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
7
  • 'Someone turns at the sound of the distant horns. 6000 horsemen, lead by people,'
  • 'A man is playing the drums while wearing earphones. We'
  • 'Now, someone stands below an overcast sky. Strands of his greasy black hair'
5
  • 'Someone throws them onto someone and punches the both of them in the face. The crone then'
  • 'Someone stirs the cookie dough in a bowl. The dough'
  • 'A logo for a sports even is shown. There'
8
  • 'A teenage girl is dressed in a long sleeve red leotard and jumps up on a balance beam. Once she is on, she'
  • 'Someone watches with a heaving chest. He'
  • 'A woman smiles at the camera. The woman'
0
  • "Someone changes into a Spanish policeman's outfit and heads down an outside staircase with the packed up rifle. As someone leaves, someone"
  • 'He shows a water bottle he has along with a brush, and uses the brush to remove snow from the dash window of a car and the water to remove any excess snow left on the windshield. Once finished, he'
  • "Someone and someone step into a tent. Someone's mouth"
2
  • 'People suddenly wrap their arms around each other and kiss hungrily. Someone'
  • 'Loose papers fly and a wind blows blankets off the bed. Someone'
  • 'Together, they wander a few steps without taking their eyes off of him. Now in the car as someone drives, someone'
1
  • 'Villagers stare up at the night sky. Flashes of white light'
  • 'The water gets rough as the past through some rocks. Several people'
  • 'We see a title screen. We'
3
  • 'He is shown playing a game with a virtual sumo wrestler. The shorter man'
  • 'The Indian guy keeps his malevolent gaze on someone and looks away. The barmaid'
  • 'We see a man in red talking. A man'
4
  • 'He turns away and covers his face with one hand. Someone'
  • 'With a nod, the man hands it over to the defeated boy. Someone'
  • "On the shop floor, his little helper helps himself to an expensive handbag from a display cabinet, then some women's designer shoes, all of which are detailed on a list. He"
6
  • 'The girl does 2 perfect flips. The girls'
  • 'The man claps his hands together. The man'
  • 'A grey bunny is standing on a bed on a black towel eating something in his hand. As he eats, the bunny'

Evaluation

Metrics

Label Accuracy
all 0.0885

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-10")
# Run inference
preds = model("He approaches the object and reads a plaque on its side. Someone")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 6 13.9667 40
Label Training Sample Count
0 10
1 10
2 10
3 10
4 10
5 10
6 10
7 10
8 10

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
0.0044 1 0.2849 -
0.2222 50 0.1894 -
0.4444 100 0.0847 -
0.6667 150 0.0578 -
0.8889 200 0.0584 -
1.1111 250 0.011 -
1.3333 300 0.0183 -
1.5556 350 0.0106 -
1.7778 400 0.0125 -
2.0 450 0.0071 -

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}
}
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