Open Source
p-tuning-v2
An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks
Our verdict
We have not reviewed p-tuning-v2 yet
No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.
From the listing
About p-tuning-v2
p-tuning-v2 is a deep prompt tuning strategy that achieves performance comparable to full fine-tuning across various model scales and tasks. It optimizes continuous prompts in the embedding space during training, enabling parameter-efficient adaptation of pre-trained language models.
Best for
Developers and researchers seeking parameter-efficient alternatives to full fine-tuning for NLP tasks
Use cases
- Adapting large pre-trained language models to downstream tasks
- Parameter-efficient fine-tuning with minimal added parameters
- Multi-task learning with shared prompt encodings
Notes
2,078 stars on GitHub. Last updated 2023-11-16. Licensed Apache-2.0.
Pros
- Achieves near fine-tuning performance with fewer trainable parameters
- Works consistently across different model sizes such as BERT and GPT
- Open-source implementation with over 2,000 GitHub stars indicates community validation
Cons
- Requires careful tuning of prompt length and learning rate for optimal results
- Less flexible than full fine-tuning for tasks needing significant architectural changes
- Primarily tested on encoder and encoder-decoder models; coverage for other architectures is limited
Key facts
What we have checked
- GitHub stars
- 2,080
Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.
Stars and usage
How p-tuning-v2 is growing
2,078 to 2,080
Sources
Where this comes from
- github api via nightly crawl, recovered from git history 20e874270887c1008f860ee0198e461a23c9e438Registry
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Changelog
What changed
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