| --- |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| widget: |
| - text: 4) Malersvend Frederik Jørgen Nielsen, født i Vejle, omtrent 20 Aar gl., høj |
| af Væxt, blond, Haar, graa Øjne, stor lige Næse, iført blaa Jakke graastribet |
| Vest og Benklæder, Fjedersko, sort rundpuldet Hat, hvidt Linned og hvidt Uldtøi, |
| er den 16. Ds. bortgaaet fra sit Logis i Lille Fredens gade 5, 3. Sal, og det |
| formodes at han har aflivet. |
| - text: 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). |
| - text: 3) Karoline Johanne Marie Ridel, svensk af Fødsel, 18 Aar gl., middel af Væxt |
| og Bygning, blondt Haar og blaa Øjne; iført mørktærnet Kjole og Jaket, sigtes |
| for Tyveri. (St. 6, 489.) |
| - text: '1) For at Forhør kan blive optaget i Anledning af en modtagen Melding om, |
| at svensk Arbejdskarl Sven Pehrson, som den 21. Ds. har forladt St. Grundet, skal |
| have gjort sig skyldig i Vold, beder Herredsfogden i Nørvang Tørrild Herreder |
| i Vejle sig meddelt Underretning om, hvor bemeldte Sven Pehrson, der signaliseres: |
| 21 a 22 Aar gl., født i Jemshog Sogn, Bleking Lehn i Sverig, middel af Højde, |
| blondt Haar, Skjæg over hele Ansigtet, iført hvid Lærreds Dragt, Støvler. og høj |
| mørk Kaskjet, har taget Ophold.' |
| - text: Harald Valdemar Vogel, ca. 30 Aar gammel, temmelig høj, stærk bygget, blondt |
| Haar, blaa Øjne, belevent Væsen, i Mai 1886 har han stærk, rødligt Overskjæg, |
| sigtes for Tyveri. Anholdes til K. A. nordre Birk. |
| 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: 0.98 |
| name: Accuracy |
| - type: f1 |
| value: 0.9370629370629371 |
| name: F1 |
| - type: precision |
| value: 0.9436619718309859 |
| name: Precision |
| - type: recall |
| value: 0.9305555555555556 |
| name: Recall |
| --- |
| |
| # 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. |
|
|
| 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 |
| <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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 | |
| |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | 0 | <ul><li>'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.)'</li><li>'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.'</li><li>'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.'</li></ul> | |
| | 1 | <ul><li>'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.)'</li><li>'Efterlysninger. Et Fruentimmer fra Lidemark ved Navn'</li><li>'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.)'</li></ul> | |
|
|
| ## 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: |
|
|
| ```bash |
| 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).") |
| ``` |
|
|
| <!-- |
| ### Downstream Use |
|
|
| *List how someone could finetune this model on their own dataset.* |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## 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} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
