Update config.json

config.json

@@ -1,38 +1,15 @@
 {
   "model_type": "CustomModel",
-  "architecture": "Feedforward Neural Network with Ba-inspired Activation Function",
+  "architecture": "CustomModel",
   "input_size": 512,
   "hidden_size": 128,
   "output_size": 768,
-  "activation_function": {
-    "name": "Ba-inspired Activation",
-    "details": {
-      "weights_initialization": "Random Normal Distribution",
-      "a": 0.5,
-      "epsilon": 1e-6,
-      "function": "epsilon * torch.cos(np.pi * a * fractional_inspired * torch.log(torch.abs(fractional_inspired) + epsilon))"
-    }
-  },
-  "tokenizer": "bert-base-uncased",
-  "training_details": {
-    "optimizer": "AdamW",
-    "learning_rate": 5e-5,
-    "loss_function": "CrossEntropyLoss",
-    "batch_size": 8,
-    "epochs": 3,
-    "dataset": "Custom Dataset from JSON Lines File",
-    "dataset_preprocessing": {
-      "max_length": 512,
-      "padding": true,
-      "truncation": true
-    }
-  },
-  "performance": {
-    "final_accuracy": "Dependent on specific run and dataset",
-    "final_loss": "Dependent on specific run and dataset"
-  },
-  "usage": {
-    "inference": "Model can be used for tasks requiring sequence classification. Ensure input size matches model configuration.",
-    "additional_notes": "Model and tokenizer need to be loaded with Hugging Face's transformers library for usage."
-  }
+  "vocab_size": 30522,  // Example vocab size, adjust according to your tokenizer's vocabulary
+  "layer_norm_epsilon": 1e-12,
+  "hidden_dropout_prob": 0.1,
+  "num_attention_heads": 12,  // Adjust if your model uses attention mechanisms
+  "attention_probs_dropout_prob": 0.1,
+  "intermediate_size": 3072,  // Example size, adjust based on your model's architecture
+  "num_hidden_layers": 12,  // Adjust based on your model's depth
+  "initializer_range": 0.02
 }