This is a SetFit model that can be used for Text Classification. This SetFit model uses kushikawa/ML-paraphrase-multilingual-mpnet-base-v2-BLOCK-20260309-134415 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: kushikawa/ML-paraphrase-multilingual-mpnet-base-v2-BLOCK-20260309-134415
Classification head: a LogisticRegression instance
Maximum Sequence Length: 128 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
0	'ombrelone de madeira' 'vinho almaden' 'kit hobety'
1	'qual o nome daquele negocio de colocar no carro pra deixar o carro com ares de time attack car?' 'qual o nome daquele negocio de fazer brigadeiro de colher com granulado?' 'precos de iphone'

Evaluation

Metrics

Label	Accuracy
all	0.8952

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("kushikawa/SF-ML-paraphrase-multilingual-mpnet-base-v2-BLOCK-20260309-134415-ROUTE-20260422-005341")
# Run inference
preds = model("som pioneer")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	4.8586	88

Label	Training Sample Count
0	5000
1	5000

Training Hyperparameters

batch_size: (32, 32)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 5
body_learning_rate: (2e-05, 1e-05)
head_learning_rate: 0.01
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: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0003	1	0.3941	-
0.16	500	0.2035	0.1576
0.32	1000	0.0862	0.1501
0.48	1500	0.0464	0.1467
0.64	2000	0.0275	0.1575
0.8	2500	0.0181	0.1555
0.96	3000	0.015	0.1550

Framework Versions

Python: 3.10.12
SetFit: 1.1.3
Sentence Transformers: 5.1.2
Transformers: 4.56.2
PyTorch: 2.8.0+cu128
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}
}