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Add SetFit model
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: Buses are more simple - you just buy a ticket .
  - text: >-
      As citizens of village , we totally care about environment of our village
      .
  - text: >-
      So , finally I suggest that it would be a great idea to combine the
      different types of activities , both popular and the newest .
  - text: Had 12 years old .
  - text: >-
      On the other hand , I have the theoretical knowledge to use new the
      technologies this great project requires .
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: 0.13152173913043477
            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
7
  • "When I 've had a very bad and stressful day I can relax doing karate , because It 's the kind of sport that it is n't very hard ."
  • "Also , you 'll meet friendly people who usually ask to you something to be friends and change your telephone number ."
  • 'When I have spare time , I often gather my friends to watch basketball match on television .'
4
  • "stop shouting . do n't shout ."
  • 'Yours Sincerely .'
  • 'Something that they don know was that the whole thing was a movie !'
1
  • 'She stay sleeping in the bed and doing nothing all day .'
  • 'People collects trash of their house and await the trash truck that carried the trash to a landfill located outside the village .'
  • "Travelling by car is n't so much more convenient unless it is so much more comfortable , but actually we do n't think about the contamination in our planet ."
6
  • 'When the concert finished , we went to cloakroom to get signatures from musicians .'
  • 'We have solar panels and a place to make compost at the last garden , with worms who eat and degrade all the organic waste of the school .'
  • 'The aim of this report is to give you my personal point of view of the course I did in your branch in Madrid last month .'
5
  • 'You can also bought a lot of gifts like key chains , statue , or what else memories to be made before returning to Malaysia .'
  • 'I always said that I passed that test and I was sure of that .'
  • 'In addition , to decrease the risk of negative comments or posts , Facebook and Twitter would improve their futures to solve the less personal privacy problem .'
2
  • 'They were not only really clever people but also excellent co - workers .'
  • 'On balance , learning foreign languages is very positive on different aspect , so if you have the positivity of learning a new language do it , because it will bring you many benefits .'
  • 'In many years of work I have honed my skills in managing non - standard situations , analyzing the problem , finding and implementing practical and easy solutions .'
0
  • 'It is very funny .'
  • 'In China , English is took to be a foreign language which many students choose to learn .'
  • 'We also value that they have specialised studies in Cloud technology , and hosting management .'
3
  • "Usually there are generation problems , sons do n't understand parents and vicecersa , but dialoging and listening emotions and facts , everyone can have another point of view ."
  • 'the two boys heard that he was planing to steal some money and kill people so the boys start their adventure on stoping Injuin Joe ...'
  • 'As an example , if you are able to get alone with your travel companion could enjoy each moment of the trip , exchange some pictures , eat together , and visit places with common interest such as museums or malls .'

Evaluation

Metrics

Label Accuracy
all 0.1315

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/BEA2019-multi-class-4")
# Run inference
preds = model("Had 12 years old .")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 19.1562 42
Label Training Sample Count
0 4
1 4
2 4
3 4
4 4
5 4
6 4
7 4

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
0.0125 1 0.1886 -
0.625 50 0.0778 -
1.25 100 0.0194 -
1.875 150 0.0069 -

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}
}