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:
- 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 OneVsRestClassifier instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 44 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Evaluation
Metrics
Label | F1_Micro | F1_Macro | F1_Weighted | Precision | Accuracy | Recall |
---|---|---|---|---|---|---|
all | 0.6077 | 0.0781 | 0.5584 | 0.9588 | 0.9869 | 0.4448 |
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("hasCreatedDate: 2024-01-04, hasCustomerHomeCountry: United States, hasCustomerID: 14458, hasCustomerName: Lowe's Companies Inc(Lowe's FVS), hasCutting: Trim to size, hasElementID: 3044610, hasElementTitle: G284498 Commodity Moulding Profile Card 77991, hasFinishedSizeHeight: 6.875, hasFinishedSizeWidth: 3, hasFlatSizeHeight: 6.875, hasFlatSizeWidth: 3, hasFscPaperBeenSpecified: No, hasInternalID: 671d6e41-c7c2-4c42-83ff-d1c87deb890b, hasMaterialCategory: Other, hasMaterialDescription: 8PT _C1S Cover, hasMaterialType: Other, hasNumberOfVersions: 1, hasPrice: 0.01 USD, hasPrintedSides: Single sided, hasProofType: PDF digital proof, hasQuantity: 1, hasRecycledContentBeenOffered: N/A, hasSupplierName: HH IC Content Production + Development(HH IC Content Production + Development), hasTotalColours: 4, hasUnitOfMeasure: Inches (in), ")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 62 | 109.9067 | 637 |
Framework Versions
- Python: 3.10.16
- SetFit: 1.1.1
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- 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}
}
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Inference Providers
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This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model authors have turned it off explicitly.
Evaluation results
- F1_Micro on Northell/ros-classifiers-materials-flattest set self-reported0.608
- F1_Macro on Northell/ros-classifiers-materials-flattest set self-reported0.078
- F1_Weighted on Northell/ros-classifiers-materials-flattest set self-reported0.558
- Precision on Northell/ros-classifiers-materials-flattest set self-reported0.959
- Accuracy on Northell/ros-classifiers-materials-flattest set self-reported0.987
- Recall on Northell/ros-classifiers-materials-flattest set self-reported0.445