Update README.md

README.md

@@ -6,14 +6,21 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+license: apache-2.0
+datasets:
+- nli_tr
+- emrecan/stsb-mt-turkish
+language:
+- tr
 ---
 
 # atasoglu/mbert-base-cased-nli-stsb-tr
 
 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.
 
-<!--- Describe your model here -->
+This model was adapted from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) and fine-tuned on these datasets:
+- [nli_tr](https://huggingface.co/datasets/nli_tr)
+- [emrecan/stsb-mt-turkish](https://huggingface.co/datasets/emrecan/stsb-mt-turkish)
 
 ## Usage (Sentence-Transformers)
 
@@ -76,10 +83,14 @@ print(sentence_embeddings)
 
 ## Evaluation Results
 
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=atasoglu/mbert-base-cased-nli-stsb-tr)
+Achieved results on the [STS-b](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) test split are given below:
 
+```txt
+Cosine-Similarity :	    Pearson: 0.8152	Spearman: 0.8130
+Manhattan-Distance:	    Pearson: 0.8049	Spearman: 0.8128
+Euclidean-Distance:	    Pearson: 0.8049	Spearman: 0.8126
+Dot-Product-Similarity:	Pearson: 0.7878	Spearman: 0.7822
+```
 
 ## Training
 The model was trained with the parameters: