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joshuapsa/setfit-news-topic-sentences

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face

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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:

1.Fine-tuning a Sentence Transformer with contrastive learning ("sentence-transformers/paraphrase-mpnet-base-v2" specifically in this case).
2.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.<br> 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:

bash

python -m pip install setfit

You can then run inference as follows:

python

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

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}
}