uds-lsv/bert-stable-fine-tuning
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
This project helps machine learning engineers and NLP researchers improve the reliability of fine-tuning large language models like BERT. It takes your existing fine-tuning setup for BERT, RoBERTa, or ALBERT models and helps you achieve more consistent, less variable task performance. The output is a more stable fine-tuned model, reducing the performance differences caused by random initializations.
138 stars. No commits in the last 6 months.
Use this if you are a machine learning engineer or researcher experiencing inconsistent performance when fine-tuning BERT-based models for natural language processing tasks.
Not ideal if you are not working with transformer-based language models or if your primary concern is model performance rather than stability across different training runs.
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138
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21
Language
Python
License
Apache-2.0
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Last pushed
Sep 06, 2023
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