Add language; replace {MODEL_NAME} with myzens/turemb_512

Hello!

This is awesome! In this PR I propose the following:
* Specify the language metadata, this allows users to find your model if they filter for Turkish models.
* Replace "{MODEL_NAME}" with "myzens/turemb_512", this should make the snippets copy-paste ready.

- Tom Aarsen

README.md

@@ -5,7 +5,8 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+language:
+- tr
 ---
 
 # turemb_512
@@ -28,7 +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('myzens/turemb_512')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -54,8 +55,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('myzens/turemb_512')
+model = AutoModel.from_pretrained('myzens/turemb_512')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -77,7 +78,7 @@ print(sentence_embeddings)
 
 <!--- 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={MODEL_NAME})
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=myzens/turemb_512)
 
 
 ## Training
@@ -160,5 +161,4 @@ SentenceTransformer(
   journal={IEEE Sinyal Isleme ve Iletisim Uygulamalar{\i} Kurultay{\i} (SIU 2016)},
   year={2016}
 }
-```
-
+```