---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
    Quelles sont les règles en matière de garde d'enfants et de pension
    alimentaire ?
- text: Comment se déroule une procédure de divorce ?
- text: >-
    Quelles sont les principales difficultés rencontrées dans l'application de
    cette loi ?
- text: Quels sont les régimes matrimoniaux possibles ?
- text: >-
    Comment peut-on obtenir réparation pour un préjudice subi du fait d'une
    décision administrative illégale ?
pipeline_tag: text-classification
inference: true
base_model: intfloat/multilingual-e5-small
model-index:
- name: SetFit with intfloat/multilingual-e5-small
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1
      name: Accuracy
language:
- fr
- en
---

# SetFit with intfloat/multilingual-e5-small

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 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                                                                                                                                                                                                                      |
|:------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| independent | <ul><li>'Comment rédiger un contrat de travail ?'</li><li>'Quels sont les impôts et taxes applicables aux entreprises ?'</li><li>'Comment peut-on contester un licenciement abusif ?'</li></ul>                               |
| follow_up   | <ul><li>'Quelles sont les conséquences de cette loi ?'</li><li>"Comment cette loi s'inscrit-elle dans le cadre plus large du droit algérien ?"</li><li>"Comment puis-je obtenir plus d'informations sur ce sujet ?"</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("super-cinnamon/fewshot-followup-multi-e5")
# Run inference
preds = model("Comment se déroule une procédure de divorce ?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 9.6184 | 16  |

| Label       | Training Sample Count |
|:------------|:----------------------|
| independent | 43                    |
| follow_up   | 33                    |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0027 | 1    | 0.3915        | -               |
| 0.1326 | 50   | 0.3193        | -               |
| 0.2653 | 100  | 0.2252        | -               |
| 0.3979 | 150  | 0.1141        | -               |
| 0.5305 | 200  | 0.0197        | -               |
| 0.6631 | 250  | 0.0019        | -               |
| 0.7958 | 300  | 0.0021        | -               |
| 0.9284 | 350  | 0.0002        | -               |
| 1.0610 | 400  | 0.0008        | -               |
| 1.1936 | 450  | 0.0005        | -               |
| 1.3263 | 500  | 0.0002        | -               |
| 1.4589 | 550  | 0.0002        | -               |
| 1.5915 | 600  | 0.0007        | -               |
| 1.7241 | 650  | 0.0001        | -               |
| 1.8568 | 700  | 0.0003        | -               |
| 1.9894 | 750  | 0.0002        | -               |
| 2.1220 | 800  | 0.0001        | -               |
| 2.2546 | 850  | 0.0002        | -               |
| 2.3873 | 900  | 0.0           | -               |
| 2.5199 | 950  | 0.0003        | -               |
| 2.6525 | 1000 | 0.0001        | -               |
| 2.7851 | 1050 | 0.0001        | -               |
| 2.9178 | 1100 | 0.0001        | -               |
| 3.0504 | 1150 | 0.0001        | -               |
| 3.1830 | 1200 | 0.0001        | -               |
| 3.3156 | 1250 | 0.0001        | -               |
| 3.4483 | 1300 | 0.0001        | -               |
| 3.5809 | 1350 | 0.0001        | -               |
| 3.7135 | 1400 | 0.0           | -               |
| 3.8462 | 1450 | 0.0           | -               |
| 3.9788 | 1500 | 0.0           | -               |
| 4.1114 | 1550 | 0.0           | -               |
| 4.2440 | 1600 | 0.0001        | -               |
| 4.3767 | 1650 | 0.0001        | -               |
| 4.5093 | 1700 | 0.0001        | -               |
| 4.6419 | 1750 | 0.0001        | -               |
| 4.7745 | 1800 | 0.0           | -               |
| 4.9072 | 1850 | 0.0001        | -               |
| 5.0398 | 1900 | 0.0           | -               |
| 5.1724 | 1950 | 0.0001        | -               |
| 5.3050 | 2000 | 0.0           | -               |
| 5.4377 | 2050 | 0.0001        | -               |
| 5.5703 | 2100 | 0.0           | -               |
| 5.7029 | 2150 | 0.0           | -               |
| 5.8355 | 2200 | 0.0           | -               |
| 5.9682 | 2250 | 0.0001        | -               |
| 6.1008 | 2300 | 0.0001        | -               |
| 6.2334 | 2350 | 0.0           | -               |
| 6.3660 | 2400 | 0.0001        | -               |
| 6.4987 | 2450 | 0.0           | -               |
| 6.6313 | 2500 | 0.0           | -               |
| 6.7639 | 2550 | 0.0           | -               |
| 6.8966 | 2600 | 0.0           | -               |
| 7.0292 | 2650 | 0.0           | -               |
| 7.1618 | 2700 | 0.0           | -               |
| 7.2944 | 2750 | 0.0           | -               |
| 7.4271 | 2800 | 0.0001        | -               |
| 7.5597 | 2850 | 0.0           | -               |
| 7.6923 | 2900 | 0.0           | -               |
| 7.8249 | 2950 | 0.0           | -               |
| 7.9576 | 3000 | 0.0           | -               |
| 8.0902 | 3050 | 0.0           | -               |
| 8.2228 | 3100 | 0.0           | -               |
| 8.3554 | 3150 | 0.0           | -               |
| 8.4881 | 3200 | 0.0001        | -               |
| 8.6207 | 3250 | 0.0           | -               |
| 8.7533 | 3300 | 0.0           | -               |
| 8.8859 | 3350 | 0.0           | -               |
| 9.0186 | 3400 | 0.0001        | -               |
| 9.1512 | 3450 | 0.0           | -               |
| 9.2838 | 3500 | 0.0           | -               |
| 9.4164 | 3550 | 0.0001        | -               |
| 9.5491 | 3600 | 0.0           | -               |
| 9.6817 | 3650 | 0.0001        | -               |
| 9.8143 | 3700 | 0.0           | -               |
| 9.9469 | 3750 | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu118
- Datasets: 2.15.0
- Tokenizers: 0.15.0

## 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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