Update README.md

README.md

@@ -1,5 +1,7 @@
 ---
 pipeline_tag: sentence-similarity
+language:
+- hi
 tags:
 - sentence-transformers
 - feature-extraction
@@ -7,7 +9,7 @@ tags:
 - transformers
 ---
 
-# {MODEL_NAME}
+# hiiamsid/sentence_similarity_hindi
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -27,7 +29,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('hiiamsid/sentence_similarity_hindi')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -73,9 +75,10 @@ print(sentence_embeddings)
 
 
 ## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
+```
+cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
+0.825825032,0.8227195932,0.8127990959,0.8214681478,0.8111641963,0.8194870279,0.8096042841,0.8061808483
+```
 For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
 
 
@@ -122,4 +125,8 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+<!--- Describe where people can find more information -->
+- Model: [setu4993/LaBSE]
+(https://huggingface.co/setu4993/LaBSE)
+- Sentence Transformers [Semantic Textual Similarity]
+(https://www.sbert.net/examples/training/sts/README.html)