Update README.md

README.md

@@ -1,8 +1,9 @@
----
-library_name: transformers
-datasets:
-- bigcode/the-stack-v2
----
+---
+library_name: transformers
+datasets:
+- bigcode/the-stack-v2
+license: bigcode-openrail-m
+---
 
 # Model Card for Model ID
 
@@ -22,8 +23,44 @@ Input should take this format when tokenized:
 
 f"{tokenizer.sep_token}{code_snippet}{tokenizer.cls_token}"
 
+### How to use
+```python
+from transformers import AutoModel
+from transformers import AutoTokenizer
+
+#import the model
+model = AutoModel.from_pretrained("andreagurioli1995/ModularStarEncoder-finetuned", trust_remote_code=True)
+
+#import the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("andreagurioli1995/ModularStarEncoder-finetuned")
+
+ 
+language = "yourlanguagelowercased"
+
+#instruction in case of code embedding in a code language
+instruction_code = f"Represent this {language} code snippet for retrieval:"
+
+#instruction in case of code embedding in English
+instruction_natural_language = "Represent this code description for retrieving supporting snippets of code:"
+
+code_snippet = "your code to embed here"
+
+#You should follow this pattern to embed a snippet of code or natural language queries 
+sentence =  f"{tokenizer.sep_token}{instruction_code}{tokenizer.sep_token}{code_snippet)}{tokenizer.cls_token}"
+
+#Tokenizing your sentence
+tokenized_sensence = tokenizer(sentence, return_tensors="pt",truncation=True, max_length=2048)
+
+#Embedding the tokenized sentence
+embedded_sentence = model(**sentence)
+```
 
+You will get as an output three elements:
 
+- projected_pooled_normalized: a list of the projected, pooled, and normalized embeddings from the five exit points;
+- raw_hidden_states: raw representation from all the hidden states of the model, without pooling, normalization, and projection
+- attentions: attention scores from the encoder
+  
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->