# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aieng-lab/codebert-base_review-aspect")
model = AutoModelForSequenceClassification.from_pretrained("aieng-lab/codebert-base_review-aspect")Quick Links
CodeBERT base for classifying API reviews
This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'.
- Developed by: Fabian C. Peña, Steffen Herbold
- Finetuned from: microsoft/codebert-base
- Replication kit: https://github.com/aieng-lab/senlp-benchmark
- Language: English
- License: MIT
Citation
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
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Model tree for aieng-lab/codebert-base_review-aspect
Base model
microsoft/codebert-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aieng-lab/codebert-base_review-aspect")