setfit-bbc-news / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: UK's National Gallery in the pink
  - text: Regiments' group in poll move
  - text: Ad firm WPP's profits surge 15%
  - text: Ivanovic seals Canberra victory
  - text: Text message record smashed
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: 0.8878923766816144
            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
4
  • 'PC photo printers challenge pros'
  • 'Doors open at biggest gadget fair'
  • 'Progress on new internet domains'
0
  • 'US interest rate rise expected'
  • 'Russian oil merger excludes Yukos'
  • 'Budget Aston takes on Porsche'
2
  • 'Benitez issues warning to Gerrard'
  • 'Wenger steps up row'
  • 'Mansfield 0-1 Leyton Orient'
3
  • 'Observers to monitor UK election'
  • "Blair 'up for it' ahead of poll"
  • "Hospital suspends 'no Welsh' plan"
1
  • 'Oscars race enters final furlong'
  • "Campaigners attack MTV 'sleaze'"
  • 'Fightstar take to the stage'

Evaluation

Metrics

Label Accuracy
all 0.8879

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("Kevinger/setfit-bbc-news")
# Run inference
preds = model("Text message record smashed")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 4 4.5625 7
Label Training Sample Count
0 16
1 16
2 16
3 16
4 16

Training Hyperparameters

  • batch_size: (16, 2)
  • num_epochs: (1, 16)
  • max_steps: -1
  • sampling_strategy: oversampling
  • 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
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0031 1 0.3227 -
0.1562 50 0.0136 -
0.3125 100 0.0024 -
0.4688 150 0.0013 -
0.625 200 0.001 -
0.7812 250 0.0009 -
0.9375 300 0.001 -

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}
}