library_name: transformers
datasets:
- bigcode/the-stack-v2
license: bigcode-openrail-m
Model Card for Model ID
Model Details
How to use
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
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