setfit-tutorial / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      has the chops of a smart-aleck film school brat and the imagination of a
      big kid ... 
  - text: 'that ''s truly deserving of its oscar nomination '
  - text: 'instead gets ( sci-fi ) rehash '
  - text: 'career-defining revelation '
  - text: 'is ultimately about as inspiring as a hallmark card . '
pipeline_tag: text-classification
inference: true
co2_eq_emissions:
  emissions: 0.05983916547782622
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
  ram_total_size: 251.49160385131836
  hours_used: 0.001
base_model: sentence-transformers/paraphrase-mpnet-base-v2

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
0
  • 'stale and uninspired . '
  • "the film 's considered approach to its subject matter is too calm and thoughtful for agitprop , and the thinness of its characterizations makes it a failure as straight drama . ' "
  • "that their charm does n't do a load of good "
1
  • "broomfield is energized by volletta wallace 's maternal fury , her fearlessness "
  • 'flawless '
  • 'insightfully written , delicately performed '

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("hojzas/setfit-tutorial")
# Run inference
preds = model("career-defining revelation ")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 11.4375 33
Label Training Sample Count
0 8
1 8

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • body_learning_rate: (2e-05, 2e-05)
  • head_learning_rate: 2e-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
0.025 1 0.2176 -

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Carbon Emitted: 0.000 kg of CO2
  • Hours Used: 0.001 hours

Training Hardware

  • On Cloud: No
  • GPU Model: No GPU used
  • CPU Model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
  • RAM Size: 251.49 GB

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.2.2
  • Transformers: 4.36.1
  • PyTorch: 2.1.2+cu121
  • Datasets: 2.14.7
  • 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}
}