metadata
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
base_model:
- JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
- CALDISS-AAU/DA-BERT_Old_News_V1
PE_first_classifier
This is a SetFit model that can be used for text classification. It was trained to identify the first line in regular articles in the publication Politiets Efterretninger (1867–1890)
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.
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("/private/var/folders/6b/0g07c1bd5nx_dqlnklk5kq5h0000gn/T/tmpgk263u4x/JohanHeinsen/PE_first_classifier")
# Run inference
preds = model(["VI. Andre meddelelser", "1) Reserven er løbet bort."])
Metrics: Accuracy: 0.9883720930232558
F1: 0.976497346474602
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}
}