Update README.md

README.md

@@ -20,7 +20,10 @@ library_name: sentence-transformers
 
 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 originates from the [Camel-Bert_Classical Arabic](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca) model. It was then trained on the Jawami' Kalim dataset, 
+specifically a dataset of 440,000 matns and their corresponding taraf labels. 
+Taraf labels indicate two hadith are about the same report, and as such, are more semantically similar.
+
 
 ## Usage (Sentence-Transformers)
 
@@ -34,7 +37,7 @@ 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 = ["أنا أحب القراءة والكتابة.", "الطيور تحلق في السماء."]
 
 model = SentenceTransformer('FDSRashid/QulBERT')
 embeddings = model.encode(sentences)
@@ -59,7 +62,7 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ["أنا أحب القراءة والكتابة.", "الطيور تحلق في السماء."]
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('FDSRashid/QulBERT')
@@ -83,9 +86,21 @@ print(sentence_embeddings)
 
 ## Evaluation Results
 
-<!--- Describe how your model was evaluated -->
+he dataset was split into 75% training,  15% eval, 10% test.
+
+
+
+Validation Results during Training:
+
+
+Binary Classification Evaluation:
+
+
+
+Triplet Evaluation:
+
+
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
 
 
 ## Training