Northell/ros-classifiers

SetFit

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

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

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

Model Details

Model Description

• Model Type: SetFit

• Classification head: a OneVsRestClassifier instance

• Maximum Sequence Length: 512 tokens

• Number of Classes: 8 classes

Model Sources

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

Evaluation

Metrics

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("hasAdditionalInformation: AO, hasCreatedDate: 2024-10-08, hasCustomerHomeCountry: United States, hasCustomerID: 30642, hasCustomerName: Station Casinos LLC(Station Casinos), hasCutting: Trim to size, hasElementID: 3555960, hasElementTitle: 211696 - 381\" X 363\" SS FRONTLIT EXTERIOR VINYL SIGN , hasFinishedSizeHeight: 363, hasFinishedSizeWidth: 381, hasFscPaperBeenSpecified: No, hasInternalID: 04b8890a-dc33-4778-ad73-c1f68f68231c, hasMaterialCategory: Plastic, hasMaterialDescription: 13OZ VINYL, hasMaterialRecycledPercentage: 0%, hasMaterialThicknessOrWeight: 13, hasMaterialType: PVC, hasMaterialUnitOfMeasure: Ounces (oz), hasNumberOfVersions: 1, hasPrice: 676.0, hasPrintedSides: Not printed, hasProductCategory: Banners (synthetic), hasProofType: PDF digital proof, hasQuantity: 1, hasRecycledContentBeenOffered: N/A, hasSupplierName: WestRock Company(Westrock - 14360 - HHGSP), hasTotalColours: 4, hasUnitOfMeasure: Inches (in), ")

Training Details

Training Set Metrics

Framework Versions

• Python: 3.10.16
• SetFit: 1.1.2
• Sentence Transformers: 3.4.1
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Datasets: 3.2.0
• Tokenizers: 0.21.1

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}
}