Update README.md

README.md

@@ -15,9 +15,7 @@ datasets:
 
 # emrecan/bert-base-turkish-cased-mean-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 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. The model was trained on Turkish machine translated versions of [NLI](https://huggingface.co/datasets/nli_tr) and [STS-b](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) datasets, using example [training scripts]( https://github.com/UKPLab/sentence-transformers/tree/master/examples/training) from sentence-transformers GitHub repository.
 
 ## Usage (Sentence-Transformers)
 
@@ -31,7 +29,7 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["Bu örnek bir cümle", "Her cümle vektöre çevriliyor"]
 
 model = SentenceTransformer('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
 embeddings = model.encode(sentences)
@@ -56,7 +54,7 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ["Bu örnek bir cümle", "Her cümle vektöre çevriliyor"]
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
@@ -80,12 +78,20 @@ print(sentence_embeddings)
 
 ## Evaluation Results
 
-<!--- Describe how your model was evaluated -->
+Evaluation results on test and development sets are given below:
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=emrecan/bert-base-turkish-cased-mean-nli-stsb-tr)
+| Split      | Epoch | cosine_pearson | cosine_spearman | euclidean_pearson | euclidean_spearman | manhattan_pearson | manhattan_spearman | dot_pearson | dot_spearman |
+|------------|-------|----------------|-----------------|-------------------|--------------------|-------------------|--------------------|-------------|--------------|
+| test       |   -   | 0.834          | 0.830           | 0.820             | 0.819              | 0.819             | 0.818              | 0.799       | 0.789        |
+| validation | 1     | 0.850          | 0.848           | 0.831             | 0.835              | 0.83              | 0.83               | 0.80        | 0.806        |
+| validation | 2     | 0.857          | 0.857           | 0.844             | 0.848              | 0.844             | 0.848              | 0.813       | 0.810        |
+| validation | 3     | 0.860          | 0.859           | 0.846             | 0.851              | 0.846             | 0.850              | 0.825       | 0.822        |
+| validation | 4     | 0.859          | 0.860           | 0.846             | 0.851              | 0.846             | 0.851              | 0.825       | 0.823        |
 
 
 ## Training
+Training scripts [`training_nli_v2.py`](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli_v2.py) and [`training_stsbenchmark_continue_training.py`](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py) were used to train the model.
+
 The model was trained with the parameters:
 
 **DataLoader**: