Swag-multi-class-20 / README.md
HelgeKn's picture
Add SetFit model
6181626
metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: She picks up a wine glass and takes a drink. She
  - text: Someone smiles as she looks out her window. Their car
  - text: >-
      Someone turns and her jaw drops at the site of the other woman. Moving in
      slow motion, someone
  - text: He sneers and winds up with his fist. Someone
  - text: >-
      He smooths it back with his hand. Finally, appearing confident and relaxed
      and with the old familiar glint in his eyes, someone
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: 0.16538461538461538
            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 SetFitHead 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
8
  • 'Later she meets someone at the bar. He'
  • 'He heads to them and sits. The bus'
  • 'Someone leaps to his feet and punches the agent in the face. Seemingly unaffected, the agent'
2
  • 'A man sits behind a desk. Two people'
  • 'A man is seen standing at the bottom of a hole while a man records him. Two men'
  • 'Someone questions his female colleague who shrugs. Through a window, we'
0
  • 'A woman bends down and puts something on a scale. She then'
  • 'He pulls down the blind. He'
  • 'Someone flings his hands forward. The someone fires, but the water'
6
  • 'People are sitting down on chairs. They'
  • 'They look up at stained glass skylights. The Americans'
  • 'The lady and the man dance around each other in a circle. The people'
1
  • 'An older gentleman kisses her. As he leads her off, someone'
  • 'The first girl comes back and does it effortlessly as the second girl still struggles. For the last round, the girl'
  • 'As she leaves, the bartender smiles. Now the blonde'
3
  • 'Someone lowers his demoralized gaze. Someone'
  • 'Someone goes into his bedroom. Someone'
  • 'As someone leaves, someone spots him on the monitor. Someone'
7
  • 'Four inches of Plexiglas separate the two and they talk on monitored phones. Someone'
  • 'The American and Russian commanders each watch them returning. As someone'
  • 'A group of walkers walk along the sidewalk near the lake. A man'
4
  • 'The secretary flexes the foot of her crossed - leg as she eyes someone. The woman'
  • 'A man in a white striped shirt is smiling. A woman'
  • 'He grabs her hair and pulls her head back. She'
5
  • 'He heads out of the plaza. Someone'
  • "As he starts back, he sees someone's scared look just before he slams the door shut. Someone"
  • 'He nods at her beaming. Someone'

Evaluation

Metrics

Label Accuracy
all 0.1654

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("HelgeKn/Swag-multi-class-20")
# Run inference
preds = model("He sneers and winds up with his fist. Someone")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 5 12.1056 33
Label Training Sample Count
0 20
1 20
2 20
3 20
4 20
5 20
6 20
7 20
8 20

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (2, 2)
  • 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
0.0022 1 0.3747 -
0.1111 50 0.2052 -
0.2222 100 0.1878 -
0.3333 150 0.1126 -
0.4444 200 0.1862 -
0.5556 250 0.1385 -
0.6667 300 0.0154 -
0.7778 350 0.0735 -
0.8889 400 0.0313 -
1.0 450 0.0189 -
1.1111 500 0.0138 -
1.2222 550 0.0046 -
1.3333 600 0.0043 -
1.4444 650 0.0021 -
1.5556 700 0.0033 -
1.6667 750 0.001 -
1.7778 800 0.0026 -
1.8889 850 0.0022 -
2.0 900 0.0014 -

Framework Versions

  • Python: 3.9.13
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.36.0
  • PyTorch: 2.1.1+cpu
  • Datasets: 2.15.0
  • Tokenizers: 0.15.0

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}
}