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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      A gentle nudge to complete the healthcare webinar questionnaire sent last
      week.
  - text: Sudden severe chest pain, suspecting a cardiac emergency.
  - text: >-
      Annual physical examination due in Tuesday, March 05. Please book an
      appointment.
  - text: Please confirm your attendance at the lifestyle next month.
  - text: Could you verify your emergency contact details in our records?
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.85
            name: Accuracy

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-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. 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 Sources

Model Labels

Label Examples
2
  • 'Rapid onset of confusion and weakness, urgent evaluation needed.'
  • 'Unconscious patient found, immediate medical response required.'
  • 'Urgent: Suspected heart attack, immediate medical attention required.'
1
  • 'Reminder: Your dental check-up is scheduled for Monday, February 05.'
  • 'Reminder: Your dental check-up is scheduled for Saturday, February 24.'
  • 'Nutritionist appointment reminder for Sunday, January 21.'
0
  • 'Could you verify your lifestyle contact details in our records?'
  • 'Kindly update your emergency contact list at your earliest convenience.'
  • 'We request you to update your wellness information for our records.'

Evaluation

Metrics

Label Accuracy
all 0.85

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("konsman/setfit-messages-generated-test")
# Run inference
preds = model("Sudden severe chest pain, suspecting a cardiac emergency.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 7 10.125 12
Label Training Sample Count
0 16
1 16
2 16

Training Hyperparameters

  • batch_size: (8, 8)
  • num_epochs: (2, 2)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 40
  • body_learning_rate: (2.2041595048800003e-05, 2.2041595048800003e-05)
  • head_learning_rate: 2.2041595048800003e-05
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0021 1 0.2841 -
0.1042 50 0.0603 -
0.2083 100 0.0017 -
0.3125 150 0.0003 -
0.4167 200 0.0004 -
0.5208 250 0.0003 -
0.625 300 0.0003 -
0.7292 350 0.0002 -
0.8333 400 0.0003 -
0.9375 450 0.0001 -
1.0417 500 0.0002 -
1.1458 550 0.0003 -
1.25 600 0.0002 -
1.3542 650 0.0002 -
1.4583 700 0.0001 -
1.5625 750 0.0002 -
1.6667 800 0.0001 -
1.7708 850 0.0001 -
1.875 900 0.0001 -
1.9792 950 0.0002 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.2.2
  • Transformers: 4.35.2
  • PyTorch: 2.1.0+cu121
  • Datasets: 2.16.1
  • Tokenizers: 0.15.0

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}
}