Updated Readme.md

README.md

@@ -1,8 +1,21 @@
+---
+pipeline_tag: sentence-similarity
+language:
+- es
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- LSG
+- 4096 context
+license: apache-2.0
+---
 ### LSG Variant of hiiamsid/sentence_similarity_spanish_es
 
 #### Overview
 
-This model is an enhanced version of hiiamsid/sentence_similarity_spanish_es, now transformed using Local Sparse Global (LSG) attention mechanism. The adaptation to LSG allows for efficient handling of longer sequences, making the model more versatile and robust in a wider range of natural language processing tasks.
+This model is an enhanced version of [hiiamsid/sentence_similarity_spanish_es](https://huggingface.co/hiiamsid/sentence_similarity_spanish_es), now transformed using Local Sparse Global (LSG) attention mechanism. The adaptation to LSG allows for efficient handling of longer sequences, making the model more versatile and robust in a wider range of natural language processing tasks.
 
 This LSG adaptation enables the model to efficiently process sequences up to 4096 tokens in length.
 
@@ -12,7 +25,7 @@ This LSG adaptation enables the model to efficiently process sequences up to 409
 
 #### Model adaptation
 
-This LSG variant has been adapted from our original model, ["hiiamsid/sentence_similarity_spanish_es"](https://huggingface.co/hiiamsid/sentence_similarity_spanish_eshttps:/), with the primary goal of extending its capabilities to efficiently handle longer text inputs. This enhancement enables the model to maintain high accuracy and efficiency, even with extended sequence lengths that were previously challenging for the base model.
+This LSG variant has been adapted from our original model, ["hiiamsid/sentence_similarity_spanish_es"](https://huggingface.co/hiiamsid/sentence_similarity_spanish_es), with the primary goal of extending its capabilities to efficiently handle longer text inputs. This enhancement enables the model to maintain high accuracy and efficiency, even with extended sequence lengths that were previously challenging for the base model.
 
 #### Use cases