Update README.md

README.md

@@ -9,7 +9,7 @@ tags:
 - sentence-embedding
 - mteb
 model-index:
-- name: bilingual-embedding-large-8k
+- name: bilingual-document-embedding
   results:
   - task:
       type: Clustering
@@ -1527,9 +1527,9 @@ metrics:
 - spearmanr
 ---
 
-# [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)
+# [bilingual-document-embedding](https://huggingface.co/Lajavaness/bilingual-document-embedding)
 
-bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [BGE M3](https://huggingface.co/BAAI/bge-m3), a pre-trained language model larged on the [BGE M3](https://huggingface.co/BAAI/bge-m3) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
+bilingual-document-embedding is the Embedding Model for document in bilingual language: french and english with context length up to 8096 tokens . This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [BGE M3](https://huggingface.co/BAAI/bge-m3), a pre-trained language model larged on the [BGE M3](https://huggingface.co/BAAI/bge-m3) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
 
 
 ## Full Model Architecture
@@ -1568,7 +1568,7 @@ from sentence_transformers import SentenceTransformer
 
 sentences = ["Paris est une capitale de la France", "Paris is a capital of France"]
 
-model = SentenceTransformer('Lajavaness/bilingual-embedding-large-8k', trust_remote_code=True)
+model = SentenceTransformer('Lajavaness/bilingual-document-embedding', trust_remote_code=True)
 print(embeddings)
 
 ```