timoschick/pet

• highabout#1

  Clarify the project's full name and domain in the 'About' description

  原因：

  当前

  This repository contains the code for "Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference"

  复制粘贴的修复

  复制

  Code for Pattern-Exploiting Training (PET), a semi-supervised method for few-shot text classification and natural language inference.

  待办完成忽略
• highreadme#2

  Emphasize semi-supervised learning and unlabeled data in the README's introduction

  原因：

  当前

  This repository contains the code for Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference and It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. The papers introduce pattern-exploiting training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases. In low-resource settings, PET and iPET significantly outperform regular supervised training, various semi-supervised baselines and even GPT-3 despite requiring 99.9% less parameters. The iterative variant of PET (iPET) trains multiple generations of models and can even be used without any training data.

  复制粘贴的修复

  复制

  This repository contains the code for Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference and It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. The papers introduce Pattern-Exploiting Training (PET), a powerful semi-supervised training procedure that reformulates input examples as cloze-style phrases. PET and its iterative variant (iPET) are particularly effective for leveraging unlabeled data in low-resource settings, significantly outperforming regular supervised training and various semi-supervised baselines for tasks like text classification and natural language inference, even without any training data.

  待办完成忽略
• mediumtopics#3

  Add more specific topics to improve discoverability for core methodologies

  原因：

  当前

  machine-learning, nlp, python

  复制粘贴的修复

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  machine-learning, nlp, python, few-shot-learning, semi-supervised-learning, text-classification, natural-language-inference

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  What are effective methods for few-shot text classification in low-resource settings?

  你：第 2 位
  AI 推荐顺序：

  1. SetFit
  2. PET ← 你
  3. P-tuning v2
  4. AdapterHub
  5. BERT
  6. RoBERTa
  7. Sentence-BERT
  8. XLM-R
  9. Logistic Regression
  10. SVM
  11. MLP
  12. GPT-3.5
  13. GPT-4
  14. SimCSE
  查看 AI 完整回答
• 品类问题

  How to leverage unlabeled data for natural language inference without much supervision?

  你：未被推荐
  AI 推荐顺序：

  1. SetFit
  2. Snorkel
  3. Hugging Face Transformers
  4. Sentence Transformers
  5. Hugging Face Transformers
  6. modAL

  AI 推荐了 6 个替代方案，却始终没点名 timoschick/pet。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of timoschick/pet?

  pass
  ✓AI 明确点名了 timoschick/pet

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts timoschick/pet in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 timoschick/pet

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo timoschick/pet solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 timoschick/pet

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？