---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: En Kokkepige søger Condition hos et Herskab eller honette Borgerfolk, enten
    strax eller til St. Hansdag, anvises i Adelgaden No. 287, første Bagsal.
- text: En anstændig Pige ønsker Condition som Stue, Kokke= eller Ene=Pige. Hun forstaaer
    Hvad hun paatage sig og recommenderes fra det Sted, hun nu tjener, paa Hjørnet
    af Raadhuusstrædet og Brolæggerstrædet Nr. 46, anden Sal.
- text: Et anstændig Fruentimmer som i flere Aar har tient i store Landhuusholdninger,
    ønsker sig en saadan Tieneste igien til 1ste November.
- text: En Pige søger Tieneste hos eenlige Folk, eller hvor der er et Par Børn at
    passe, anvises i lille Færgestrædet Nr. 231.
- text: Formedelst Sygdom er en Tieneste ledig for en Pige som kan malke, men uden
    godt Skudsmaal nytter det ikke at melde sig; Anviisningengives i Gothersgaden
    15.
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: 0.9433962264150944
      name: Accuracy
    - type: f1
      value: 0.9238578680203046
      name: F1
    - type: precision
      value: 0.8921568627450981
      name: Precision
    - type: recall
      value: 0.9578947368421052
      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>'En skikkelig Karl fra Jylland søger Condition til St. Hansdag og er at finde paa Christianshavn paa Hiørnet af Dronningensgade og Torvegagen i Kielderen i Nr. 359.'</li><li>'En Amme søger Plads, eller i Mangel som Goldamme, er at finde i Nyehavn, anden Port fra Charlottenborg.'</li><li>'En skikkelig Pige, som kan forevise de bedste Skudsmaale, ønsker sig en Tieneste som Frøkenpige eller Stuepige til 1ste Novbr. enten paa en Herregaard eller hos en honet Familie i Kiøbstæden. Hun anvises fra Adressecomtoiret.'</li></ul> |
| 1     | <ul><li>'En skikkelig Jomfru, som forstaaer godt Madlavning, Vadsk, Reengjøren og deslige, kan faae Condition paa Vesterbro Nr. 63, men uden gode Recommendationer om Troskab og god Opførsel nytter det ikke at mælde sig.'</li><li>'En Pige, som kan paatage sig et Kjøkken, kan strax faae Condition, naar hun mælder sig i Toldbodgaden Nr. 44, i Stuen.'</li><li>'En Goldamme kan strax faae Condition i Kronprindsensgaden Nr. 39, 3die Sal.'</li></ul>                                                                                         |

## Evaluation

### Metrics
| Label   | Accuracy | F1     | Precision | Recall |
|:--------|:---------|:-------|:----------|:-------|
| **all** | 0.9434   | 0.9239 | 0.8922    | 0.9579 |

## 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("En Pige søger Tieneste hos eenlige Folk, eller hvor der er et Par Børn at passe, anvises i lille Færgestrædet Nr. 231.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 8   | 32.1640 | 176 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 389                   |
| 1     | 227                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0011 | 1    | 0.0621        | -               |
| 0.0541 | 50   | 0.2937        | -               |
| 0.1082 | 100  | 0.1367        | -               |
| 0.1623 | 150  | 0.037         | -               |
| 0.2165 | 200  | 0.0215        | -               |
| 0.2706 | 250  | 0.0165        | -               |
| 0.3247 | 300  | 0.0103        | -               |
| 0.3788 | 350  | 0.0134        | -               |
| 0.4329 | 400  | 0.0146        | -               |
| 0.4870 | 450  | 0.003         | -               |
| 0.5411 | 500  | 0.0028        | -               |
| 0.5952 | 550  | 0.0027        | -               |
| 0.6494 | 600  | 0.0039        | -               |
| 0.7035 | 650  | 0.0003        | -               |
| 0.7576 | 700  | 0.0001        | -               |
| 0.8117 | 750  | 0.0001        | -               |
| 0.8658 | 800  | 0.0001        | -               |
| 0.9199 | 850  | 0.0001        | -               |
| 0.9740 | 900  | 0.0           | -               |
| 1.0281 | 950  | 0.0           | -               |
| 1.0823 | 1000 | 0.0           | -               |
| 1.1364 | 1050 | 0.0           | -               |
| 1.1905 | 1100 | 0.0           | -               |
| 1.2446 | 1150 | 0.0           | -               |
| 1.2987 | 1200 | 0.0           | -               |
| 1.3528 | 1250 | 0.0           | -               |
| 1.4069 | 1300 | 0.0           | -               |
| 1.4610 | 1350 | 0.0           | -               |
| 1.5152 | 1400 | 0.0           | -               |
| 1.5693 | 1450 | 0.0           | -               |
| 1.6234 | 1500 | 0.0           | -               |
| 1.6775 | 1550 | 0.0           | -               |
| 1.7316 | 1600 | 0.0           | -               |
| 1.7857 | 1650 | 0.0           | -               |
| 1.8398 | 1700 | 0.0           | -               |
| 1.8939 | 1750 | 0.0           | -               |
| 1.9481 | 1800 | 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}
}
```

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