You can deploy this model and start earning money today!

kushikawa/
SF-ML-paraphrase-multilingual-mpnet-base-v2-BLOCK-20260309-134415-ROUTE-20260407-230229

Name: SF-ML-paraphrase-multilingual-mpnet-base-v2-BLOCK-20260309-134415-ROUTE-20260407-230229
Availability: InStock

setfit

safetensors

xlm-roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

text-embeddings-inference

endpoints_compatible

Views

No views yet

SetFit

This is a SetFit model that can be used for Text Classification. 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

Classification head: a LogisticRegression instance
Maximum Sequence Length: 128 tokens

Model Sources

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

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-20260407-230229")
# Run inference
preds = model("I loved the spiderman movie!")

Training Details

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}
}