---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Etunimi Sukunimi Kaunistelua sanoa että vain yhden ihmisen tähden. Putinin
    toimilla on Venäjällä laaja kannatus, kyllä kansakunnalla on myös kollektiivista
    vastuuta tapahtumista. Sotaa vastaan protestoivia on ollut vähemmän kuin Venäjällä
    sotilaita Ukrainassa.
- text: Toivottavasti valtio korvaa jokaisen menetetyn euron
- text: Etunimi Sukunimi Niin on..  ja valtioita joista lähinnä venäjä ja valko-venäjä.
- text: www.maskikauppa.fi
- text: Etunimi Sukunimi Jatka veikkonen.
metrics:
- metric
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: TurkuNLP/bert-base-finnish-cased-v1
model-index:
- name: SetFit with TurkuNLP/bert-base-finnish-cased-v1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: metric
      value: 0.7911322719833359
      name: Metric
---

# SetFit with TurkuNLP/bert-base-finnish-cased-v1

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [TurkuNLP/bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) 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:** [TurkuNLP/bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1)
- **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>'Etunimi Sukunimi katso tilastot ja vertaa funssiin'</li><li>'Ei muuta kuin Norjaan öljyostoksille 🙂'</li><li>'Etunimi Etunimi Härmä ja mitenköhän se yle ne anonyymit sinne haastatteluun saa jos he eivät itse ilmottaudu. Kun ovat noi potilastiedot salaisia.... joskus olis ihan hyvä hetki miettiä....🤔🤔'</li></ul> |
| 1     | <ul><li>'Etunimi väittäisin, että paremmin he tämän hoitavat, kuin viimekertainen "poikalauma"'</li><li>'Eikö. Lisää piikkejä vaan niin hyvää tulee. Ne pelastaa. 😆'</li><li>'Etunimi Sukunimi perustuslakia ei ole rikottu niissä asioissa mitä convoypellet väitti, kaikki mitä kaverit väittää ei ole totta .'</li></ul>       |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.7911 |

## 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("Finnish-actions/SetFit-FinBERT1-A3-challenge")
# Run inference
preds = model("www.maskikauppa.fi")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 20.2233 | 213 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 843                   |
| 1     | 120                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 6
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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
- evaluation_strategy: epoch
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0014 | 1    | 0.2409        | -               |
| 0.0692 | 50   | 0.2872        | -               |
| 0.1383 | 100  | 0.2515        | -               |
| 0.2075 | 150  | 0.2327        | -               |
| 0.2766 | 200  | 0.1678        | -               |
| 0.3458 | 250  | 0.0977        | -               |
| 0.4149 | 300  | 0.0434        | -               |
| 0.4841 | 350  | 0.031         | -               |
| 0.5533 | 400  | 0.0183        | -               |
| 0.6224 | 450  | 0.0084        | -               |
| 0.6916 | 500  | 0.0069        | -               |
| 0.7607 | 550  | 0.0057        | -               |
| 0.8299 | 600  | 0.0045        | -               |
| 0.8990 | 650  | 0.0011        | -               |
| 0.9682 | 700  | 0.0005        | -               |
| 1.0    | 723  | -             | 0.4558          |
| 1.0373 | 750  | 0.0003        | -               |
| 1.1065 | 800  | 0.0008        | -               |
| 1.1757 | 850  | 0.0007        | -               |
| 1.2448 | 900  | 0.0001        | -               |
| 1.3140 | 950  | 0.0001        | -               |
| 1.3831 | 1000 | 0.0008        | -               |
| 1.4523 | 1050 | 0.0001        | -               |
| 1.5214 | 1100 | 0.0001        | -               |
| 1.5906 | 1150 | 0.0001        | -               |
| 1.6598 | 1200 | 0.0001        | -               |
| 1.7289 | 1250 | 0.0001        | -               |
| 1.7981 | 1300 | 0.0001        | -               |
| 1.8672 | 1350 | 0.0001        | -               |
| 1.9364 | 1400 | 0.0001        | -               |
| 2.0    | 1446 | -             | 0.4562          |
| 2.0055 | 1450 | 0.0           | -               |
| 2.0747 | 1500 | 0.0001        | -               |
| 2.1438 | 1550 | 0.0001        | -               |
| 2.2130 | 1600 | 0.0           | -               |
| 2.2822 | 1650 | 0.0001        | -               |
| 2.3513 | 1700 | 0.0           | -               |
| 2.4205 | 1750 | 0.0           | -               |
| 2.4896 | 1800 | 0.0           | -               |
| 2.5588 | 1850 | 0.0           | -               |
| 2.6279 | 1900 | 0.0           | -               |
| 2.6971 | 1950 | 0.0           | -               |
| 2.7663 | 2000 | 0.0           | -               |
| 2.8354 | 2050 | 0.0           | -               |
| 2.9046 | 2100 | 0.0           | -               |
| 2.9737 | 2150 | 0.0           | -               |
| 3.0    | 2169 | -             | 0.4560          |
| 3.0429 | 2200 | 0.0           | -               |
| 3.1120 | 2250 | 0.0           | -               |
| 3.1812 | 2300 | 0.0           | -               |
| 3.2503 | 2350 | 0.0           | -               |
| 3.3195 | 2400 | 0.0           | -               |
| 3.3887 | 2450 | 0.0           | -               |
| 3.4578 | 2500 | 0.0           | -               |
| 3.5270 | 2550 | 0.0           | -               |
| 3.5961 | 2600 | 0.0           | -               |
| 3.6653 | 2650 | 0.0           | -               |
| 3.7344 | 2700 | 0.0           | -               |
| 3.8036 | 2750 | 0.0           | -               |
| 3.8728 | 2800 | 0.0           | -               |
| 3.9419 | 2850 | 0.0           | -               |
| 4.0    | 2892 | -             | 0.4593          |

### Framework Versions
- Python: 3.11.9
- SetFit: 1.1.3
- Sentence Transformers: 3.2.0
- Transformers: 4.44.0
- PyTorch: 2.4.0+cu124
- Datasets: 2.21.0
- Tokenizers: 0.19.1

## 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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