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
6
  • 'If you were especially helpful in a corrupt scheme you received not just cash in a bag , but equity . '
  • 'Two American companies reached deals for fields auctioned in June . '
  • 'Let me prove it , Phil . '
2
  • 'This building shook like hell and it kept getting stronger . '
  • 'Now you could ask me , why should the user mind about MathML ? '
  • '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 . '
3
  • 'The tumor , he suggested , developed when the second , normal copy also was damaged . '
  • 'Proper English bells are started off in rounds , from the highest-pitched bell to the lowest -- a simple descending scale using , in larger churches , as many as 12 bells . '
  • 'Treatment should be delayed or discontinued , or the dose reduced , in patients whose blood counts are abnormal or who have certain other side effects . '
5
  • 'Schools that are structured in this way produce students with higher morale and superior academic performance . '
  • 'I got home , let the dogs into the house and noticed some sounds above my head , as if someone were walking on the roof , or upstairs . '
  • 'Give me your address . '
0
  • '-- 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 . '
  • '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 . '
4
  • '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 . '
  • 'Cerenia contains the active substance maropitant and is available as tablet or as solution for injection . '
  • '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 ) . '
1
  • 'The sound of bells is a net to draw people into the church , he says . '
  • 'Progressive education ( as it was once called ) is far more interesting and agreeable to teachers than is disciplined instruction . '
  • "The defense lawyers also claim , for example , that Mr. Hayes may have been prejudiced when Judge Blue declined to allow them to test potential jurors ' reactions by showing them grisly crime-scene photographs during jury selection . "

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-v1-10")
# Run inference
preds = model("`` But why pay her bills ? ")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 5 25.8286 75
Label Training Sample Count
0 10
1 10
2 10
3 10
4 10
5 10
6 10

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (4, 4)
  • 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
0.0057 1 0.2314 -
0.2857 50 0.218 -
0.5714 100 0.1161 -
0.8571 150 0.0559 -
1.1429 200 0.0087 -
1.4286 250 0.0029 -
1.7143 300 0.001 -
2.0 350 0.0006 -
2.2857 400 0.0011 -
2.5714 450 0.0009 -
2.8571 500 0.0005 -
3.1429 550 0.0006 -
3.4286 600 0.0004 -
3.7143 650 0.0003 -
4.0 700 0.0005 -

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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