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.

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

'3) J Anledning af en Skilsmissesag udbedes Oplysning hertil om hvor Bagersvend Christian Meyer for Tiden opholder sig. Han er født paa Sams, 34 Aar gl., middel af Højde og spinkel, har mørkeblondt Haar og Kindskjægog brune Øjne. (H. St.)'
'3) Arbejdsmand August Hepper, f. d. 3/3 1850 i Schlesien, temmelig høj og godt bygget, mørkt Haar og Overskjæg, mulig iført brungraa Jakke, Buxer og Vest, samt sort Hat, sigtes for Tyveri. Han gaaer rimeligt under falskt Navn, muligvisWentzel. Anh. til K. A. søndre Birk.'
'5) Slagtersvend Jacob Peter Lydolf, ca. 25 Aar gl., født i Stubbekjøbing, modtog den 9de d. M. en Ko af Gjæstgiver Frits Johansen, i Skaaruper og forpligtede sig til at betale den med 38 Rd., nemlig 34 Rd. Lørdagen den 14de. d. M. paa Værtshusholder Bertelsens Bopæl i Svendborg og 4 Rd. i Løbet af 8 eller 14 Dage. Da Johansen den 14de indfandt sig hos Bertelsen, erfarede han paa Politikammeret, at Lydolf den 11te var viseret til Veile. Lydolf, som er temmelig fordrukken og forslagen, bedes afhørt og efter Omstændighederne anholdt samt Underretning meddelt Sunds-Gudme Herreders Kontor i Svendborg.'

'6) Et Fruentimmer, 28 a 30 Aar gl., af almindelig Højde og Bygning, formentlig frugtsommelig, blondt Haar, der stritter frem i Panden, iført sort ulden Kjole, gamle Fjederstøvler og graat uldent Shavl med 1 1/2 Kvarter bred Bort, sort Fløjls Hat med mørkerød Fløjls Blomst, sorte Atlaskes Hagebaand, der vare knyttede ned om Hagen, hvid Blonde paa Kjoleærmet ved Haanden og paa det ene Haandled et Stenkuls Armbaand, sigtes for Boutikstyveri. (St. 3.)'
'Efterlysninger. Et Fruentimmer fra Lidemark ved Navn'
'3) Et Fruentimmer, c. 40 Aar gl., temmelig høj, spinkel bygget, langagtigt, magert Ansigt, mørkt Haar og mørk Teint, iført graat Shavl, chocoladebrun Kjole og sort Kyse, sigtes forFalsk og Bedrageri. (St. 5, 61.)'

Evaluation

Metrics

Label	Accuracy	F1	Precision	Recall
all	0.98	0.9371	0.9437	0.9306

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("2) En Mandsperson, 19-24 Aar gl., lidt under Middelhøide, blond, uden Skjæg, rødmusset, sort Klædesfrakke og sort, flad Kaskjet, – sigtes for Tyveriet Nr. 765. (II).")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	7	56.6019	1181

Label	Training Sample Count
0	861
1	189

Training Hyperparameters

batch_size: (24, 24)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 44
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: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0003	1	0.2644	-
0.0130	50	0.2902	-
0.0260	100	0.1297	-
0.0390	150	0.0355	-
0.0519	200	0.02	-
0.0649	250	0.0086	-
0.0779	300	0.0039	-
0.0909	350	0.0024	-
0.1039	400	0.0019	-
0.1169	450	0.0007	-
0.1299	500	0.0001	-
0.1429	550	0.0001	-
0.1558	600	0.0001	-
0.1688	650	0.0001	-
0.1818	700	0.0001	-
0.1948	750	0.0	-
0.2078	800	0.0	-
0.2208	850	0.0	-
0.2338	900	0.0	-
0.2468	950	0.0	-
0.2597	1000	0.0	-
0.2727	1050	0.0	-
0.2857	1100	0.0	-
0.2987	1150	0.0	-
0.3117	1200	0.0	-
0.3247	1250	0.0	-
0.3377	1300	0.0	-
0.3506	1350	0.0	-
0.3636	1400	0.0	-
0.3766	1450	0.0	-
0.3896	1500	0.0	-
0.4026	1550	0.0	-
0.4156	1600	0.0	-
0.4286	1650	0.0	-
0.4416	1700	0.0	-
0.4545	1750	0.0	-
0.4675	1800	0.0	-
0.4805	1850	0.0	-
0.4935	1900	0.0	-
0.5065	1950	0.0	-
0.5195	2000	0.0	-
0.5325	2050	0.0	-
0.5455	2100	0.0	-
0.5584	2150	0.0	-
0.5714	2200	0.0	-
0.5844	2250	0.0	-
0.5974	2300	0.0	-
0.6104	2350	0.0	-
0.6234	2400	0.0	-
0.6364	2450	0.0	-
0.6494	2500	0.0	-
0.6623	2550	0.0	-
0.6753	2600	0.0	-
0.6883	2650	0.0	-
0.7013	2700	0.0	-
0.7143	2750	0.0	-
0.7273	2800	0.0	-
0.7403	2850	0.0	-
0.7532	2900	0.0	-
0.7662	2950	0.0	-
0.7792	3000	0.0	-
0.7922	3050	0.0	-
0.8052	3100	0.0	-
0.8182	3150	0.0	-
0.8312	3200	0.0	-
0.8442	3250	0.0	-
0.8571	3300	0.0	-
0.8701	3350	0.0	-
0.8831	3400	0.0	-
0.8961	3450	0.0	-
0.9091	3500	0.0	-
0.9221	3550	0.0	-
0.9351	3600	0.0	-
0.9481	3650	0.0	-
0.9610	3700	0.0	-
0.9740	3750	0.0	-
0.9870	3800	0.0	-
1.0	3850	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

Citation

BibTeX

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}
}

JohanHeinsen/PE_efterlyst_gender

SetFit with JohanHeinsen/Old_News_Segmentation_SBERT_V0.1