Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uainishaji wa Hali-ya-Sifuri× | Uainishaji wa maandishi kwa michache ya mifano (Few-Shot Text Classification)× | |
|---|---|---|
| Nyanja | Uchimbaji wa Matini | Uchimbaji wa Matini |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2019 | — |
| Mwanzilishi≠ | Yin, Hay & Roth | — |
| Aina≠ | NLP text-classification task | NLP text-classification task (low-resource) |
| Chanzo asilia≠ | Yin, W., Hay, J. & Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. EMNLP, 3914-3923. DOI ↗ | Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL. DOI ↗ |
| Majina mbadala≠ | zero-shot text classification, entailment-based classification, Sıfır Atışlı Sınıflandırma (Zero-Shot Classification) | few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot) |
| Zinazohusiana≠ | 3 | 4 |
| Muhtasari≠ | Zero-shot classification is a natural-language-processing task that assigns text to categories described in plain language without requiring any labelled training data. Formalised as an entailment problem by Yin, Hay and Roth (2019), it lets a large pretrained language model recognise new categories on the fly simply by naming them, enabling rapid adaptation to fresh label sets. | Few-shot text classification assigns documents to classes using only a handful of labelled examples per class. Building on advances by Gao et al. (2021) and the prompt-free SetFit approach of Tunstall et al. (2022), it leans on prototypical networks, MAML, or fine-tuning of a large pretrained model to learn from scarce labels. |
| ScholarGateSeti ya data ↗ |
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