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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      The Alavas worked themselves to the bone in the last period , and English
      and San Emeterio ( 65-75 ) had already made it clear that they were not
      going to let anyone take away what they had earned during the first thirty
      minutes . 
  - text: 'To break the uncomfortable silence , Haney began to talk . '
  - text: >-
      For the treatment of non-small cell lung cancer , the effects of Alimta
      were compared with those of docetaxel ( another anticancer medicine ) in
      one study involving 571 patients with locally advanced or metastatic
      disease who had received chemotherapy in the past . 
  - text: >-
      As we all know , a few minutes before the end of the game ( that their
      team had already won ) , both players deliberately wasted time which made
      the referee show the second yellow card to both of them . 
  - text: >-
      In contrast , patients whose cancer was affecting squamous cells had
      shorter survival times if they received Alimta . 
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: 0.17086092715231788
            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 SetFitHead 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
3
  • 'Saves the log with all results into an HTML file to be able to print or publish . '
  • 'Alimta is used together with cisplatin ( another anticancer medicine ) when the cancer is unresectable ( cannot be removed by surgery alone ) and malignant ( has spread , or is likely to spread easily , to other parts of the body ) , in patients who have not received chemotherapy ( medicines for cancer ) before advanced or metastatic non-small cell lung cancer that is not affecting the squamous cells . '
  • "It was the most exercise we 'd had all morning and it was followed by our driving immediately to the nearest watering hole . "
6
  • '3 -RRB- Republican congressional representatives , because of their belief in a minimalist state , are less willing to engage in local benefit-seeking than are Democratic members of Congress . '
  • 'Here , the experience of New York City is decisive . '
  • 'The idea would be to administer to patients the growth-controlling proteins made by healthy versions of the damaged genes . '
2
  • '-- Students should move up the educational ladder as their academic potential allows . '
  • 'The next day , Sunday , the hangover reminded Haney where he had been the night before . '
  • 'It explains how the Committee for Medicinal Products for Veterinary Use ( CVMP ) assessed the studies performed , to reach their recommendations on how to use the medicine . '
0
  • 'A minor contrast to Costa Rica , comparing the 22 players called by both countries for the friendly game today , at 3:05 pm at the National Stadium in San Jose . '
  • 'Never in my life have I been so frightened . '
  • 'Prior to 1932 , the pattern was nearly the opposite . '
5
  • '2 -RRB- Congressional representatives have two basic responsibilities while voting in office -- dealing with national issues -LRB- programmatic actions such as casting roll call votes on legislation that imposes costs and/or confers benefits on the population at large -RRB- and attending to local issues -LRB- constituency service and pork barrel -RRB- . '
  • 'The scientists say that since breast cancer often strikes multiple members of certain families , the gene , when inherited in a damaged form , may predispose women to the cancer . '
  • "On the Right , the tone was set by Jacques Chirac , who declared in 1976 that 900,000 unemployed would not become a problem in a country with 2 million of foreign workers , '' and on the Left by Michel Rocard explaining in 1990 that France can not accommodate all the world 's misery . '' "
4
  • 'Researchers say the inactivation of tumor-suppressor genes , alone or in combination , appears crucial to the development of such scourges as cancer of the brain , the skin , kidney , prostate , and cervix . '
  • 'One writer , signing his letter as Red-blooded , balanced male , remarked on the frequency of women fainting in peals , and suggested that they settle back into their traditional role of making tea at meetings . '
  • 'To ring for even one service at this tower , we have to scrape , says Mr. Hammond , a retired water-authority worker . `` '
1
  • "kalgebra 's console is useful as a calculator . "
  • 'Mr. Neuberger realized that , although of Italian ancestry , Mr. Mariotta still could qualify as a minority person since he was born in Puerto Rico . '
  • "Biggest trouble was scared family who could n't get a phone line through , and spent a really horrible hour not knowing . "

Evaluation

Metrics

Label Accuracy
all 0.1709

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("HelgeKn/SemEval-multi-class-8")
# Run inference
preds = model("To break the uncomfortable silence , Haney began to talk . ")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 4 27.0 74
Label Training Sample Count
0 8
1 8
2 8
3 8
4 8
5 8
6 8

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (2, 2)
  • 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
0.0071 1 0.2786 -
0.3571 50 0.1703 -
0.7143 100 0.0932 -
1.0714 150 0.0173 -
1.4286 200 0.0048 -
1.7857 250 0.0024 -

Framework Versions

  • Python: 3.9.13
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.36.0
  • PyTorch: 2.1.1+cpu
  • Datasets: 2.15.0
  • 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}
}