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SetFit-FinBERT1-A3-statement

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SetFit-FinBERT1-A3-statement / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Etunimi Sukunimi Ruotsin kansasta yli puolet reilusti kannattaa Natoon hakemista.
Ainoana esteenä näkisin, että Ruotsin asevoimat eivät ole läheskään niin hyvällä
tolalla kuin Suomen mitä tulee varusmiehiin, reserviläisiin tai edes kalustoonkaan.
Mutta yhteisen hakemuksen kohdalla se tuskin olisi ongelma muille Nato-maille
hyväksynnän suhteen. Toinen ongelma on, että hyväksynnän tulisi olla sataprosenttinen
ja ei ole poissujettua, että Venäjä esmes vaikuttaisi yksittäiseen maahan niin,
että juuri se ei hyväksyisikään hakemusta.
- text: Etunimi Pugh nyt ymmärrän sun puolustelut asut jenkeissä.....
- text: Etunimi Sukunimi ei varmasti moni uskalla
- text: Etunimi Sukunimi Voisitko laittaa tuohon lastentappoväitteeseen mukaan jonkinlaista
faktaa. Jää muuten melko irralliseksi heitoksi. Ja etkös aiemmin korostanut, että
maa ei ole sama kuin ihmiset? No mikä on maa tai valtio, se on jäsentensä muodostama.
Nyt sitten väität, että Ukraina on maana tappanut lapsia 8 vuotta.
- text: Etunimi Sukunimi Historiaa kirjoitetaan vielä maaliskuun 2020 tapahtumista.
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: 0.8718253349471188
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) -->
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### 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 Raines jep. Suomalainen rokottamaton paha, ukrainalainen rokottamaton hyvä. Tää näkyy olevan nyt se mentaliteetti tällä hetkellä...'</li><li>'Etunimi Bistrom tilastot.'</li><li>'Etunimi Sukunimi myös delta oli suurimmalle osalle myös rokottamattomille lähes oireeton, omikron kuulemma vielä lievempi👏'</li></ul> |
| 0 | <ul><li>'Etunimi Sukunimi RAutaa rajoille Suomi suureksi ja Viena vapaaksi'</li><li>'Perussuomalaiset siivoamassa keskustelupalstoja, koronakriisiavustuksien avulla? Onhan tämä nyt joku Monty Python -sketsi?'</li><li>'on se hyvä että Kiurussa ei ole miestä vaan Niskavuoren Hetaa joka pistää tuollaisen pojanklopin aisoihin viimeistään silloin kun Vapaavuori on kaltereissa johtaessaan Uuttamaata terveyspaniikkiin.'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.8718 |
## 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")
# Run inference
preds = model("Etunimi Sukunimi ei varmasti moni uskalla")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 19.6854 | 213 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 263 |
| 1 | 700 |
### 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 | 0.2224 | - |
| 0.0692 | 50 | 0.2676 | - |
| 0.1383 | 100 | 0.2486 | - |
| 0.2075 | 150 | 0.2208 | - |
| 0.2766 | 200 | 0.1892 | - |
| 0.3458 | 250 | 0.1509 | - |
| 0.4149 | 300 | 0.1194 | - |
| 0.4841 | 350 | 0.0745 | - |
| 0.5533 | 400 | 0.039 | - |
| 0.6224 | 450 | 0.0298 | - |
| 0.6916 | 500 | 0.01 | - |
| 0.7607 | 550 | 0.006 | - |
| 0.8299 | 600 | 0.0021 | - |
| 0.8990 | 650 | 0.0017 | - |
| 0.9682 | 700 | 0.0038 | - |
| 1.0 | 723 | - | 0.2008 |
| 1.0373 | 750 | 0.0088 | - |
| 1.1065 | 800 | 0.0041 | - |
| 1.1757 | 850 | 0.0067 | - |
| 1.2448 | 900 | 0.0041 | - |
| 1.3140 | 950 | 0.0021 | - |
| 1.3831 | 1000 | 0.0036 | - |
| 1.4523 | 1050 | 0.0036 | - |
| 1.5214 | 1100 | 0.0011 | - |
| 1.5906 | 1150 | 0.0035 | - |
| 1.6598 | 1200 | 0.0047 | - |
| 1.7289 | 1250 | 0.0005 | - |
| 1.7981 | 1300 | 0.0002 | - |
| 1.8672 | 1350 | 0.0029 | - |
| 1.9364 | 1400 | 0.0029 | - |
| 2.0 | 1446 | - | 0.2342 |
| 2.0055 | 1450 | 0.0014 | - |
| 2.0747 | 1500 | 0.0023 | - |
| 2.1438 | 1550 | 0.0022 | - |
| 2.2130 | 1600 | 0.0014 | - |
| 2.2822 | 1650 | 0.0024 | - |
| 2.3513 | 1700 | 0.0035 | - |
| 2.4205 | 1750 | 0.0014 | - |
| 2.4896 | 1800 | 0.0022 | - |
| 2.5588 | 1850 | 0.0025 | - |
| 2.6279 | 1900 | 0.0003 | - |
| 2.6971 | 1950 | 0.0042 | - |
| 2.7663 | 2000 | 0.0014 | - |
| 2.8354 | 2050 | 0.0003 | - |
| 2.9046 | 2100 | 0.0022 | - |
| 2.9737 | 2150 | 0.0031 | - |
| 3.0 | 2169 | - | 0.2224 |
| 3.0429 | 2200 | 0.0016 | - |
| 3.1120 | 2250 | 0.0014 | - |
| 3.1812 | 2300 | 0.005 | - |
| 3.2503 | 2350 | 0.0045 | - |
| 3.3195 | 2400 | 0.001 | - |
| 3.3887 | 2450 | 0.0012 | - |
| 3.4578 | 2500 | 0.0004 | - |
| 3.5270 | 2550 | 0.0013 | - |
| 3.5961 | 2600 | 0.0022 | - |
| 3.6653 | 2650 | 0.0009 | - |
| 3.7344 | 2700 | 0.0018 | - |
| 3.8036 | 2750 | 0.0015 | - |
| 3.8728 | 2800 | 0.0019 | - |
| 3.9419 | 2850 | 0.0025 | - |
| 4.0 | 2892 | - | 0.2222 |
### 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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