---
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Ce sont des travaux très pénibles qui nuisent à leur santé physique.
- text: Besides, 4 disinfection spray machines provided to Patuakhali RC Unit.
- text: Pese a los beneficios descritos anteriormente, Moody’s también advierte que
    la migración puede traer consigo un incremento en la tasa de desempleo de los
    trabajadores locales.
- text: More people in NSAG/TBAF areas view things in a positive light now (41%) than
    in May (36%), but focal points in this AoC are still the least certain that precautionary
    measures will have an impact.
- text: 'The observed spike was driven by the increased number of interviewed returnees’
    households reporting poor food consumption: almost double from July to August
    2020.'
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.75
      name: Accuracy
---

# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'HLPSS partners also held successful negotiations to halt a planned eviction of 559 IDPs from Biafra Camp Bulabulin in MMC LGA, when the IDPs were unable to meet landowners’ demands to pay between 500 to 1000 Naira monthly as rent since October 2019.'</li><li>'Sin embargo, un prestador del servicio de aseo encontró dificultad al momento de comprar: cepillos, guantes y escobas.'</li><li>'Conflict results in frequent civilian harm and atrocities have been committed in the area, including against children; populations are also subject to recurrent forced displacement.'</li></ul> |
| 0     | <ul><li>'En menor proporción y contrario a estos eventos, en Norte de Santander se reportaron afectaciones por la sequía propia de la temporada.'</li><li>'Cette situation est relativement meilleure comparé à la MAM mais l’objectif national de 70% n’est pas atteint.'</li><li>'These figures are consistent with those from the June and May consultations.'</li></ul>                                                                                                                                                                                                                                    |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.75     |

## 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("osmedi/sentence_independancy_model")
# Run inference
preds = model("Ce sont des travaux très pénibles qui nuisent à leur santé physique.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 25.1481 | 78  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 54                    |
| 1     | 54                    |

### 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0037 | 1    | 0.3515        | -               |
| 0.1852 | 50   | 0.2656        | -               |
| 0.3704 | 100  | 0.1631        | -               |
| 0.5556 | 150  | 0.0073        | -               |
| 0.7407 | 200  | 0.0016        | -               |
| 0.9259 | 250  | 0.001         | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Datasets: 3.0.1
- 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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