timoschick/pet

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1622 stars · 281 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official implementation of Pattern-Exploiting Training (PET), a semi-supervised method that reformulates text classification and natural language inference tasks as cloze-style prompts for pretrained language models. It includes the iterative iPET variant and supports training with very few or even zero labeled examples.

Use cases

• few-shot text classification with pretrained language models
• natural language inference with limited labeled data
• train a classifier with zero labeled examples using iPET
• reproduce GPT-3-level few-shot results with small models
• apply prompt-based semi-supervised training to custom NLP tasks

When to choose

• you have very few labeled examples for a classification or NLI task
• you want to use small language models instead of massive few-shot models
• you need the reference implementation of the PET/iPET research papers

When to avoid

• you need modern prompt-tuning support or recent transformer versions
• you want actively maintained tooling rather than research code
• your task is not text classification or natural language inference

Facets

library · maturity maintenance

machine-learning nlp llm-training cli machine-learning large-language-models python cli few-shot-learning prompt-based-training cloze-questions semi-supervised text-classification natural-language-inference ipet research-code natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="timoschick/pet")