The model is a port of our CommentBERT model from the paper:
@inproceedings{ochodek2022automated,
title={Automated code review comment classification to improve modern code reviews},
author={Ochodek, Miroslaw and Staron, Miroslaw and Meding, Wilhelm and S{\"o}der, Ola},
booktitle={International Conference on Software Quality},
pages={23--40},
year={2022},
organization={Springer}
}
The original model was implemented in Keras with two outputs - comment-purpose and subject-purpose. Here, we divided it into two separate model with one output each.
license: apache-2.0
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np
def sigmoid(x):
return 1/(1 + np.exp(-x))
classes = [
'code_design',
'code_style',
'code_naming',
'code_logic',
'code_io',
'code_data',
'code_doc',
'code_api',
'compatibility',
'rule_def',
'config_commit_patch_review',
'config_building_installing',
]
class2id = {class_:id for id, class_ in enumerate(classes)}
id2class = {id:class_ for class_, id in class2id.items()}
checkpoint = 'mochodek/bert4comment-subject'
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
text = "What do you think about making this constant?"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
logits = output.logits.detach().numpy()
scores = sigmoid(logits)
scores = (scores > 0.5).astype(int).reshape(-1)
scores_labels = [class_name for class_name in classes if scores[class2id[class_name]] == 1 ]