# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aieng-lab/codebert-base_comment-type-pharo")
model = AutoModelForSequenceClassification.from_pretrained("aieng-lab/codebert-base_comment-type-pharo")Quick Links
CodeBERT base for classifying code comments (multi-label)
This model classifies comments in Pharo code as 'keyimplementationpoints', 'example', 'responsibilities', 'classreferences', 'intent', 'keymessages' or 'collaborators'.
- 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_comment-type-pharo
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_comment-type-pharo")