PE_efterlyst_classifier_v2

JohanHeinsen/PE_efterlyst_classifier_v2

PE_efterlyst_classifier_v2 is a text classification model from JohanHeinsen. Use it when you need a label for a piece of text. It is set up for setfit.

This is a SetFit model that can be used for Text Classification. This SetFit model uses JohanHeinsen/OldNewsSegmentationSBERTV0.1 as the Sentence Transformer embedding model. A LogisticRegression instance is used for…

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At a glance

Task: Text Classification
Library: setfit
Model type: bert
Access: Public
Created: Oct 11, 2025
Updated: Oct 16, 2025
SHA: a6d68fd4

Try a prompt

Base models

JohanHeinsen/Old_News_Segmentation_SBERT_V0.1

Model card

From the Hugging Face model README

SetFit with JohanHeinsen/Old_News_Segmentation_SBERT_V0.1

This is a SetFit model that can be used for Text Classification. This SetFit model uses JohanHeinsen/Old_News_Segmentation_SBERT_V0.1 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. It is designed to identify texts describing missing people from police gazettes in nineteenth century Denmark.

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.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
1	<ul><li>'2) Fattiglem, fhv. Roersbetjent, Jens Hansen, af Fare, har den 15de ds. forladt Fattiggaarden. der og formodes at drive arbejdsløs omkring muligvis er han taget til Kjøbenhavn for at søge Hyre som Sømand. Saafremt han, der er 50 Aar gl., middel af Væxt, og var iført sort rundpullet Hat, blaa Trøje og Benklæder muligvis dog et Par Lærreds ovenpaa – samt Træsko, maatte antræffes, bedes Underretning derom meddelt Byog Herredsfogden i Storehedinge.'</li><li>'1) En Mandsperson, ca. 20 Aar gl., middelstor eller lidt mindre, lyst Polkahaar, ordentlig klædt med mørk Sækfrakke og lyse Buxer, – sigtes for Tyveriet Nr. 3. (St. 2, 291.)'</li><li>'2) Oplysning om, hvor Garversvend Niels Peter Schmidt eller Niels Peter Nielsen Schmidt, født 16de Febr. 1839, maatte opholde sig, bedes meddeelt Muckadell m. fl. Birkers Kontor i Spanget pr. Kværndrup. Paagjældende blev blev den 28de Januar d. A. viseret derfra til Odense, hvorfra han strax igien skal være afgaaet til Fredericia.'</li></ul>
0	<ul><li>'2) 2 Høns og en Hane, denne sidste graa med laadne Ben, den ene Høne brunspættet, den anden sort med laadne Ben, ere bortkomne siden den 24. f.M. (St. 7, 448).'</li><li>'Hans Edvard Valdemar Holst (Kbhvn.), 45 Aar. Løsgængeri.'</li><li>'Peter Christian Leyring (Levring), 26 Aar. Betleri.'</li></ul>

Label

Examples

<ul><li>'2) Fattiglem, fhv. Roersbetjent, Jens Hansen, af Fare, har den 15de ds. forladt Fattiggaarden. der og formodes at drive arbejdsløs omkring muligvis er han taget til Kjøbenhavn for at søge Hyre som Sømand. Saafremt han, der er 50 Aar gl., middel af Væxt, og var iført sort rundpullet Hat, blaa Trøje og Benklæder muligvis dog et Par Lærreds ovenpaa – samt Træsko, maatte antræffes, bedes Underretning derom meddelt Byog Herredsfogden i Storehedinge.'</li><li>'1) En Mandsperson, ca. 20 Aar gl., middelstor eller lidt mindre, lyst Polkahaar, ordentlig klædt med mørk Sækfrakke og lyse Buxer, – sigtes for Tyveriet Nr. 3. (St. 2, 291.)'</li><li>'2) Oplysning om, hvor Garversvend Niels Peter Schmidt eller Niels Peter Nielsen Schmidt, født 16de Febr. 1839, maatte opholde sig, bedes meddeelt Muckadell m. fl. Birkers Kontor i Spanget pr. Kværndrup. Paagjældende blev blev den 28de Januar d. A. viseret derfra til Odense, hvorfra han strax igien skal være afgaaet til Fredericia.'</li></ul>

<ul><li>'2) 2 Høns og en Hane, denne sidste graa med laadne Ben, den ene Høne brunspættet, den anden sort med laadne Ben, ere bortkomne siden den 24. f.M. (St. 7, 448).'</li><li>'Hans Edvard Valdemar Holst (Kbhvn.), 45 Aar. Løsgængeri.'</li><li>'Peter Christian Leyring (Levring), 26 Aar. Betleri.'</li></ul>

Evaluation

Metrics

Label	Accuracy	F1	Precision	Recall
all	0.9817	0.9385	0.9231	0.9545

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	20.8907	245

Label	Training Sample Count
0	1195
1	205

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (3, 3)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 12
body_learning_rate: (2e-05, 2e-05)
head_learning_rate: 2e-05
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: False
use_amp: False
warmup_proportion: 0.1
l2_weight: 0.01
seed: 87
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0005	1	0.1797	-
0.0238	50	0.2091	-
0.0476	100	0.1061	-
0.0714	150	0.0529	-
0.0952	200	0.0491	-
0.1190	250	0.0238	-
0.1429	300	0.0195	-
0.1667	350	0.013	-
0.1905	400	0.0066	-
0.2143	450	0.005	-
0.2381	500	0.0038	-
0.2619	550	0.0038	-
0.2857	600	0.005	-
0.3095	650	0.0062	-
0.3333	700	0.0024	-
0.3571	750	0.0002	-
0.3810	800	0.0003	-
0.4048	850	0.0008	-
0.4286	900	0.0001	-
0.4524	950	0.0006	-
0.4762	1000	0.0022	-
0.5	1050	0.0003	-
0.5238	1100	0.0016	-
0.5476	1150	0.0001	-
0.5714	1200	0.0	-
0.5952	1250	0.0	-
0.6190	1300	0.0	-
0.6429	1350	0.0	-
0.6667	1400	0.0	-
0.6905	1450	0.0	-
0.7143	1500	0.0	-
0.7381	1550	0.0024	-
0.7619	1600	0.0002	-
0.7857	1650	0.0001	-
0.8095	1700	0.0	-
0.8333	1750	0.0	-
0.8571	1800	0.0	-
0.8810	1850	0.0	-
0.9048	1900	0.0	-
0.9286	1950	0.0	-
0.9524	2000	0.0	-
0.9762	2050	0.0	-
1.0	2100	0.0	-
1.0238	2150	0.0	-
1.0476	2200	0.0	-
1.0714	2250	0.0	-
1.0952	2300	0.0	-
1.1190	2350	0.0	-
1.1429	2400	0.0	-
1.1667	2450	0.0	-
1.1905	2500	0.0	-
1.2143	2550	0.0	-
1.2381	2600	0.0	-
1.2619	2650	0.0	-
1.2857	2700	0.0	-
1.3095	2750	0.0	-
1.3333	2800	0.0	-
1.3571	2850	0.0	-
1.3810	2900	0.0	-
1.4048	2950	0.0	-
1.4286	3000	0.0	-
1.4524	3050	0.0	-
1.4762	3100	0.0	-
1.5	3150	0.0	-
1.5238	3200	0.0	-
1.5476	3250	0.0	-
1.5714	3300	0.0	-
1.5952	3350	0.0	-
1.6190	3400	0.0	-
1.6429	3450	0.0	-
1.6667	3500	0.0	-
1.6905	3550	0.0	-
1.7143	3600	0.0	-
1.7381	3650	0.0	-
1.7619	3700	0.0	-
1.7857	3750	0.0	-
1.8095	3800	0.0	-
1.8333	3850	0.0	-
1.8571	3900	0.0	-
1.8810	3950	0.0	-
1.9048	4000	0.0	-
1.9286	4050	0.0	-
1.9524	4100	0.0	-
1.9762	4150	0.0	-
2.0	4200	0.0	-
2.0238	4250	0.0	-
2.0476	4300	0.0	-
2.0714	4350	0.0	-
2.0952	4400	0.0	-
2.1190	4450	0.0	-
2.1429	4500	0.0	-
2.1667	4550	0.0	-
2.1905	4600	0.0	-
2.2143	4650	0.0	-
2.2381	4700	0.0	-
2.2619	4750	0.0	-
2.2857	4800	0.0	-
2.3095	4850	0.0	-
2.3333	4900	0.0	-
2.3571	4950	0.0	-
2.3810	5000	0.0	-
2.4048	5050	0.0	-
2.4286	5100	0.0	-
2.4524	5150	0.0	-
2.4762	5200	0.0	-
2.5	5250	0.0	-
2.5238	5300	0.0	-
2.5476	5350	0.0	-
2.5714	5400	0.0	-
2.5952	5450	0.0	-
2.6190	5500	0.0	-
2.6429	5550	0.0	-
2.6667	5600	0.0	-
2.6905	5650	0.0	-
2.7143	5700	0.0	-
2.7381	5750	0.0	-
2.7619	5800	0.0	-
2.7857	5850	0.0	-
2.8095	5900	0.0	-
2.8333	5950	0.0	-
2.8571	6000	0.0	-
2.8810	6050	0.0	-
2.9048	6100	0.0	-
2.9286	6150	0.0	-
2.9524	6200	0.0	-
2.9762	6250	0.0	-
3.0	6300	0.0	-

Framework Versions

Python: 3.11.12
SetFit: 1.1.3
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.7.0
Datasets: 2.19.2
Tokenizers: 0.21.1

Task: Text Classification
Library: setfit
Type: bert
Languages: da
Created: Oct 11, 2025
Updated: Oct 16, 2025