Instructions to use aieng-lab/t5-base_bug-issue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aieng-lab/t5-base_bug-issue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aieng-lab/t5-base_bug-issue")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aieng-lab/t5-base_bug-issue") model = AutoModelForSequenceClassification.from_pretrained("aieng-lab/t5-base_bug-issue") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- t5-base
pipeline_tag: text-classification
T5 base for classifying bug issues
This model classifies GitHub issues as 'bug' or 'not a bug'.
- Developed by: Fabian C. Peña, Steffen Herbold
- Finetuned from: t5-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}
}