subham18's picture
Add SetFit model
141ea98 verified
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: >-
      this is a story of two misfits who do n't stand a chance alone , but
      together they are magnificent . 
  - text: >-
      it does n't believe in itself , it has no sense of humor ... it 's just
      plain bored . 
  - 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 . 
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: 0.8562176165803109
            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
negative
  • 'stale and uninspired . '
  • "the film 's considered approach to its subject matter is too calm and thoughtful for agitprop , and the thinness of its characterizations makes it a failure as straight drama . ' "
  • "that their charm does n't do a load of good "
positive
  • "broomfield is energized by volletta wallace 's maternal fury , her fearlessness "
  • 'flawless '
  • 'insightfully written , delicately performed '

Evaluation

Metrics

Label Accuracy
all 0.8562

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("subham18/setfit-paraphrase-mpnet-base-v2-sst2")
# Run inference
preds = model("a fast , funny , highly enjoyable movie . ")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 11.4375 33
Label Training Sample Count
negative 8
positive 8

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
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.1111 1 0.2054 -
1.0 9 - 0.2199
2.0 18 - 0.1788
3.0 27 - 0.1717
4.0 36 - 0.1738
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.12.3
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.39.0
  • PyTorch: 2.4.0+cu121
  • Datasets: 2.21.0
  • Tokenizers: 0.15.2

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}
}