to an argparse Namespace.
+    """
+    for key, value in config_dict.items():
+        if isinstance(value, dict):
+            config_dict[key] = convert_to_namespace(value)
+    return Namespace(**config_dict)
+
+if __name__ == '__main__':
+    # Path to configuration and weights
+    config_path = "stage2_config.json"
+    model_weights_path = "weights/Facilitator/BioM3_Facilitator_epoch20.bin"
+
+    # Load Configuration
+    print("Loading configuration...")
+    config_dict = load_json_config(config_path)
+    config_args = convert_to_namespace(config_dict)
+
+    # Load Model
+    print("Loading pre-trained model weights...")
+    model = mod.Facilitator(
+        in_dim=config_args.emb_dim,
+        hid_dim=config_args.hid_dim,
+        out_dim=config_args.emb_dim,
+        dropout=config_args.dropout
+    ) # Initialize the model with arguments
+    model.load_state_dict(torch.load(model_weights_path, map_location="cpu"))
+    model.eval()
+    print("Model loaded successfully with weights!")
 
+```