Update README.md

README.md

@@ -522,9 +522,9 @@ model-index:
       name: Cosine Map@100
 ---
 
-# BGE base Financial Matryoshka
+# Legal-Embed-snowflake-arctic-embed-m-v2.0
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) on the [legal-rag-embedding-dataset](https://huggingface.co/datasets/axondendriteplus/legal-rag-embedding-dataset). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
 ## Model Details
 
@@ -539,22 +539,6 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [S
 - **Language:** en
 - **License:** apache-2.0
 
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
-
-### Full Model Architecture
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: GteModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Normalize()
-)
-```
-
 ## Usage
 
 ### Direct Usage (Sentence Transformers)
@@ -570,7 +554,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
+model = SentenceTransformer("axondendriteplus/Legal-Embed-snowflake-arctic-embed-m-v2.0")
 # Run inference
 sentences = [
     '1996. In section 19 of the Depositories Act, 1996 (hereafter in this chapter referred to as the principal Act in this chapter), the following Explanation shall be inserted, namely:- Explanation. For the removal of doubts, it is hereby declared that power to issue directions under this section shall include and always be deemed to have been included the power to direct any person, who made profit or averted loss by indulging in any transaction or activity in contravention of the provisions of this Act or regulations made thereunder, to disgorge an amount equivalent to the wrongful gain made or loss averted by such contravention.',