---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Is it available?
- text: Est-il possible de fixer une visite?
- text: Where is it located?
- text: Pouvez-vous me parler des projets disponibles?
- text: What’s the process to reserve?
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# 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 [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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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:** 9 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                                                                                                                                                                  |
|:------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| schedule_a_visit        | <ul><li>'I’d like to schedule a visit'</li><li>'Je voudrais planifier une visite'</li><li>'Puis-je programmer une visite?'</li></ul>                                      |
| check_availability      | <ul><li>'Est-ce encore disponible?'</li><li>'Is this still available?'</li><li>'Can I check availability?'</li></ul>                                                      |
| amenities_and_features  | <ul><li>'Parlez-moi des fonctionnalités du bien'</li><li>'Tell me the features of the property'</li><li>'Quels sont les équipements disponibles?'</li></ul>               |
| payment_plan            | <ul><li>'Pouvez-vous me parler du plan de paiement?'</li><li>'Quels sont les modes de paiement disponibles?'</li><li>'What are the payment options?'</li></ul>            |
| reservation_process     | <ul><li>'Tell me about the reservation process'</li><li>'Pouvez-vous m’expliquer le processus de réservation?'</li><li>'Comment puis-je faire une réservation?'</li></ul> |
| location_details        | <ul><li>'Où est-ce situé?'</li><li>'Can you tell me the location details?'</li><li>'What’s the address?'</li></ul>                                                        |
| pricing_details         | <ul><li>'How much does it cost?'</li><li>'Tell me the pricing details'</li><li>'Combien ça coûte?'</li></ul>                                                              |
| option_process          | <ul><li>'Tell me about the option process'</li><li>'Parlez-moi du processus des options'</li><li>'Quels sont mes choix?'</li></ul>                                        |
| information_on_projects | <ul><li>'Can you give me information about the projects?'</li><li>'I need details on the available projects'</li><li>'Quels sont les projets disponibles ?'</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("ali170506/chab")
# Run inference
preds = model("Is it available?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 5.2222 | 8   |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| information_on_projects | 3                     |
| pricing_details         | 3                     |
| location_details        | 3                     |
| amenities_and_features  | 3                     |
| check_availability      | 3                     |
| schedule_a_visit        | 3                     |
| reservation_process     | 3                     |
| option_process          | 3                     |
| payment_plan            | 3                     |

### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (4, 4)
- 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: True

### Training Results
| Epoch   | Step    | Training Loss | Validation Loss |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0062  | 1       | 0.0311        | -               |
| 0.0617  | 10      | 0.0989        | -               |
| 0.1235  | 20      | 0.0036        | -               |
| 0.1852  | 30      | 0.0121        | -               |
| 0.2469  | 40      | 0.0209        | -               |
| 0.3086  | 50      | 0.001         | -               |
| 0.3704  | 60      | 0.0067        | -               |
| 0.4321  | 70      | 0.017         | -               |
| 0.4938  | 80      | 0.0037        | -               |
| 0.5556  | 90      | 0.012         | -               |
| 0.6173  | 100     | 0.0009        | -               |
| 0.6790  | 110     | 0.0044        | -               |
| 0.7407  | 120     | 0.0014        | -               |
| 0.8025  | 130     | 0.0006        | -               |
| 0.8642  | 140     | 0.0016        | -               |
| 0.9259  | 150     | 0.0024        | -               |
| 0.9877  | 160     | 0.0011        | -               |
| 1.0     | 162     | -             | 0.0164          |
| 1.0494  | 170     | 0.0019        | -               |
| 1.1111  | 180     | 0.0017        | -               |
| 1.1728  | 190     | 0.0004        | -               |
| 1.2346  | 200     | 0.0008        | -               |
| 1.2963  | 210     | 0.0012        | -               |
| 1.3580  | 220     | 0.0009        | -               |
| 1.4198  | 230     | 0.0006        | -               |
| 1.4815  | 240     | 0.001         | -               |
| 1.5432  | 250     | 0.0009        | -               |
| 1.6049  | 260     | 0.0015        | -               |
| 1.6667  | 270     | 0.0016        | -               |
| 1.7284  | 280     | 0.0009        | -               |
| 1.7901  | 290     | 0.0005        | -               |
| 1.8519  | 300     | 0.0009        | -               |
| 1.9136  | 310     | 0.0009        | -               |
| 1.9753  | 320     | 0.0008        | -               |
| 2.0     | 324     | -             | 0.0138          |
| 2.0370  | 330     | 0.0011        | -               |
| 2.0988  | 340     | 0.0016        | -               |
| 2.1605  | 350     | 0.0006        | -               |
| 2.2222  | 360     | 0.0012        | -               |
| 2.2840  | 370     | 0.0014        | -               |
| 2.3457  | 380     | 0.0009        | -               |
| 2.4074  | 390     | 0.0008        | -               |
| 2.4691  | 400     | 0.0003        | -               |
| 2.5309  | 410     | 0.0002        | -               |
| 2.5926  | 420     | 0.0007        | -               |
| 2.6543  | 430     | 0.001         | -               |
| 2.7160  | 440     | 0.0008        | -               |
| 2.7778  | 450     | 0.0008        | -               |
| 2.8395  | 460     | 0.0003        | -               |
| 2.9012  | 470     | 0.0004        | -               |
| 2.9630  | 480     | 0.0003        | -               |
| **3.0** | **486** | **-**         | **0.0129**      |
| 3.0247  | 490     | 0.0013        | -               |
| 3.0864  | 500     | 0.0006        | -               |
| 3.1481  | 510     | 0.0008        | -               |
| 3.2099  | 520     | 0.0001        | -               |
| 3.2716  | 530     | 0.0007        | -               |
| 3.3333  | 540     | 0.0004        | -               |
| 3.3951  | 550     | 0.0004        | -               |
| 3.4568  | 560     | 0.0003        | -               |
| 3.5185  | 570     | 0.0003        | -               |
| 3.5802  | 580     | 0.0002        | -               |
| 3.6420  | 590     | 0.0002        | -               |
| 3.7037  | 600     | 0.0002        | -               |
| 3.7654  | 610     | 0.0007        | -               |
| 3.8272  | 620     | 0.0007        | -               |
| 3.8889  | 630     | 0.0007        | -               |
| 3.9506  | 640     | 0.0003        | -               |
| 4.0     | 648     | -             | 0.0129          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.37.0
- PyTorch: 2.4.1+cu121
- Datasets: 3.0.1
- Tokenizers: 0.15.2

## 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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