---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Etunimi Sukunimi nyt unohdat, että punakapinaan oli sekaantunut myös venäläisiä
    kommunisteja. Tukivat punakapinallisia asetoimituksin ja lähettämällä upseereita
    johtamaan kapinaa. Yhteisen kielen puute vain onneksi häiritsi kapinallista työskentelyä.
    Historiaa sinun kannattaa lukea vähän enemmän. Venäjä on hyökännyt Suomeen kremlin
    johdolla useastikin. Alkaen jo tsarien ajoista. Pikku ja isoviha esim.
- text: 'Etunimi Sukunimi poistitko kommenttisi? Kirjoitin tällaisen vastauksen. Eipä
    tuolla mitään lähteitä ollut mainittu. Ainoastaan tämä jutun perässä: "Pääkirjoitukset
    ovat HS:n kannanottoja ajankohtaiseen aiheeseen. Kirjoitukset laatii HS:n pääkirjoitustoimitus,
    ja ne heijastavat lehden periaatelinjaa."'
- text: Voi olla, mutta ennen sen hävityn sodan loppua kuolee paljon ukrainalaisia
    ja myös venäläisiä eikä Putinia kavereineen saada siitä koskaan vastuuseen 😡
- text: Etunimi Sukunimi 🙋‍♀️
- text: Koska kolme rokotetta on täysi rokotesarja, niin todennäköisesti kolmesti
    rokotettuja. Nyt kun niitä tarjotaan kuitenkin kaikille yli 18-vuotiaille.
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: 0.9199207759105805
      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                                                                                                                                                                                                                                                                                                                      |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'Etunimi Sukunimi herra senkun aloittaa keräyksen♥️'</li><li>'Etunimi KorpelaNiko Korpela selvitä nyt oikeasti mitä perustuslaissa sanotaan, se ei ole mikään mutulaki. Kysy oikeilta asiantuntijoilta👍'</li><li>'Etunimi Sukunimi laita tähän esimerkkejä lainoppineista, joka on kanssasi samaa mieltä 👇'</li></ul> |
| 0     | <ul><li>'Etunimi Sukunimi venäjän syy hintojen nousu vai syytätkö sodastakin Suomen hallitusta ? 😖'</li><li>'Etunimi Sukunimi Olikhaan se virve'</li><li>'Etunimi Sukunimi onneks sentään ryyppäämään pääsee, eikä tule siihen ikäviä taukoja'</li></ul>                                                                      |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9199 |

## 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-request")
# Run inference
preds = model("Etunimi Sukunimi 🙋‍♀️")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 20.3115 | 213 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 865                   |
| 1     | 98                    |

### 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    | 0.2414        | -               |
| 0.0692 | 50   | 0.2723        | -               |
| 0.1383 | 100  | 0.2492        | -               |
| 0.2075 | 150  | 0.2232        | -               |
| 0.2766 | 200  | 0.1138        | -               |
| 0.3458 | 250  | 0.0362        | -               |
| 0.4149 | 300  | 0.0104        | -               |
| 0.4841 | 350  | 0.005         | -               |
| 0.5533 | 400  | 0.0018        | -               |
| 0.6224 | 450  | 0.0003        | -               |
| 0.6916 | 500  | 0.0002        | -               |
| 0.7607 | 550  | 0.0002        | -               |
| 0.8299 | 600  | 0.0015        | -               |
| 0.8990 | 650  | 0.0003        | -               |
| 0.9682 | 700  | 0.0006        | -               |
| 1.0    | 723  | -             | 0.2427          |
| 1.0373 | 750  | 0.0018        | -               |
| 1.1065 | 800  | 0.0001        | -               |
| 1.1757 | 850  | 0.0007        | -               |
| 1.2448 | 900  | 0.0001        | -               |
| 1.3140 | 950  | 0.0001        | -               |
| 1.3831 | 1000 | 0.0002        | -               |
| 1.4523 | 1050 | 0.0001        | -               |
| 1.5214 | 1100 | 0.0002        | -               |
| 1.5906 | 1150 | 0.0001        | -               |
| 1.6598 | 1200 | 0.0001        | -               |
| 1.7289 | 1250 | 0.0001        | -               |
| 1.7981 | 1300 | 0.0           | -               |
| 1.8672 | 1350 | 0.0           | -               |
| 1.9364 | 1400 | 0.0           | -               |
| 2.0    | 1446 | -             | 0.2464          |
| 2.0055 | 1450 | 0.0           | -               |
| 2.0747 | 1500 | 0.0           | -               |
| 2.1438 | 1550 | 0.0           | -               |
| 2.2130 | 1600 | 0.0           | -               |
| 2.2822 | 1650 | 0.0           | -               |
| 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 | -             | 0.2489          |
| 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 | -             | 0.2492          |

### 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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