README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: "6) Implement advocacy strategies with heads of government ministries, departments\
    \ \n\nand institutions, national, district and local leaders on solutions to major\
    \ nutrition \nproblems."
- text: 'The Government plans to continue its interventions aimed at increasing access
    to drinking water by:

    - in rural areas, constructing an additional 2 500 water points (mainly boreholes)
    and rehabilitating an extra 2 000 existing water points.

    - in urban and pre-urban areas, rehabilitating and constructing water supply infrastructure
    in the various urban towns.

    The Government will also, in terms of sanitation, continue to promote community-based
    approaches and construct facilities.


    Objective: ensure adequate access to sanitation facilities and increase access
    to clean and safe drinking water from 64% (2014) to 67% of the population in urban
    areas and from 83% (2014) to 85% in urban and pre-urban areas.


    '
- text: "Specific objective\ni) to improve safe water supply services to the people\
    \ in the rural communities\nii) to improve the water supply service levels in\
    \ rural area to enable rural the population in the \nproject areas to increase\
    \ their economic income through incorporating back yard or mini \nirrigation system."
- text: "Social security contributions  \n\nLabor \nMarkets \n\nActivation measures\
    \  \n\n• During the period of state of emergency, all training activities \nrecognized\
    \ by the Ministry of Labor and Social Protection can be \ndelivered online."
- text: "Training infrastructure will be adapted to accommodate \nnew\tprogrammes."
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 OneVsRestClassifier 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 OneVsRestClassifier 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/model_g20_multilabel")
# Run inference
preds = model("Training infrastructure will be adapted to accommodate 
new	programmes.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max  |
|:-------------|:----|:--------|:-----|
| Word count   | 1   | 48.9866 | 1181 |

### Training Hyperparameters
- batch_size: (16, 16)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0002 | 1    | 0.2348        | -               |
| 0.0119 | 50   | 0.1747        | -               |
| 0.0237 | 100  | 0.153         | -               |
| 0.0356 | 150  | 0.1314        | -               |
| 0.0475 | 200  | 0.1263        | -               |
| 0.0593 | 250  | 0.1168        | -               |
| 0.0712 | 300  | 0.116         | -               |
| 0.0831 | 350  | 0.098         | -               |
| 0.0949 | 400  | 0.1085        | -               |
| 0.1068 | 450  | 0.0975        | -               |
| 0.1187 | 500  | 0.094         | -               |
| 0.1305 | 550  | 0.082         | -               |
| 0.1424 | 600  | 0.0856        | -               |
| 0.1543 | 650  | 0.0838        | -               |
| 0.1662 | 700  | 0.0762        | -               |
| 0.1780 | 750  | 0.0722        | -               |
| 0.1899 | 800  | 0.0722        | -               |
| 0.2018 | 850  | 0.0634        | -               |
| 0.2136 | 900  | 0.0584        | -               |
| 0.2255 | 950  | 0.0664        | -               |
| 0.2374 | 1000 | 0.0688        | -               |
| 0.2492 | 1050 | 0.0629        | -               |
| 0.2611 | 1100 | 0.0579        | -               |
| 0.2730 | 1150 | 0.0652        | -               |
| 0.2848 | 1200 | 0.0573        | -               |
| 0.2967 | 1250 | 0.0584        | -               |
| 0.3086 | 1300 | 0.0558        | -               |
| 0.3204 | 1350 | 0.0586        | -               |
| 0.3323 | 1400 | 0.0574        | -               |
| 0.3442 | 1450 | 0.0444        | -               |
| 0.3560 | 1500 | 0.0462        | -               |
| 0.3679 | 1550 | 0.0488        | -               |
| 0.3798 | 1600 | 0.0505        | -               |
| 0.3916 | 1650 | 0.0529        | -               |
| 0.4035 | 1700 | 0.0487        | -               |
| 0.4154 | 1750 | 0.0459        | -               |
| 0.4272 | 1800 | 0.0531        | -               |
| 0.4391 | 1850 | 0.0448        | -               |
| 0.4510 | 1900 | 0.0382        | -               |
| 0.4629 | 1950 | 0.0457        | -               |
| 0.4747 | 2000 | 0.0493        | -               |
| 0.4866 | 2050 | 0.0488        | -               |
| 0.4985 | 2100 | 0.049         | -               |
| 0.5103 | 2150 | 0.0495        | -               |
| 0.5222 | 2200 | 0.0402        | -               |
| 0.5341 | 2250 | 0.0493        | -               |
| 0.5459 | 2300 | 0.0496        | -               |
| 0.5578 | 2350 | 0.0438        | -               |
| 0.5697 | 2400 | 0.0361        | -               |
| 0.5815 | 2450 | 0.0428        | -               |
| 0.5934 | 2500 | 0.0419        | -               |
| 0.6053 | 2550 | 0.0416        | -               |
| 0.6171 | 2600 | 0.0338        | -               |
| 0.6290 | 2650 | 0.0397        | -               |
| 0.6409 | 2700 | 0.0385        | -               |
| 0.6527 | 2750 | 0.0285        | -               |
| 0.6646 | 2800 | 0.0461        | -               |
| 0.6765 | 2850 | 0.0341        | -               |
| 0.6883 | 2900 | 0.0379        | -               |
| 0.7002 | 2950 | 0.0435        | -               |
| 0.7121 | 3000 | 0.0341        | -               |
| 0.7239 | 3050 | 0.0395        | -               |
| 0.7358 | 3100 | 0.0424        | -               |
| 0.7477 | 3150 | 0.0415        | -               |
| 0.7596 | 3200 | 0.0422        | -               |
| 0.7714 | 3250 | 0.0402        | -               |
| 0.7833 | 3300 | 0.0309        | -               |
| 0.7952 | 3350 | 0.0379        | -               |
| 0.8070 | 3400 | 0.039         | -               |
| 0.8189 | 3450 | 0.0427        | -               |
| 0.8308 | 3500 | 0.0331        | -               |
| 0.8426 | 3550 | 0.0457        | -               |
| 0.8545 | 3600 | 0.0306        | -               |
| 0.8664 | 3650 | 0.034         | -               |
| 0.8782 | 3700 | 0.0354        | -               |
| 0.8901 | 3750 | 0.0393        | -               |
| 0.9020 | 3800 | 0.036         | -               |
| 0.9138 | 3850 | 0.0339        | -               |
| 0.9257 | 3900 | 0.0332        | -               |
| 0.9376 | 3950 | 0.0274        | -               |
| 0.9494 | 4000 | 0.0372        | -               |
| 0.9613 | 4050 | 0.0319        | -               |
| 0.9732 | 4100 | 0.0339        | -               |
| 0.9850 | 4150 | 0.0349        | -               |
| 0.9969 | 4200 | 0.0383        | -               |

### Framework Versions
- Python: 3.11.13
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## 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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