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On the recordRead 7 Oct 202616 facts
OpenPrompt
Plans and prices
OpenPrompt
Free
Open-source prompt-learning framework · Python 3.8+ and PyTorch 1.8.1+ listed as tested requirements
Read 7 Oct 2026 · github.com
On the record
- Core components
- A PromptModel combines a pretrained language model with one or more templates and verbalizers for training and inference.github.com · read 7 Oct 2026
- Custom research
- The framework is designed to let users develop and experiment with their own prompt-learning methods.github.com · read 7 Oct 2026
- Datasets
- The project provides scripts for downloading benchmark datasets.github.com · read 7 Oct 2026
- Documentation caveat
- The README says some documentation may be outdated following major changes to the project.github.com · read 7 Oct 2026
- Installation
- The README provides pip installation with `pip install openprompt` and instructions to install from the GitHub source.github.com · read 7 Oct 2026
- Intended users
- The package metadata lists developers, education, and science or research as intended audiences.github.com · read 7 Oct 2026
- License
- The repository identifies its license as Apache-2.0.github.com · read 7 Oct 2026
- Maintainer organization
- THUNLP identifies itself as the Natural Language Processing Lab at Tsinghua University in Beijing.github.com · read 7 Oct 2026
- Model integration
- OpenPrompt supports loading pretrained language models from Hugging Face Transformers.github.com · read 7 Oct 2026
- Prompt methods
- It implements prompting methods for templating, verbalizing, and optimization in a unified framework.github.com · read 7 Oct 2026
- Purpose
- OpenPrompt is an open-source framework for building prompt-learning pipelines for downstream NLP tasks.github.com · read 7 Oct 2026
- Runtime requirements
- The README says the repository is tested on Python 3.8+ and PyTorch 1.8.1+.github.com · read 7 Oct 2026
- Task support
- The documentation lists data processors for text classification, entity typing, relation classification, language inference, and conditional generation.thunlp.github.io · read 7 Oct 2026
- Template options
- The documentation lists manual, prefix, P-tuning, PTR, and mixed templates.thunlp.github.io · read 7 Oct 2026
- Verbalizer options
- The documentation lists one-to-one, manual, automatic, knowledgeable, PTR, generation, and soft verbalizers.thunlp.github.io · read 7 Oct 2026
