---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
Niels Rasmussen, kaldet Niels Slagter, alm. Højde, svær bygget, mørkt Haar,
iført mørk Stortrøie, Benklæder og Kaskjet, sigtes forTyveri. Anholdes
hertil. (St. 3, 688.)
- text: Nicolai Gerhard Ahrenzen (Kbhvn.), 49 Aar.
- text: Peter Mortensen (Kbhvn.), 31 Aar. Død. P. [2733].
- text: Claus Tollefsen, 40 Aar.
- text: >-
2) Et Fruentimmer, ca. 40 Aar gl., middelaf Højde og Bygning med mørkt Haar,
iført mørk Kjole graaligt Shavl og over Hovedet et hvidt uldent Tørklæde med
en rød Kant, og medførte en brun Hankekurv, sigtes for Bedrageri. (St. 4,
177.)
metrics:
- accuracy
- f1
- precision
- recall
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
model-index:
- name: SetFit with JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9816666666666667
name: Accuracy
- type: f1
value: 0.9385474860335196
name: F1
- type: precision
value: 0.9230769230769231
name: Precision
- type: recall
value: 0.9545454545454546
name: Recall
language:
- da
---
# SetFit with JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [JohanHeinsen/Old_News_Segmentation_SBERT_V0.1](https://huggingface.co/JohanHeinsen/Old_News_Segmentation_SBERT_V0.1) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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:
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.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [JohanHeinsen/Old_News_Segmentation_SBERT_V0.1](https://huggingface.co/JohanHeinsen/Old_News_Segmentation_SBERT_V0.1)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | - '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.'
- '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.)'
- '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.'
|
| 0 | - '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).'
- 'Hans Edvard Valdemar Holst (Kbhvn.), 45 Aar. Løsgængeri.'
- 'Peter Christian Leyring (Levring), 26 Aar. Betleri.'
|
## 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