metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      Someone comes out of the shack and shoves one of the kids to the ground.
      He
  - text: He approaches the object and reads a plaque on its side. Someone
  - text: >-
      Later at someone's family farm, someone sees the lights on in the hangar.
      Someone
  - text: >-
      Someone stands looking over some of the old photographs as someone goes
      through the mess on the desk. Someone
  - text: Snow blows around a city of towering crystalline structures. A warrior
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: 0.11475409836065574
            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:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
Classification head: a SetFitHead instance
Maximum Sequence Length: 512 tokens
Number of Classes: 9 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
6	'The girl does 2 perfect flips. The girls' 'Emerging in open water, he does a breaststroke toward the murky. He' 'A young child is moving back and fourth on a swing while laughing and smiling to the camera. The child'
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"
1	'A lot of people are sitting on terraces in a big field and people is walking in the entrance of a big stadium. men' 'The water gets rough as the past through some rocks. Several people' 'We see a man dunk the ball twice. We'
5	'A man wearing a safari hat leads a group of camels with riders in a single file. The camera' 'Someone stirs the cookie dough in a bowl. The dough' 'A logo for a sports even is shown. There'
8	'A girl is shown several times running on a track. She' 'Someone peers out from the cabin. As she emerges, someone' 'Someone and someone swap looks. She'
2	'In slow motion, both the Russians and Americans celebrate. Someone' 'Through a window, we watch someone raise his teacup to his companions. At home, someone' 'People stand by the wall, laughing. He'
7	'Someone turns at the sound of the distant horns. 6000 horsemen, lead by people,' "The man plays the video in reverse to look as if he's putting shaving cream on with the razor. The men then" 'He eyes someone with a furrowed brow, then springs up and hurries after her. Someone and someone'
0	'She opens a small metal box on a desk and pushes a button inside. Someone' 'Someone starts to say something then thinks better of it, and remains silent. Someone' "Someone changes into a Spanish policeman's outfit and heads down an outside staircase with the packed up rifle. As someone leaves, someone"
3	'He is shown playing a game with a virtual sumo wrestler. The shorter man' 'The girls flips, then runs, flips and dismounts. The cloud' 'The official extends a red flag. As Master someone'

Evaluation

Metrics

Label	Accuracy
all	0.1148

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-4")
# 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	13.25	31

Label	Training Sample Count
0	4
1	4
2	4
3	4
4	4
5	4
6	4
7	4
8	4

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
0.0111	1	0.2285	-
0.5556	50	0.0567	-
1.1111	100	0.0083	-
1.6667	150	0.0084	-

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}
}