Update README.md

README.md

@@ -8,11 +8,15 @@ tags:
 
 ---
 
-# {MODEL_NAME}
+# GitHub Issues Preprocessed MPNet Sentence Transformer (10 Epochs)
 
-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.
+This is a [sentence-transformers](https://www.SBERT.net) model, specific for GitHub Issue data.
+
+## Dataset
+
+For training, we used the [NLBSE22 dataset](https://nlbse2022.github.io/tools/), after removing issues with empty body and duplicates.
+Similarity between title and body was used to train the sentence embedding model.
 
-<!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
 
@@ -54,8 +58,8 @@ 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('Collab-uniba/github-issues-preprocessed-mpnet-st-e10')
+model = AutoModel.from_pretrained('Collab-uniba/github-issues-preprocessed-mpnet-st-e10')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')