---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Emergency insurance payouts complement humanitarian assistance by providing
    timely financial resources that facilitate quicker recovery from climate disasters.
- text: "c) Establish strategic and operational partnerships and alliances with private,\
    \ public and civil society \norganizations in food and nutrition."
- text: 'COVID-19: The Development Program for Drinking Water Supply and Sanitation
    Systems of the Kyrgyz Republic until 2026 was approved.


    The Program is aimed at increasing the provision of drinking water of standard
    quality, improving the health and quality of life of the population of the republic,
    reducing the harmful effects on the environment through the construction, reconstruction,
    and modernization of drinking water supply and sanitation systems.'
- text: "The program mainly aims at \nthe construction of rural roads, capacity building\
    \ of local bodies, and \nawareness raising activities."
- text: "Mr. Speaker, the PF Government \n\nremains committed to ensuring that all\
    \ \n\nZambians have access to clean water supply \n\nand sanitation services."
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
---

# 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 MultiOutputClassifier 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 MultiOutputClassifier instance
- **Maximum Sequence Length:** 128 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)

## 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("faodl/20250909_model_g20_multilabel_MiniLM-L12-all-labels-artificial-governance-multi-output")
# Run inference
preds = model("The program mainly aims at 
the construction of rural roads, capacity building of local bodies, and 
awareness raising activities.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max  |
|:-------------|:----|:--------|:-----|
| Word count   | 1   | 41.6795 | 1753 |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1     | 0.184         | -               |
| 0.0039 | 50    | 0.1927        | -               |
| 0.0078 | 100   | 0.1729        | -               |
| 0.0117 | 150   | 0.1484        | -               |
| 0.0156 | 200   | 0.1301        | -               |
| 0.0196 | 250   | 0.1134        | -               |
| 0.0235 | 300   | 0.1079        | -               |
| 0.0274 | 350   | 0.1021        | -               |
| 0.0313 | 400   | 0.0876        | -               |
| 0.0352 | 450   | 0.0834        | -               |
| 0.0391 | 500   | 0.0886        | -               |
| 0.0430 | 550   | 0.0728        | -               |
| 0.0469 | 600   | 0.0775        | -               |
| 0.0508 | 650   | 0.0811        | -               |
| 0.0548 | 700   | 0.0745        | -               |
| 0.0587 | 750   | 0.0753        | -               |
| 0.0626 | 800   | 0.0745        | -               |
| 0.0665 | 850   | 0.07          | -               |
| 0.0704 | 900   | 0.0702        | -               |
| 0.0743 | 950   | 0.0707        | -               |
| 0.0782 | 1000  | 0.0702        | -               |
| 0.0821 | 1050  | 0.0607        | -               |
| 0.0860 | 1100  | 0.067         | -               |
| 0.0899 | 1150  | 0.065         | -               |
| 0.0939 | 1200  | 0.0659        | -               |
| 0.0978 | 1250  | 0.066         | -               |
| 0.1017 | 1300  | 0.066         | -               |
| 0.1056 | 1350  | 0.06          | -               |
| 0.1095 | 1400  | 0.0609        | -               |
| 0.1134 | 1450  | 0.0587        | -               |
| 0.1173 | 1500  | 0.0542        | -               |
| 0.1212 | 1550  | 0.0523        | -               |
| 0.1251 | 1600  | 0.0559        | -               |
| 0.1291 | 1650  | 0.052         | -               |
| 0.1330 | 1700  | 0.0487        | -               |
| 0.1369 | 1750  | 0.053         | -               |
| 0.1408 | 1800  | 0.0477        | -               |
| 0.1447 | 1850  | 0.0492        | -               |
| 0.1486 | 1900  | 0.0474        | -               |
| 0.1525 | 1950  | 0.0488        | -               |
| 0.1564 | 2000  | 0.0461        | -               |
| 0.1603 | 2050  | 0.0481        | -               |
| 0.1643 | 2100  | 0.0463        | -               |
| 0.1682 | 2150  | 0.0432        | -               |
| 0.1721 | 2200  | 0.0482        | -               |
| 0.1760 | 2250  | 0.0444        | -               |
| 0.1799 | 2300  | 0.0466        | -               |
| 0.1838 | 2350  | 0.0423        | -               |
| 0.1877 | 2400  | 0.041         | -               |
| 0.1916 | 2450  | 0.0422        | -               |
| 0.1955 | 2500  | 0.0401        | -               |
| 0.1995 | 2550  | 0.0405        | -               |
| 0.2034 | 2600  | 0.0448        | -               |
| 0.2073 | 2650  | 0.0387        | -               |
| 0.2112 | 2700  | 0.0371        | -               |
| 0.2151 | 2750  | 0.0429        | -               |
| 0.2190 | 2800  | 0.0379        | -               |
| 0.2229 | 2850  | 0.0384        | -               |
| 0.2268 | 2900  | 0.0378        | -               |
| 0.2307 | 2950  | 0.0392        | -               |
| 0.2346 | 3000  | 0.038         | -               |
| 0.2386 | 3050  | 0.0325        | -               |
| 0.2425 | 3100  | 0.0345        | -               |
| 0.2464 | 3150  | 0.0341        | -               |
| 0.2503 | 3200  | 0.0415        | -               |
| 0.2542 | 3250  | 0.0313        | -               |
| 0.2581 | 3300  | 0.0355        | -               |
| 0.2620 | 3350  | 0.033         | -               |
| 0.2659 | 3400  | 0.0308        | -               |
| 0.2698 | 3450  | 0.0343        | -               |
| 0.2738 | 3500  | 0.0379        | -               |
| 0.2777 | 3550  | 0.032         | -               |
| 0.2816 | 3600  | 0.0358        | -               |
| 0.2855 | 3650  | 0.0334        | -               |
| 0.2894 | 3700  | 0.0312        | -               |
| 0.2933 | 3750  | 0.0336        | -               |
| 0.2972 | 3800  | 0.0291        | -               |
| 0.3011 | 3850  | 0.0268        | -               |
| 0.3050 | 3900  | 0.034         | -               |
| 0.3090 | 3950  | 0.0337        | -               |
| 0.3129 | 4000  | 0.0266        | -               |
| 0.3168 | 4050  | 0.0269        | -               |
| 0.3207 | 4100  | 0.0326        | -               |
| 0.3246 | 4150  | 0.0317        | -               |
| 0.3285 | 4200  | 0.0271        | -               |
| 0.3324 | 4250  | 0.0313        | -               |
| 0.3363 | 4300  | 0.0263        | -               |
| 0.3402 | 4350  | 0.0267        | -               |
| 0.3442 | 4400  | 0.0273        | -               |
| 0.3481 | 4450  | 0.026         | -               |
| 0.3520 | 4500  | 0.0252        | -               |
| 0.3559 | 4550  | 0.0261        | -               |
| 0.3598 | 4600  | 0.0243        | -               |
| 0.3637 | 4650  | 0.0252        | -               |
| 0.3676 | 4700  | 0.0291        | -               |
| 0.3715 | 4750  | 0.0286        | -               |
| 0.3754 | 4800  | 0.0245        | -               |
| 0.3794 | 4850  | 0.0263        | -               |
| 0.3833 | 4900  | 0.0249        | -               |
| 0.3872 | 4950  | 0.0209        | -               |
| 0.3911 | 5000  | 0.0245        | -               |
| 0.3950 | 5050  | 0.0278        | -               |
| 0.3989 | 5100  | 0.0277        | -               |
| 0.4028 | 5150  | 0.0266        | -               |
| 0.4067 | 5200  | 0.0249        | -               |
| 0.4106 | 5250  | 0.0279        | -               |
| 0.4145 | 5300  | 0.027         | -               |
| 0.4185 | 5350  | 0.0283        | -               |
| 0.4224 | 5400  | 0.022         | -               |
| 0.4263 | 5450  | 0.0232        | -               |
| 0.4302 | 5500  | 0.0198        | -               |
| 0.4341 | 5550  | 0.0254        | -               |
| 0.4380 | 5600  | 0.0186        | -               |
| 0.4419 | 5650  | 0.0237        | -               |
| 0.4458 | 5700  | 0.0249        | -               |
| 0.4497 | 5750  | 0.0241        | -               |
| 0.4537 | 5800  | 0.0239        | -               |
| 0.4576 | 5850  | 0.0258        | -               |
| 0.4615 | 5900  | 0.0212        | -               |
| 0.4654 | 5950  | 0.0208        | -               |
| 0.4693 | 6000  | 0.0227        | -               |
| 0.4732 | 6050  | 0.0262        | -               |
| 0.4771 | 6100  | 0.0257        | -               |
| 0.4810 | 6150  | 0.0227        | -               |
| 0.4849 | 6200  | 0.0226        | -               |
| 0.4889 | 6250  | 0.0231        | -               |
| 0.4928 | 6300  | 0.0255        | -               |
| 0.4967 | 6350  | 0.0199        | -               |
| 0.5006 | 6400  | 0.022         | -               |
| 0.5045 | 6450  | 0.0253        | -               |
| 0.5084 | 6500  | 0.0209        | -               |
| 0.5123 | 6550  | 0.0207        | -               |
| 0.5162 | 6600  | 0.0215        | -               |
| 0.5201 | 6650  | 0.0225        | -               |
| 0.5241 | 6700  | 0.0185        | -               |
| 0.5280 | 6750  | 0.019         | -               |
| 0.5319 | 6800  | 0.0214        | -               |
| 0.5358 | 6850  | 0.0252        | -               |
| 0.5397 | 6900  | 0.0216        | -               |
| 0.5436 | 6950  | 0.0205        | -               |
| 0.5475 | 7000  | 0.0205        | -               |
| 0.5514 | 7050  | 0.0244        | -               |
| 0.5553 | 7100  | 0.0223        | -               |
| 0.5592 | 7150  | 0.0181        | -               |
| 0.5632 | 7200  | 0.0199        | -               |
| 0.5671 | 7250  | 0.0217        | -               |
| 0.5710 | 7300  | 0.0198        | -               |
| 0.5749 | 7350  | 0.0224        | -               |
| 0.5788 | 7400  | 0.0234        | -               |
| 0.5827 | 7450  | 0.0193        | -               |
| 0.5866 | 7500  | 0.0168        | -               |
| 0.5905 | 7550  | 0.0193        | -               |
| 0.5944 | 7600  | 0.0232        | -               |
| 0.5984 | 7650  | 0.0183        | -               |
| 0.6023 | 7700  | 0.0255        | -               |
| 0.6062 | 7750  | 0.0209        | -               |
| 0.6101 | 7800  | 0.0262        | -               |
| 0.6140 | 7850  | 0.0228        | -               |
| 0.6179 | 7900  | 0.0208        | -               |
| 0.6218 | 7950  | 0.0167        | -               |
| 0.6257 | 8000  | 0.0217        | -               |
| 0.6296 | 8050  | 0.0175        | -               |
| 0.6336 | 8100  | 0.0196        | -               |
| 0.6375 | 8150  | 0.0215        | -               |
| 0.6414 | 8200  | 0.0186        | -               |
| 0.6453 | 8250  | 0.0181        | -               |
| 0.6492 | 8300  | 0.0171        | -               |
| 0.6531 | 8350  | 0.0224        | -               |
| 0.6570 | 8400  | 0.0214        | -               |
| 0.6609 | 8450  | 0.0214        | -               |
| 0.6648 | 8500  | 0.0192        | -               |
| 0.6688 | 8550  | 0.0213        | -               |
| 0.6727 | 8600  | 0.0185        | -               |
| 0.6766 | 8650  | 0.02          | -               |
| 0.6805 | 8700  | 0.0218        | -               |
| 0.6844 | 8750  | 0.0163        | -               |
| 0.6883 | 8800  | 0.0183        | -               |
| 0.6922 | 8850  | 0.0177        | -               |
| 0.6961 | 8900  | 0.0178        | -               |
| 0.7000 | 8950  | 0.0157        | -               |
| 0.7039 | 9000  | 0.0201        | -               |
| 0.7079 | 9050  | 0.017         | -               |
| 0.7118 | 9100  | 0.0198        | -               |
| 0.7157 | 9150  | 0.0196        | -               |
| 0.7196 | 9200  | 0.0189        | -               |
| 0.7235 | 9250  | 0.018         | -               |
| 0.7274 | 9300  | 0.0193        | -               |
| 0.7313 | 9350  | 0.0179        | -               |
| 0.7352 | 9400  | 0.0218        | -               |
| 0.7391 | 9450  | 0.0186        | -               |
| 0.7431 | 9500  | 0.0175        | -               |
| 0.7470 | 9550  | 0.0168        | -               |
| 0.7509 | 9600  | 0.0193        | -               |
| 0.7548 | 9650  | 0.0183        | -               |
| 0.7587 | 9700  | 0.0168        | -               |
| 0.7626 | 9750  | 0.0194        | -               |
| 0.7665 | 9800  | 0.021         | -               |
| 0.7704 | 9850  | 0.0178        | -               |
| 0.7743 | 9900  | 0.018         | -               |
| 0.7783 | 9950  | 0.0171        | -               |
| 0.7822 | 10000 | 0.0191        | -               |
| 0.7861 | 10050 | 0.0147        | -               |
| 0.7900 | 10100 | 0.0193        | -               |
| 0.7939 | 10150 | 0.0174        | -               |
| 0.7978 | 10200 | 0.0171        | -               |
| 0.8017 | 10250 | 0.0156        | -               |
| 0.8056 | 10300 | 0.0176        | -               |
| 0.8095 | 10350 | 0.0195        | -               |
| 0.8135 | 10400 | 0.0151        | -               |
| 0.8174 | 10450 | 0.0192        | -               |
| 0.8213 | 10500 | 0.0201        | -               |
| 0.8252 | 10550 | 0.0192        | -               |
| 0.8291 | 10600 | 0.015         | -               |
| 0.8330 | 10650 | 0.0181        | -               |
| 0.8369 | 10700 | 0.0143        | -               |
| 0.8408 | 10750 | 0.0177        | -               |
| 0.8447 | 10800 | 0.015         | -               |
| 0.8487 | 10850 | 0.0193        | -               |
| 0.8526 | 10900 | 0.0168        | -               |
| 0.8565 | 10950 | 0.0169        | -               |
| 0.8604 | 11000 | 0.0166        | -               |
| 0.8643 | 11050 | 0.0148        | -               |
| 0.8682 | 11100 | 0.0163        | -               |
| 0.8721 | 11150 | 0.0189        | -               |
| 0.8760 | 11200 | 0.0197        | -               |
| 0.8799 | 11250 | 0.0138        | -               |
| 0.8838 | 11300 | 0.0168        | -               |
| 0.8878 | 11350 | 0.0153        | -               |
| 0.8917 | 11400 | 0.0147        | -               |
| 0.8956 | 11450 | 0.0178        | -               |
| 0.8995 | 11500 | 0.0184        | -               |
| 0.9034 | 11550 | 0.0158        | -               |
| 0.9073 | 11600 | 0.0183        | -               |
| 0.9112 | 11650 | 0.0127        | -               |
| 0.9151 | 11700 | 0.0169        | -               |
| 0.9190 | 11750 | 0.018         | -               |
| 0.9230 | 11800 | 0.0156        | -               |
| 0.9269 | 11850 | 0.0156        | -               |
| 0.9308 | 11900 | 0.0162        | -               |
| 0.9347 | 11950 | 0.0124        | -               |
| 0.9386 | 12000 | 0.0175        | -               |
| 0.9425 | 12050 | 0.0179        | -               |
| 0.9464 | 12100 | 0.0182        | -               |
| 0.9503 | 12150 | 0.0176        | -               |
| 0.9542 | 12200 | 0.0182        | -               |
| 0.9582 | 12250 | 0.0189        | -               |
| 0.9621 | 12300 | 0.0125        | -               |
| 0.9660 | 12350 | 0.0176        | -               |
| 0.9699 | 12400 | 0.0143        | -               |
| 0.9738 | 12450 | 0.0162        | -               |
| 0.9777 | 12500 | 0.017         | -               |
| 0.9816 | 12550 | 0.0196        | -               |
| 0.9855 | 12600 | 0.0192        | -               |
| 0.9894 | 12650 | 0.0184        | -               |
| 0.9934 | 12700 | 0.0149        | -               |
| 0.9973 | 12750 | 0.0172        | -               |

### Framework Versions
- Python: 3.12.11
- SetFit: 1.1.3
- Sentence Transformers: 5.1.0
- Transformers: 4.56.1
- PyTorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0

## 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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