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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      we get some truly unique character studies and a cross-section of
      americana that hollywood could n't possibly fictionalize and be believed .
  - text: >-
      the movie is one of the best examples of artful large format filmmaking
      you are likely to see anytime soon .
  - text: my response to the film is best described as lukewarm .
  - text: >-
      the movie 's ripe , enrapturing beauty will tempt those willing to probe
      its inscrutable mysteries .
  - text: >-
      fear dot com is so rambling and disconnected it never builds any suspense
      .
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: 0.5380090497737556
            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
0
  • "it 's not a motion picture ; it 's an utterly static picture ."
  • "frankly , it 's kind of insulting , both to men and women ."
  • 'under-rehearsed and lifeless'
2
  • "recoing 's fantastic performance does n't exactly reveal what makes vincent tick , but perhaps any definitive explanation for it would have felt like a cheat ."
  • "do n't expect any subtlety from this latest entry in the increasingly threadbare gross-out comedy cycle ."
  • "merry friggin ' christmas !"
3
  • "so purely enjoyable that you might not even notice it 's a fairly straightforward remake of hollywood comedies such as father of the bride ."
  • "what saves this deeply affecting film from being merely a collection of wrenching cases is corcuera 's attention to detail ."
  • 'for once , a movie does not proclaim the truth about two love-struck somebodies , but permits them time and space to convince us of that all on their own .'
1
  • "the fact that it is n't very good is almost beside the point ."
  • 'what starts off as a satisfying kids flck becomes increasingly implausible as it races through contrived plot points .'
  • 'the film is ultimately about as inspiring as a hallmark card .'
4
  • 'cool gadgets and creatures keep this fresh .'
  • 'morton deserves an oscar nomination .'
  • 'a brutal and funny work .'

Evaluation

Metrics

Label Accuracy
all 0.5380

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("vidhi0206/setfit-paraphrase-mpnet-sst5_v2")
# Run inference
preds = model("my response to the film is best described as lukewarm .")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 18.8062 52
Label Training Sample Count
0 64
1 64
2 64
3 64
4 64

Training Hyperparameters

  • batch_size: (8, 8)
  • 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
0.0006 1 0.2259 -
0.0312 50 0.2373 -
0.0625 100 0.1726 -
0.0938 150 0.1607 -
0.125 200 0.1869 -
0.1562 250 0.1863 -
0.1875 300 0.224 -
0.2188 350 0.1625 -
0.25 400 0.1284 -
0.2812 450 0.1357 -
0.3125 500 0.2193 -
0.3438 550 0.1434 -
0.375 600 0.0524 -
0.4062 650 0.0558 -
0.4375 700 0.072 -
0.4688 750 0.0312 -
0.5 800 0.0732 -
0.5312 850 0.0117 -
0.5625 900 0.0311 -
0.5938 950 0.0228 -
0.625 1000 0.0026 -
0.6562 1050 0.0196 -
0.6875 1100 0.0017 -
0.7188 1150 0.0067 -
0.75 1200 0.0029 -
0.7812 1250 0.0041 -
0.8125 1300 0.0006 -
0.8438 1350 0.0022 -
0.875 1400 0.0006 -
0.9062 1450 0.0007 -
0.9375 1500 0.001 -
0.9688 1550 0.0009 -
1.0 1600 0.0013 -

Framework Versions

  • Python: 3.8.10
  • SetFit: 1.0.3
  • Sentence Transformers: 2.3.1
  • Transformers: 4.37.2
  • PyTorch: 2.2.0+cu121
  • Datasets: 2.17.0
  • Tokenizers: 0.15.1

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}
}