Update README.md

README.md

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 ---
-license: mit
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- RAG
+- domain-adapted
+- custom-embeddings
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+metrics:
+- cosine_similarity
+- mrr
+- ndcg@10
 ---
+
+# Custom Contextual Embedding Model (v1.0-FineTuned)
+
+This is a specialized, fine-tuned dense text embedding model engineered for production Retrieval-Augmented Generation (RAG), context-aware semantic search, and document reranking. 
+
+This model has undergone custom contrastive instruction tuning to improve cross-domain query-to-document matching and handling of nuanced contextual semantics.
+
+---
+
+## Key Improvements & Features
+
+* **Custom Contrastive Fine-Tuning:** Trained using Multiple Negatives Ranking Loss (MNRL) paired with hard-negative mining for high-precision retrieval.
+* **Enhanced Context Window:** Retains structural context for long-form passages (up to 512–8192 tokens depending on sequence truncation limits).
+* **Low-Latency Retrieval:** 0.6B parameter scale balances embedding quality with fast query-side inference on standard GPU infrastructure.
+* **Optimized Cosine Space:** Specifically calibrated for Cosine Similarity metric evaluation, eliminating the need for expensive vector recalibration.
+
+---
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [`sentence-transformers`](https://www.SBERT.net) installed:
+
+```bash
+pip install -U sentence-transformers
+
+from sentence_transformers import SentenceTransformer, util
+
+model = SentenceTransformer(
+    "Layasaran/text_embed_0.5b",
+    trust_remote_code=True
+)
+
+texts = [
+    "Scientists explore the universe driven by curiosity.",
+    "Children learn through curious exploration.",
+    "Historical discoveries began with curious questions.",
+    "Animals use curiosity to adapt and survive.",
+    "Philosophy examines the nature of curiosity.",
+]
+
+doc_embeddings = model.encode(texts, convert_to_tensor=True)
+
+query = "How do children acquire knowledge?"
+query_embedding = model.encode(query, convert_to_tensor=True)
+
+similarity_scores = util.cos_sim(query_embedding, doc_embeddings)[0]
+
+top_k = 3
+top_indices = similarity_scores.argsort(descending=True)[:top_k]
+
+print(f"Query: '{query}'\n")
+print("Top Retrieved Contexts for RAG Prompt:")
+print("-" * 50)
+
+retrieved_context = []
+for idx in top_indices:
+    score = float(similarity_scores[idx])
+    text = texts[idx]
+    retrieved_context.append(text)
+    print(f"Score: {score:.4f} | Text: {text}")
+
+rag_context_str = "\n".join([f"- {doc}" for doc in retrieved_context])
+rag_prompt = f"""Use the following context to answer the question:
+
+Context:
+{rag_context_str}
+
+Question: {query}
+Answer:"""
+
+print("\n" + "=" * 50)
+print("Final RAG Prompt structure:")
+print("=" * 50)
+print(rag_prompt)