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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      amy and matthew have a bit of a phony relationship , but the film works in
      spite of it .
  - text: it 's refreshing to see a romance this smart .
  - text: >-
      bogdanich is unashamedly pro-serbian and makes little attempt to give
      voice to the other side .
  - text: >-
      sayles has an eye for the ways people of different ethnicities talk to and
      about others outside the group .
  - text: >-
      eddie murphy and owen wilson have a cute partnership in i spy , but the
      movie around them is so often nearly nothing that their charm does n't do
      a load of good .
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
  - name: SetFit with BAAI/bge-small-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.8478857770455793
            name: Accuracy

SetFit with BAAI/bge-small-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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
  • 'there might be some sort of credible gender-provoking philosophy submerged here , but who the hell cares ?'
  • 'represents the depths to which the girls-behaving-badly film has fallen .'
  • '-lrb- a -rrb- crushing disappointment .'
positive
  • 'what saves it ... and makes it one of the better video-game-based flicks , is that the film acknowledges upfront that the plot makes no sense , such that the lack of linearity is the point of emotional and moral departure for protagonist alice .'
  • 'but it could be , by its art and heart , a necessary one .'
  • 'a culture-clash comedy that , in addition to being very funny , captures some of the discomfort and embarrassment of being a bumbling american in europe .'

Evaluation

Metrics

Label Accuracy
all 0.8479

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("skylord/setfit-bge-small-v1.5-sst2-8-shot")
# Run inference
preds = model("it 's refreshing to see a romance this smart .")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 6 22.5 45
Label Training Sample Count
negative 8
positive 8

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (10, 10)
  • 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: False

Training Results

Epoch Step Training Loss Validation Loss
0.2 1 0.2109 -
10.0 50 0.01 -

Framework Versions

  • Python: 3.10.11
  • SetFit: 1.0.3
  • Sentence Transformers: 2.3.1
  • Transformers: 4.37.2
  • PyTorch: 2.2.0+cu121
  • Datasets: 2.16.1
  • 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}
}