---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Crisis Group telephone interview, UNRWA official, November 2023.
- text: Testimony of Maryam al-Khdeirat (55) from Khirbet Zanutah, Hebron 14 Box 3.
- text: Consulte los materiales adjuntos para lecturas adicionales.
- text: Témoignage de Leila, réfugiée syrienne en Jordanie 29 Boîte 4.
- text: Interview téléphonique, représentant de l'ONU, février 2024.
inference: true
---

# SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 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>'In addition to date, UNFPA has distributed dignity kits to 12,650 people through partners.'</li><li>'In particular, WHO, acting on the eight pillars of the global WHO Strategic Preparedness and Response Plan, continues engaging the MoH and health partners to enhance technical capacity and awareness, including on rational use of PPEs, case management, infection prevention and control, environmental disinfection, and risk communication; and is focused on procuring and enhancing integral medical supplies including in laboratory testing and PPE for case management and healthcare facilities'</li><li>'Adicionalmente, la propuesta incluyóla entrega de mercados para asistencia alimentaria al menos a 244 personas sobrevivientes de Minas Antipersonal (MAP), Municiones sin Explotar (MSE) y/o Artefactos Explosivos Improvisados (AEI) y sus núcleos familiares.'</li></ul> |
| 0     | <ul><li>'Labor market indicators by age 42 List of figures Figure 2.'</li><li>'Women’s involvement in conflict mediation: percentage of women leading initiatives 52 List of boxes Box 2.'</li><li>'Entrevista telefónica, funcionario de la ONU, octubre de 2023.'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |

## 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("setfit_model_id")
# Run inference
preds = model("Consulte los materiales adjuntos para lecturas adicionales.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 24.6961 | 85  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 81                    |
| 1     | 100                   |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 35
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0025 | 1    | 0.3104        | -               |
| 0.1263 | 50   | 0.2567        | -               |
| 0.2525 | 100  | 0.0406        | -               |
| 0.3788 | 150  | 0.0034        | -               |
| 0.5051 | 200  | 0.0017        | -               |
| 0.6313 | 250  | 0.0012        | -               |
| 0.7576 | 300  | 0.0009        | -               |
| 0.8838 | 350  | 0.0008        | -               |

### Framework Versions
- Python: 3.11.5
- SetFit: 1.1.0
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.1.0
- Datasets: 2.17.1
- Tokenizers: 0.20.0

## 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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