An Introduction to Transfer Learning in NLP and HuggingFace
summary
Survey of how large pretrained transformer models (BERT/GPT/RoBERTa/T5 and distilled variants) use sequential transfer learning: large-scale self‑supervised language modeling then fine‑tuning with a small task head. HuggingFace releases the libraries Transformers, Tokenizers, Datasets, a model hub with ~2–3k checkpoints, a tokenizer implemented in Rust for speed, and datasets with on‑disk zero‑copy. Practical result: a small GLUE‑style set of 2,500 examples reaches >90% after one epoch and down to 3.6% error after three epochs. Limitations: model size, catastrophic forgetting, and out‑of‑domain generalization.
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chapters (7)
- 0:00Intro
- 0:45What is Transfer Learning
- 7:40Text Classification
- 11:53Limitations
- 13:58Open Source Libraries
- 18:22Questions
- 23:39Transformer
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