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---
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](https://github.com/huggingface/setfit) 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](https://www.sbert.net) 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:
```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("/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
```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}
}
``` |