Add usage example script

README.md

@@ -27,6 +27,92 @@ See the README.md for detailed usage examples using our module abstractions.
 - **Type**: Pretrained backbone (no fine-tuning)
 - **Embedding Dimension**: Varies by backbone model
 
+## Usage Example
+
+```python
+"""
+Example: Using EmbeddingGemma-300m for Tag Classification
+
+This example shows how to use the pretrained EmbeddingGemma-300m backbone
+for zero-shot tag classification using our module abstractions.
+
+Installation:
+    pip install git+https://github.com/Pieces/TAG-module.git@main
+    # Or: pip install -e .
+"""
+
+import torch
+from tags_model.models.backbone import SharedTextBackbone
+from playground.validate_from_checkpoint import compute_ranked_tags
+
+# Load the pretrained backbone
+print("Loading EmbeddingGemma-300m...")
+backbone = SharedTextBackbone(
+    model_name="google/embeddinggemma-300m",
+    embedding_dim=768,
+    freeze_backbone=True,
+    pooling_mode="mean",
+)
+backbone.eval()
+print("✓ Model loaded!")
+
+# Example query
+query_text = "How to implement OAuth2 authentication in a Python Flask API?"
+
+# Candidate tags to rank
+candidate_tags = [
+    "python", "flask", "oauth2", "authentication", "api",
+    "security", "web-development", "jwt", "rest-api", "backend"
+]
+
+print(f"\nQuery: {query_text}")
+print(f"Candidate tags: {candidate_tags}\n")
+
+# Encode query and tags
+with torch.inference_mode():
+    query_emb = backbone.encode_texts([query_text], max_length=2048, return_dict=False)[0]
+    tag_embs = backbone.encode_texts(candidate_tags, max_length=2048, return_dict=False)
+
+print(f"Query embedding shape: {query_emb.shape}")
+print(f"Tag embeddings shape: {tag_embs.shape}")
+
+# Rank tags by similarity
+ranked_tags = compute_ranked_tags(
+    query_emb=query_emb,
+    pos_embs=torch.empty(0, 768),  # No positives for zero-shot
+    neg_embs=torch.empty(0, 768),  # No negatives for zero-shot
+    general_embs=tag_embs,
+    positive_tags=[],
+    negative_tags=[],
+    general_tags=candidate_tags,
+)
+
+# Display top-ranked tags
+print("\n" + "="*60)
+print("Top Ranked Tags:")
+print("="*60)
+for tag, rank, label, score in ranked_tags[:5]:
+    print(f"{rank:2d}. {tag:20s} (score: {score:.4f})")
+
+print("\n" + "="*60)
+print("Example complete!")
+
+
+```
+
+### Running the Example
+
+```bash
+# Install the repository first
+pip install git+https://github.com/Pieces/TAG-module.git@main
+# Or for local development:
+pip install -e .
+
+# Run the example
+python embeddinggemma_example.py
+```
+
+
 ## Citation
 
 If you use this model, please cite: