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
positive
  • 'klein , charming in comedies like american pie and dead-on in election , '
  • 'be fruitful '
  • 'soulful and '
negative
  • 'covered earlier and much better '
  • 'it too is a bomb . '
  • 'guilty about it '

Evaluation

Metrics

Label Accuracy
all 0.89

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("DrGwin/setfit-paraphrase-mpnet-base-v2-sst2A")
# Run inference
preds = model("i had to look away - this was god awful . ")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 9.55 46
Label Training Sample Count
negative 40
positive 60

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (4, 4)
  • 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
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0030 1 0.4181 -
0.1506 50 0.2514 -
0.3012 100 0.0932 -
0.4518 150 0.0029 -
0.6024 200 0.001 -
0.7530 250 0.0006 -
0.9036 300 0.0006 -
1.0 332 - 0.1722
1.0542 350 0.0014 -
1.2048 400 0.0004 -
1.3554 450 0.0004 -
1.5060 500 0.0095 -
1.6566 550 0.0003 -
1.8072 600 0.0003 -
1.9578 650 0.0003 -
2.0 664 - 0.1820
2.1084 700 0.0003 -
2.2590 750 0.0023 -
2.4096 800 0.0003 -
2.5602 850 0.0002 -
2.7108 900 0.0002 -
2.8614 950 0.0002 -
3.0 996 - 0.1970
3.0120 1000 0.0002 -
3.1627 1050 0.0003 -
3.3133 1100 0.0012 -
3.4639 1150 0.0002 -
3.6145 1200 0.0002 -
3.7651 1250 0.0003 -
3.9157 1300 0.001 -
4.0 1328 - 0.1810

Framework Versions

  • Python: 3.11.11
  • SetFit: 1.1.1
  • Sentence Transformers: 3.4.1
  • Transformers: 4.48.3
  • PyTorch: 2.5.1+cu124
  • Datasets: 3.3.2
  • Tokenizers: 0.21.0

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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