Create README.md

README.md

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+
+# Summary Classifier (with Gemma Embeddings)
+
+This repository contains a custom PyTorch model for summary classification. It uses `google/embeddinggemma-300m` for embeddings and a custom classification head.
+
+## How to Use
+
+First, install the required libraries:
+
+```bash
+pip install -r requirements.txt
+```
+
+You can then load and use the model with the following Python code. The code will automatically download the model files from this repository.
+
+```python
+import torch
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import hf_hub_download
+from model import SummaryClassifier 
+
+REPO_ID = "Prahaladha/summary-gemma-classifier" 
+WEIGHTS_FILE = "summary_classifier_gemma.pth"
+
+print(f"Downloading model from {REPO_ID}...")
+model_path = hf_hub_download(repo_id=REPO_ID, filename=WEIGHTS_FILE)
+
+checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
+
+embedder_name = checkpoint['embedder_name']
+num_classes = checkpoint['num_classes']
+dropout = checkpoint.get('dropout', 0.1) 
+
+print(f"Loading model with embedder: {embedder_name}")
+print(f"Number of classes: {num_classes}")
+
+
+embedder = SentenceTransformer(embedder_name)
+
+model = SummaryClassifier(
+    embedder=embedder, 
+    num_classes=num_classes, 
+    dropout=dropout
+)
+
+model.head.load_state_dict(checkpoint['head_state_dict'])
+model.eval()
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model.to(device)
+print(f"Model loaded and moved to {device}")
+
+test_summaries = ["A concise and accurate recap.", "This was a long and winding explanation."]
+with torch.no_grad():
+    logits = model(test_summaries) 
+    probs = torch.softmax(logits, dim=-1)
+    predicted_class = torch.argmax(probs, dim=-1)
+    
+    print("\n--- Inference Test ---")
+    print(f"Input: {test_summaries}")
+    print(f"Probs: {probs.cpu().numpy()}")
+    print(f"Predicted: {predicted_class.cpu().numpy()}")
+```