library_name: transformers
datasets:
- bigcode/the-stack-v2
license: bigcode-openrail-m
Model Card for Model ID
Model Details
You can download the tokenizer following:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("andreagurioli1995/ModularStarEncoder")
Input should take this format when tokenized:
f"{tokenizer.sep_token}{code_snippet}{tokenizer.cls_token}"
How to use
from transformers import AutoModel
from transformers import AutoTokenizer
#import the model
model = AutoModel.from_pretrained("andreagurioli1995/ModularStarEncoder", trust_remote_code=True)
#import the tokenizer
tokenizer = AutoTokenizer.from_pretrained("andreagurioli1995/ModularStarEncoder")
code_snippet = "your code to embed here"
#You should follow this pattern to embed a snippet of code
sentence = f"{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 six elements:
- last_hidden_state: the representation of the last hidden state from the model;
- hidden_states: raw representation from all the hidden states of the model, without pooling, normalization, and projection
- loss: loss value if a ground truth is given (None if used in inference)
- prediction_logits: prediction scores from masked language modeling head
- seq_relationship_scores: prediction scores of in-context loss (concatenate multiple samples with the separator token if you want a meaningful score)
- attentions: attention scores from the encoder
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