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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
widget:
  - text: 'VALID THRU 02/10/2015 '
  - text: Les chaines Universal+
  - text: "\tminute = second * 60,"
  - text: ': Session'
  - text: Phil Klay
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: 0.87
            name: Accuracy
          - type: precision
            value: 0.8452380952380952
            name: Precision
          - type: recall
            value: 0.8452380952380952
            name: Recall
          - type: f1
            value: 0.8452380952380952
            name: F1

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
True
  • 'ace} UNIV FEATURING CHAMILLIONAIRE ©) '
  • 'ee — Pra yon t “pink you are S© Mer '
  • 'ae PTAA Be hs B corms of Rermee '
False
  • 'mmmmmmmmmmlli'
  • 'Manage Cookie Preferences'
  • 'Supply Partners'

Evaluation

Metrics

Label Accuracy Precision Recall F1
all 0.87 0.8452 0.8452 0.8452

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(": Session")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 8.4575 265
Label Training Sample Count
False 384
True 416

Training Hyperparameters

  • batch_size: (16, 2)
  • num_epochs: (1, 16)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • seed: 42
  • run_name: PG-OCR-test-2
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0005 1 0.4077 -
0.025 50 0.2689 -
0.05 100 0.2505 -
0.075 150 0.1787 -
0.1 200 0.1602 -
0.125 250 0.1381 -
0.15 300 0.1753 -
0.175 350 0.018 -
0.2 400 0.024 -
0.225 450 0.0863 -
0.25 500 0.0504 -
0.275 550 0.0204 -
0.3 600 0.0124 -
0.325 650 0.066 -
0.35 700 0.1305 -
0.375 750 0.0599 -
0.4 800 0.0323 -
0.425 850 0.0039 -
0.45 900 0.0131 -
0.475 950 0.004 -
0.5 1000 0.0016 -
0.525 1050 0.0139 -
0.55 1100 0.0189 -
0.575 1150 0.0533 -
0.6 1200 0.0645 -
0.625 1250 0.0005 -
0.65 1300 0.0111 -
0.675 1350 0.002 -
0.7 1400 0.0082 -
0.725 1450 0.0009 -
0.75 1500 0.0018 -
0.775 1550 0.0003 -
0.8 1600 0.0108 -
0.825 1650 0.0009 -
0.85 1700 0.0003 -
0.875 1750 0.0009 -
0.9 1800 0.0038 -
0.925 1850 0.0406 -
0.95 1900 0.0012 -
0.975 1950 0.0024 -
1.0 2000 0.0004 -

Framework Versions

  • Python: 3.11.0
  • SetFit: 1.0.3
  • Sentence Transformers: 2.3.0
  • Transformers: 4.37.2
  • PyTorch: 2.2.1+cu121
  • Datasets: 2.16.1
  • Tokenizers: 0.15.1

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}
}