---
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](https://github.com/huggingface/setfit) 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:

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/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

```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}
}
```