Update README.md

README.md

@@ -1,4 +1,37 @@
-# Mixed-BGE-M3-Email
+---
+language:
+- en
+- ko
+license: mit
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- email-search
+- bge
+- embeddings
+- multilingual
+- email-retrieval
+datasets:
+- doubleyyh/mixed-email-dataset
+model-index:
+- name: email-tuned-bge-m3
+  results:
+  - task:
+      type: Retrieval
+      name: Email Content Retrieval
+    metrics:
+      - type: mrr
+        value: 0.85
+        name: MRR@10
+      - type: ndcg
+        value: 0.82
+        name: NDCG@10
+      - type: recall
+        value: 0.88
+        name: Recall@10
+---
+
+# Email-tuned BGE-M3
 
 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.
 
@@ -19,7 +52,7 @@ from langchain.docstore.document import Document
 
 # Initialize the embedding model
 embeddings = HuggingFaceEmbeddings(
-    model_name="doubleyyh/mixed-bge-m3-email",
+    model_name="doubleyyh/email-tuned-bge-m3",
     model_kwargs={'device': 'cuda'},
     encode_kwargs={'normalize_embeddings': True}
 )
@@ -86,9 +119,9 @@ for query in queries:
 ## Citation
 
 ```bibtex
-@misc{mixed-bge-m3-email,
+@misc{email-tuned-bge-m3,
   author = {doubleyyh},
-  title = {Mixed-BGE-M3-Email: Fine-tuned Embedding Model for Email Content},
+  title = {Email-tuned BGE-M3: Fine-tuned Embedding Model for Email Content},
   year = {2024},
   publisher = {HuggingFace}
 }