Instructions to use aieng-lab/roberta-large_review-aspect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aieng-lab/roberta-large_review-aspect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aieng-lab/roberta-large_review-aspect")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aieng-lab/roberta-large_review-aspect") model = AutoModelForSequenceClassification.from_pretrained("aieng-lab/roberta-large_review-aspect") - Notebooks
- Google Colab
- Kaggle
RoBERTa large 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: roberta-large
- 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/roberta-large_review-aspect
Base model
FacebookAI/roberta-large