Update README.md

README.md

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+# Mixed-BGE-M3-Email
 
-    # Mixed BGE-M3 Email Model
-    
-    This model is a mixture of BAAI/bge-m3 and a fine-tuned version optimized for email understanding.
-    
-    ## Model Details
-    - Base Model: BAAI/bge-m3
-    - Fine-tuning: Custom email dataset
-    - Mixing Weights: 0.5 base, 0.5 fine-tuned
-    
-    ## Usage
-    ```python
-    from transformers import AutoModel, AutoTokenizer
-    
-    model = AutoModel.from_pretrained("your-username/mixed-bge-m3-email")
-    tokenizer = AutoTokenizer.from_pretrained("your-username/mixed-bge-m3-email")
-    ```
-    
+This is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) optimized for email content retrieval. The model was trained on a mixed-language (English/Korean) email dataset to improve retrieval performance for various email-related queries.
+
+## Model Description
+
+- **Model Type:** Embedding model (encoder-only)
+- **Base Model:** BAAI/bge-m3
+- **Languages:** English, Korean
+- **Domain:** Email content, business communication
+- **Training Data:** Mixed-language email dataset with various types of queries (metadata, long-form, short-form, yes/no questions)
+
+## Quickstart
+
+```python
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.docstore.document import Document
+
+# Initialize the embedding model
+embeddings = HuggingFaceEmbeddings(
+    model_name="doubleyyh/mixed-bge-m3-email",
+    model_kwargs={'device': 'cuda'},
+    encode_kwargs={'normalize_embeddings': True}
+)
+
+# Example emails
+emails = [
+    {
+        "subject": "회의 일정 변경 안내",
+        "from": [["김철수", "[email protected]"]],
+        "to": [["이영희", "[email protected]"]],
+        "cc": [["박지원", "[email protected]"]],
+        "date": "2024-03-26T10:00:00",
+        "text_body": "안녕하세요, 내일 예정된 프로젝트 미팅을 오후 2시로 변경하고자 합니다."
+    },
+    {
+        "subject": "Project Timeline Update",
+        "from": [["John Smith", "[email protected]"]],
+        "to": [["Team", "[email protected]"]],
+        "cc": [],
+        "date": "2024-03-26T11:30:00",
+        "text_body": "Hi team, I'm writing to update you on the Q2 project milestones."
+    }
+]
+
+# Format emails into documents
+docs = []
+for email in emails:
+    # Format email content
+    content = "\n".join([f"{k}: {v}" for k, v in email.items()])
+    docs.append(Document(page_content=content))
+
+# Create FAISS index
+db = FAISS.from_documents(docs, embeddings)
+
+# Query examples (supports both Korean and English)
+queries = [
+    "회의 시간이 언제로 변경되었나요?",
+    "When is the meeting rescheduled?",
+    "프로젝트 일정",
+    "Q2 milestones"
+]
+
+# Perform similarity search
+for query in queries:
+    print(f"\nQuery: {query}")
+    results = db.similarity_search(query, k=1)
+    print(f"Most relevant email:\n{results[0].page_content[:200]}...")
+```
+
+
+## Intended Use & Limitations
+
+### Intended Use
+- Email content retrieval
+- Similar document search in email corpora
+- Question answering over email content
+- Multi-language email search systems
+
+### Limitations
+- Performance may vary for domains outside of email content
+- Best suited for business communication context
+- While supporting both English and Korean, performance might vary between languages
+
+## Citation
+
+```bibtex
+@misc{mixed-bge-m3-email,
+  author = {doubleyyh},
+  title = {Mixed-BGE-M3-Email: Fine-tuned Embedding Model for Email Content},
+  year = {2024},
+  publisher = {HuggingFace}
+}
+```
+
+## License
+
+This model follows the same license as the base model (bge-m3).
+
+## Contact
+
+For questions or feedback, please use the GitHub repository issues section or contact through HuggingFace.