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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
  • 'There were relatively few cases reported of attempts to involve users in service planning but their involvement in service provision was found to be more common . '
  • "At St. Mary 's Church in Ilminster , Somerset , the bells have fallen silent following a dust-up over church attendance . "
  • 'Treatment should be delayed or discontinued , or the dose reduced , in patients whose blood counts are abnormal or who have certain other side effects . '
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  • 'If you were especially helpful in a corrupt scheme you received not just cash in a bag , but equity . '
  • "Moreover , conservatives argue that it 's Justice Elena Kagan who has an ethical issue , not Scalia and Thomas . "
  • 'No one speaks , and the snaking of the ropes seems to make as much sound as the bells themselves , muffled by the ceiling . '
2
  • 'In and around all levels of government in the U.S. are groups of people who can best be described as belonging to a political insider commercial party . '
  • 'The report and a casebook of initiatives will be published in 1996 and provide the backdrop for a conference to be staged in Autumn , 1996 . '
  • 'This building shook like hell and it kept getting stronger . '
0
  • 'For months the Johns Hopkins researchers , using gene probes , experimentally crawled down the length of chromosome 17 , looking for the smallest common bit of genetic material lost in all tumor cells . '
  • 'It explains how the Committee for Medicinal Products for Human Use ( CHMP ) assessed the studies performed , to reach their recommendations on how to use the medicine . '
  • '-- Most important of all , schools should have principals with a large measure of authority over the faculty , the curriculum , and all matters of student discipline . '
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  • ': = : It is used to define a variable value . '
  • 'I could also see the clouds across the bay from the horrible fire in the Marina District of San Francisco . '
  • 'The man with the clipboard represented a halfhearted attempt to introduce a bit of les sportif into our itinerary . '
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  • "First , why ticket splitting has increased and taken the peculiar pattern that it has over the past half century : Prior to the election of Franklin Roosevelt as president and the advent of the New Deal , government occupied a much smaller role in society and the prisoner 's dilemma problem confronting voters in races for Congress was considerably less severe . "
  • 'The second quarter was more of the same , but the Alavan team opted for the inside game of Barac and the work of Eliyahu , who was greeted with whistles and applause at his return home , to continue increasing their lead by half-time ( 34-43 ) . '
  • 'In 2005 , the fear of invasion of the national territory by hordes of Polish plumbers was felt both on the Left and on the Right . '
1
  • 'Progressive education ( as it was once called ) is far more interesting and agreeable to teachers than is disciplined instruction . '
  • "Ringing does become a bit of an obsession , `` admits Stephanie Pattenden , master of the band at St. Mary Abbot and one of England 's best female ringers . "
  • "He says the neighbors complain , but I do n't believe it . "

Evaluation

Metrics

Label Accuracy
all 0.1616

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-label-v2")
# Run inference
preds = model("`` But why pay her bills ? ")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 6 25.8929 75
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: (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
0.0071 1 0.2758 -
0.3571 50 0.1622 -
0.7143 100 0.0874 -

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}
}
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Model size
109M params
Tensor type
F32
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Finetuned from

Evaluation results