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: 2 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

non_opinionated

'We need to look at the capacity that we have across the system to do more.'
'Does my hon.'
'I can assure the hon.'

opinion

'In my estimation, the Government’s ongoing insistence that economic growth alone will resolve deep-seated social inequalities fails to reckon with the stark reality that unregulated markets have repeatedly produced uneven development, insecure employment, and a widening gulf between those who benefit from prosperity and those who are left behind.'
'It is my firm belief that unless this Government commits to a sustained and genuinely transformative programme of investment—one that reaches far beyond the narrow confines of short-term funding pots and actually tackles the structural inequalities baked into our economic geography—we will continue to condemn entire regions to stagnation, frustration, and the persistent feeling that Westminster has neither listened to them nor acted in their interest.'
'I must stress that any attempt to modernise our transport network without a long-term funding settlement is doomed to fall victim to the same cycle of delays, cancellations, and half-delivered projects that have plagued infrastructure initiatives for decades, leaving communities disconnected and local economies at a perpetual disadvantage.'

Evaluation

Metrics

Label	Accuracy
all	0.9545

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("Does my hon.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	34.3125	66

Label	Training Sample Count
non_opinionated	8
opinion	8

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 40
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
0.0125	1	0.2985	-
0.625	50	0.0417	-

Framework Versions

Python: 3.13.1
SetFit: 1.1.3
Sentence Transformers: 5.1.2
Transformers: 4.57.3
PyTorch: 2.9.1
Datasets: 4.4.1
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}
}

andreacristiano/stancedetection

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