README.md

metadata

license: apache-2.0
tags:
  - setfit
  - sentence-transformers
  - text-classification
pipeline_tag: text-classification
base_model:
  - CALDISS-AAU/DA-BERT_Old_News_V1
  - JohanHeinsen/Old_News_Segmentation_SBERT_V0.1

PE_header_classifier

This is a SetFit model that can be used for text classification. It was created to identify headers in the publication Politiets Efterretninger (1867–1890)

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. 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/tmpn6ptrcp2/JohanHeinsen/PE_header_classifier")
# Run inference
preds = model(["VI. Andre meddelelser", "1) Reserven er løbet bort."])

Metrics:

Accuracy: 0.9977494373593399

F1: 0.953125

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}
}