THUDM/P-tuning-v2
An implementation of deep prompt tuning (P-tuning v2), a parameter-efficient fine-tuning strategy for pretrained transformer models.
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P-tuning v2 applies continuous prompts to every layer input of pretrained transformer models, enabling comparable performance to full fine-tuning with fewer trainable parameters. It targets small and medium-sized models as well as hard sequence tagging tasks. The approach was published at ACL 2022 and has been extended to text retrieval tasks in EMNLP 2023 findings.
Frequently asked
- What is THUDM/P-tuning-v2?
- An implementation of deep prompt tuning (P-tuning v2), a parameter-efficient fine-tuning strategy for pretrained transformer models.
- Is P-tuning-v2 open source?
- Yes — THUDM/P-tuning-v2 is open source, released under the Apache-2.0 license.
- What language is P-tuning-v2 written in?
- THUDM/P-tuning-v2 is primarily written in Python.
- How popular is P-tuning-v2?
- THUDM/P-tuning-v2 has 2.1k stars on GitHub.
- Where can I find P-tuning-v2?
- THUDM/P-tuning-v2 is on GitHub at https://github.com/THUDM/P-tuning-v2.