---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Bir öğrenci her gün 5 sayfa kitap okuyor. 5 gün süresince kaç sayfa kitap
    okur?
- text: Bir inşaat firmasında 74 işçi vardı, Firma 29 işçi daha işe aldı. Firmada
    şimdi kaç işçi var?
- text: If you have 4 coins that total 35 cents, and 3 of them are dimes, what is
    the other coin?
- text: Bir üçgenin tabanı 12 cm, yüksekliği 8 cm'dir. Üçgenin alanı kaç cm²'dir?
- text: Bir üçgenin kenar uzunlukları 8 cm, 10 cm ve 12 cm'dir. Üçgenin çevresi kaç
    cm'dir?
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 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                                                                                                                                                                                                                                    |
|:---------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| non_math | <ul><li>'Güneş sisteminde kaç gezegen vardır?'</li><li>'What is the largest ocean on Earth?'</li><li>'What is the intake of energy and nutrients by living organisms called?'</li></ul>                                                     |
| math     | <ul><li>"Bir düzensiz çokgenin kenar uzunlukları 5 cm, 8 cm, 7 cm ve 6 cm'dir. Çokgenin çevresi kaç cm'dir?"</li><li>'27 ÷ 3 = ?'</li><li>'Bir altıgenin bir iç açısının ölçüsü 120° ise, tüm iç açıların toplamı kaç derecedir?'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("serdarcaglar/primary-school-math-question-multi-lang")
# Run inference
preds = model("Bir üçgenin tabanı 12 cm, yüksekliği 8 cm'dir. Üçgenin alanı kaç cm²'dir?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 10.3435 | 33  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| math     | 459                   |
| non_math | 129                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0007 | 1    | 0.1982        | -               |
| 0.0340 | 50   | 0.1009        | -               |
| 0.0680 | 100  | 0.0054        | -               |
| 0.1020 | 150  | 0.002         | -               |
| 0.1361 | 200  | 0.001         | -               |
| 0.1701 | 250  | 0.0023        | -               |
| 0.2041 | 300  | 0.0002        | -               |
| 0.2381 | 350  | 0.0013        | -               |
| 0.2721 | 400  | 0.0004        | -               |
| 0.3061 | 450  | 0.0004        | -               |
| 0.3401 | 500  | 0.0002        | -               |
| 0.3741 | 550  | 0.0001        | -               |
| 0.4082 | 600  | 0.0001        | -               |
| 0.4422 | 650  | 0.0002        | -               |
| 0.4762 | 700  | 0.0001        | -               |
| 0.5102 | 750  | 0.0001        | -               |
| 0.5442 | 800  | 0.0002        | -               |
| 0.5782 | 850  | 0.0002        | -               |
| 0.6122 | 900  | 0.0001        | -               |
| 0.6463 | 950  | 0.0005        | -               |
| 0.6803 | 1000 | 0.0001        | -               |
| 0.7143 | 1050 | 0.0001        | -               |
| 0.7483 | 1100 | 0.0001        | -               |
| 0.7823 | 1150 | 0.0001        | -               |
| 0.8163 | 1200 | 0.0001        | -               |
| 0.8503 | 1250 | 0.0001        | -               |
| 0.8844 | 1300 | 0.0           | -               |
| 0.9184 | 1350 | 0.0002        | -               |
| 0.9524 | 1400 | 0.0001        | -               |
| 0.9864 | 1450 | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- Tokenizers: 0.15.2

## 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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