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:

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 LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 8 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
0	'Steam locomotives carried mail across the countryside.' 'Horse-drawn carriages lined unpaved city streets.'
1	'The Manhattan Project developed atomic weapons.' 'Rationing and blackouts were common during the Blitz.'
2	'Sputnik launched and kicked off the space race.' 'The Berlin Wall symbolized Cold War tensions.'
3	'MySpace was the most popular social network for a while.' 'The Iraq War dominated headlines; people used early Facebook.'
4	'The first iPads changed tablet computing.' 'App stores exploded; people queued for new iPhones.'
5	'Fidget spinners were everywhere in middle schools.' 'Fortnite and Instagram Stories became daily habits.'
6	'Schools went remote during the pandemic; everyone wore N95s and used Zoom.' 'Lockdowns, PCR tests, and Zoom school were widespread.'
7	'Creators blew up on TikTok and AI tools like ChatGPT became common.' 'Hybrid work continues; large language models entered workplaces.'

Evaluation

Metrics

Label	Accuracy
all	1.0

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("TikTok creators exploded in popularity.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	6	8.0625	12

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

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (4, 4)
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
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.025	1	0.0637	-
1.25	50	0.0372	-
2.5	100	0.0035	-
3.75	150	0.0017	-

Framework Versions

Python: 3.10.7
SetFit: 1.1.3
Sentence Transformers: 5.1.0
Transformers: 4.56.2
PyTorch: 2.2.2
Datasets: 4.1.1
Tokenizers: 0.22.1

Citation

BibTeX

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}
}

DelaliScratchwerk/text-period-setfit

SetFit with sentence-transformers/paraphrase-mpnet-base-v2