update: readme.md

README.md

@@ -8,32 +8,8 @@ tags:
 
 ---
 
-# {MODEL_NAME}
-
-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 -->
-
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
-```
-pip install -U sentence-transformers
-```
-
-Then you can use the model like this:
-
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
+## SARBERT for ArabicQ2Q
+This model was trained using [sentence-transformers](https://www.SBERT.net) library, it uses [ARBERT](https://huggingface.co/UBC-NLP/ARBERT) as its base for generating word embeddings which were tuned using the [Semantic Question Similarity in Arabic dataset](http://nsurl.org/2019-2/tasks/task8-semantic-question-similarity-in-arabic/)
 
 ## Usage (HuggingFace Transformers)
 Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
@@ -51,11 +27,11 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ['أين ولد أبو نواس؟	', 'أين عاش أبو نواس؟']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('nehalelkaref/SARBERT-for-ArQ2Q')
+model = AutoModel.from_pretrained('nehalelkaref/SARBERT-for-ArQ2Q')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')