Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Domenes adaptācija× | Pārneses apmācība× | |
|---|---|---|
| Nozare≠ | Teksta ieguve | Mašīnmācīšanās |
| Saime≠ | Process / pipeline | Machine learning |
| Izcelsmes gads≠ | — | 2010 (formalized); 1990s (early roots) |
| Autors≠ | — | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Tips≠ | NLP transfer-learning / fine-tuning pipeline | Learning paradigm |
| Pirmavots≠ | Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics. DOI ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Citi nosaukumi≠ | Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | Domain adaptation is a natural-language-processing technique that takes a general pretrained language model and fine-tunes it on target-domain data so that it performs better in specialised fields such as medicine, law, and finance. It builds on the transfer-learning ideas behind work like Blitzer et al. (2007) on cross-domain sentiment classification and Lee et al. (2020) on the biomedical BioBERT model. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
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