setfit
Efficient few-shot learning with Sentence Transformers (by huggingface)
CSL
[COLING 2022] CSL: A Large-scale Chinese Scientific Literature Dataset 中文科学文献数据集 (by ydli-ai)
| setfit | CSL | |
|---|---|---|
| 13 | 2 | |
| 1,990 | 515 | |
| 3.7% | - | |
| 9.2 | 1.9 | |
| 2 days ago | 11 months ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
setfit
Posts with mentions or reviews of setfit.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-02-05.
- FLaNK Stack 05 Feb 2024
- Smarter Summaries with Finetuning GPT-3.5 and Chain of Density
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[Discussion] Convince me that this training set contamination is fine (or not)
It did, sorry for the hasty edits! I removed that part b/c I realized that there isn't a compelling-enough reason for me to believe that text similarity is clearly inappropriate. In fact, you can train the Pr(condition | chat) classifier I suggested above using similarity training! Use SetFit for that. In the end you'll get a classifier and a similarity model.
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Ask HN: What's the best framework for text classification (few-shot learning)?
[3] https://github.com/huggingface/setfit
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Is it worth using LLMs like GPT-3 for text classification?
There's also kinda related approaches like SetFit which calculate embeddings from pretrained transformer models then then fit a classifier on top of the embeddings. I've yet to try it but it supposedly works well with very few labelled examples.
- LLMs for Text Classification (7B parameters)
- GPT-3 vs GPT-Neo / GPT-J for startup classification
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Ideas on how to improve classification and scoring using Mean Pooled Sentence Embeddings
You could have a look at setfit.
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SetFit (Sentence Transformer Fine-tuning) - Fewshot Learning without prompts [D]
Found relevant code at https://github.com/huggingface/setfit + all code implementations here
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Most Popular AI Research Sept 2022 - Ranked Based On Total GitHub Stars
Efficient Few-Shot Learning Without Prompts https://github.com/huggingface/setfit https://arxiv.org/abs/2209.11055v1
CSL
Posts with mentions or reviews of CSL.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-04.
What are some alternatives?
When comparing setfit and CSL you can also consider the following projects:
iris - Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%.
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
VToonify - [SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer
storydalle
motion-diffusion-model - The official PyTorch implementation of the paper "Human Motion Diffusion Model"
Text2Light - [SIGGRAPH Asia 2022] Text2Light: Zero-Shot Text-Driven HDR Panorama Generation
git-re-basin - Code release for "Git Re-Basin: Merging Models modulo Permutation Symmetries"