pattabaa/setfit-materials-vanities

SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

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

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

Model Details

Model Description

• —Model Type: SetFit
• —Sentence Transformer body: sentence-transformers/all-MiniLM-L6-v2
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 256 tokens
• —Number of Classes: 3 classes <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

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

Model Labels

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("Title: Felicity 60\" Double Bathroom Vanity Set Base Finish: Natural Ash, Top Finish: Pure White Matte Description: Bring mid-century style and modern functionality to your bathroom with this striking vanity. Real ash veneers add an airy warmth to the solid wood frame. The pure white quartz top is both sleek and functional. Soft-closing doors and drawers provide smooth access to ample storage space. Features: {'list': array([{'element': 'The pulls are as follows:\nLarge: 0.04\" x 0.98\" x 12.52\" - Space between holes 11,61\"\nSmall: 0.59\" x 0.98\" x 7.52\" - Space between holes 6,69\"'}],
      dtype=object)}")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (16, 16)
• —num_epochs: (1, 1)
• —max_steps: -1
• —sampling_strategy: oversampling
• —bodylearningrate: (2e-05, 1e-05)
• —headlearningrate: 0.01
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: False
• —warmup_proportion: 0.1
• —l2_weight: 0.01
• —seed: 42
• —evaluation_strategy: epoch
• —evalmaxsteps: -1
• —loadbestmodelatend: True

Training Results

Framework Versions

• —Python: 3.12.0
• —SetFit: 1.1.3
• —Sentence Transformers: 3.4.1
• —Transformers: 4.57.6
• —PyTorch: 2.10.0
• —Datasets: 4.5.0
• —Tokenizers: 0.22.2

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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