setfit-demoModel / README.md
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metadata
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      the band 's courage in the face of official repression is inspiring ,
      especially for aging hippies ( this one included ) . 
  - text: 'a fast , funny , highly enjoyable movie . '
  - text: >-
      the movie achieves as great an impact by keeping these thoughts hidden as
      ... ( quills ) did by showing them . 
  - text: >-
      the title not only describes its main characters , but the lazy people
      behind the camera as well . 
  - text: '... the movie is just a plain old monster . '
inference: true
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: 0.8886010362694301
            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
yes
  • 'a compelling story of musical passion '
  • 'able to forgive its mean-spirited second half '
  • 'has the chops of a smart-aleck film school brat and the imagination of a big kid ... '
no
  • 'will have completely forgotten the movie by the time you get back to your car in the parking lot '
  • 'indignation '
  • 'down the reality drain '

Evaluation

Metrics

Label Accuracy
all 0.8886

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("EphronM/setfit-demoModel")
# Run inference
preds = model("a fast , funny , highly enjoyable movie . ")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 10.58 37
Label Training Sample Count
no 50
yes 50

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • 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: True

Training Results

Epoch Step Training Loss Validation Loss
0.0031 1 0.3648 -
0.1567 50 0.0215 -
0.3135 100 0.0029 -
0.4702 150 0.001 -
0.6270 200 0.0005 -
0.7837 250 0.0012 -
0.9404 300 0.0011 -
1.0 319 - 0.1299
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.14
  • SetFit: 1.1.0.dev0
  • Sentence Transformers: 3.0.1
  • Transformers: 4.44.2
  • PyTorch: 2.4.0+cu121
  • Datasets: 2.21.0
  • Tokenizers: 0.19.1

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}
}