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