This article provides a framework for selecting and fine-tuning models from Hugging Face, a platform that now hosts over two million models. It guides users through the decision-making process, offering insights beyond the standard documentation. The framework aims to help users navigate the vast selection of models available, including popular architectures like Bert, GPT-2, Roberta, XLM-RoBERTa, and T5. AI
IMPACT Offers guidance for developers on selecting and fine-tuning models from a large repository, potentially improving efficiency in AI development.
RANK_REASON The article provides a framework for using existing tools and models, rather than announcing a new frontier release or significant industry shift.
Read on Medium — fine-tuning tag →
- Bert
- GPT-2
- Hugging Face
- Keras
- PyTorch
- Roberta
- T5 Text To Text Transfer Transformer
- Tensorflow
- transformers
- XLM-RoBERTa
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