SetFit

This is a SetFit model that can be used for Text Classification. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 3 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
1	'Can you identify specific areas that need improvement in my text' 'Point out the flaws in my writing style, please' 'Which parts of my draft are the weakest'
0	"How do I make my character's driving force more compelling" "Any tips to deepen my protagonist's underlying goals" "Suggestions for strengthening the reasons behind my character's actions"
2	'How does the Pro version elevate my writing experience' 'Could you list the premium perks of Quarkle Pro' 'What special advantages come with upgrading to Pro'

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("How do I handle flashbacks in a non-linear story")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	8.7947	14

Label	Training Sample Count
0	153
1	144
2	117

Framework Versions

Python: 3.10.15
SetFit: 1.2.0.dev0
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1
Datasets: 3.2.0
Tokenizers: 0.21.0

Citation

BibTeX

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}