---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Quels sont les recours possibles en cas de conflit entre un employeur et un
    employé ?
- text: Comment déclarer mes impôts et taxes ?
- text: Quelles sont les règles de tenue de la comptabilité ?
- text: Quels sont les frais associés à cette procédure ?
- text: Quelles sont les procédures de recours possibles contre une décision administrative
    ?
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: 0.9473684210526315
      name: Accuracy
---

# 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                                                                                                                                                                                                                                                |
|:------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| follow_up   | <ul><li>'Quels sont les régimes matrimoniaux possibles ?'</li><li>'Quelles sont les conséquences économiques ou sociales de cette loi ?'</li><li>"Est-ce que cette loi s'applique à mon cas particulier ?"</li></ul>                                    |
| independent | <ul><li>'Quelles sont les règles en matière de temps de travail et de congés ?'</li><li>"Quels sont les types de structures d'entreprise autorisés en Algérie ?"</li><li>'Quels sont les droits et obligations des travailleurs en Algérie ?'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9474   |

## 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 déclarer mes impôts et taxes ?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 9.76   | 16  |

| Label       | Training Sample Count |
|:------------|:----------------------|
| independent | 39                    |
| follow_up   | 36                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0028 | 1    | 0.3779        | -               |
| 0.1381 | 50   | 0.3395        | -               |
| 0.2762 | 100  | 0.1385        | -               |
| 0.4144 | 150  | 0.1179        | -               |
| 0.5525 | 200  | 0.0172        | -               |
| 0.6906 | 250  | 0.0006        | -               |
| 0.8287 | 300  | 0.0014        | -               |
| 0.9669 | 350  | 0.0004        | -               |
| 1.1050 | 400  | 0.0002        | -               |
| 1.2431 | 450  | 0.0002        | -               |
| 1.3812 | 500  | 0.0002        | -               |
| 1.5193 | 550  | 0.0005        | -               |
| 1.6575 | 600  | 0.0001        | -               |
| 1.7956 | 650  | 0.0001        | -               |
| 1.9337 | 700  | 0.0001        | -               |
| 2.0718 | 750  | 0.0002        | -               |
| 2.2099 | 800  | 0.0001        | -               |
| 2.3481 | 850  | 0.0002        | -               |
| 2.4862 | 900  | 0.0003        | -               |
| 2.6243 | 950  | 0.0001        | -               |
| 2.7624 | 1000 | 0.0001        | -               |
| 2.9006 | 1050 | 0.0001        | -               |
| 3.0387 | 1100 | 0.0           | -               |
| 3.1768 | 1150 | 0.0001        | -               |
| 3.3149 | 1200 | 0.0001        | -               |
| 3.4530 | 1250 | 0.0001        | -               |
| 3.5912 | 1300 | 0.0001        | -               |
| 3.7293 | 1350 | 0.0           | -               |
| 3.8674 | 1400 | 0.0001        | -               |
| 4.0055 | 1450 | 0.0001        | -               |
| 4.1436 | 1500 | 0.0001        | -               |
| 4.2818 | 1550 | 0.0002        | -               |
| 4.4199 | 1600 | 0.0001        | -               |
| 4.5580 | 1650 | 0.0001        | -               |
| 4.6961 | 1700 | 0.0002        | -               |
| 4.8343 | 1750 | 0.0           | -               |
| 4.9724 | 1800 | 0.0001        | -               |
| 5.1105 | 1850 | 0.0           | -               |
| 5.2486 | 1900 | 0.0001        | -               |
| 5.3867 | 1950 | 0.0           | -               |
| 5.5249 | 2000 | 0.0           | -               |
| 5.6630 | 2050 | 0.0001        | -               |
| 5.8011 | 2100 | 0.0           | -               |
| 5.9392 | 2150 | 0.0           | -               |
| 6.0773 | 2200 | 0.0001        | -               |
| 6.2155 | 2250 | 0.0001        | -               |
| 6.3536 | 2300 | 0.0001        | -               |
| 6.4917 | 2350 | 0.0           | -               |
| 6.6298 | 2400 | 0.0           | -               |
| 6.7680 | 2450 | 0.0           | -               |
| 6.9061 | 2500 | 0.0           | -               |
| 7.0442 | 2550 | 0.0           | -               |
| 7.1823 | 2600 | 0.0001        | -               |
| 7.3204 | 2650 | 0.0           | -               |
| 7.4586 | 2700 | 0.0           | -               |
| 7.5967 | 2750 | 0.0001        | -               |
| 7.7348 | 2800 | 0.0           | -               |
| 7.8729 | 2850 | 0.0001        | -               |
| 8.0110 | 2900 | 0.0           | -               |
| 8.1492 | 2950 | 0.0           | -               |
| 8.2873 | 3000 | 0.0           | -               |
| 8.4254 | 3050 | 0.0           | -               |
| 8.5635 | 3100 | 0.0001        | -               |
| 8.7017 | 3150 | 0.0           | -               |
| 8.8398 | 3200 | 0.0001        | -               |
| 8.9779 | 3250 | 0.0           | -               |
| 9.1160 | 3300 | 0.0           | -               |
| 9.2541 | 3350 | 0.0           | -               |
| 9.3923 | 3400 | 0.0           | -               |
| 9.5304 | 3450 | 0.0           | -               |
| 9.6685 | 3500 | 0.0           | -               |
| 9.8066 | 3550 | 0.0           | -               |
| 9.9448 | 3600 | 0.0           | -               |

### 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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