metadata

license: apache-2.0
tags:
  - setfit
  - sentence-transformers
  - text-classification
pipeline_tag: text-classification

joshuapsa/setfit-ai-generated-sent

This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning ("sentence-transformers/paraphrase-mpnet-base-v2" specifically in this case).
Training a classification head with features from the fine-tuned Sentence Transformer.

This model was finetuned with the custom dataset joshuapsa/gpt-generated-news-sentences, which is a synthetic dataset containing news sentences and their topics.
Please refer to this to understand the label meanings of the prediction output.

Usage

To use this model for inference, first install the SetFit library:

python -m pip install setfit

You can then run inference as follows:

from setfit import SetFitModel

# Download from Hub and run inference
model = SetFitModel.from_pretrained("joshuapsa/setfit-news-topic-sentences")
# Run inference
preds = model(["Tensions escalated in the Taiwan Strait as Chinese and Taiwanese naval vessels engaged in a standoff, raising fears of a potential conflict.",\
 "Following the highway closure in Toronto, transportation officials announce plans for the construction of additional lanes and improved traffic management systems."])
# The underlying model body of the setfit model is a SentenceTransformer model, hence you can use it to encode a raw sentence into dense embeddings:
emb = model.model_body.encode("Your sentence goes here")

BibTeX entry and citation info

@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}
}