
# SetFit with sentence-transformers/paraphrase-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **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:** 3 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                                                                                                                                                                                                                                            |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2     | <ul><li>'Rapid onset of confusion and weakness, urgent evaluation needed.'</li><li>'Unconscious patient found, immediate medical response required.'</li><li>'Urgent: Suspected heart attack, immediate medical attention required.'</li></ul>      |
| 1     | <ul><li>'Reminder: Your dental check-up is scheduled for Monday, February 05.'</li><li>'Reminder: Your dental check-up is scheduled for Saturday, February 24.'</li><li>'Nutritionist appointment reminder for Sunday, January 21.'</li></ul>       |
| 0     | <ul><li>'Could you verify your lifestyle contact details in our records?'</li><li>'Kindly update your emergency contact list at your earliest convenience.'</li><li>'We request you to update your wellness information for our records.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.85     |

## 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("konsman/setfit-messages-generated-test")
# Run inference
preds = model("Sudden severe chest pain, suspecting a cardiac emergency.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 7   | 10.125 | 12  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 16                    |
| 1     | 16                    |
| 2     | 16                    |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2.2041595048800003e-05, 2.2041595048800003e-05)
- head_learning_rate: 2.2041595048800003e-05
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0021 | 1    | 0.2841        | -               |
| 0.1042 | 50   | 0.0603        | -               |
| 0.2083 | 100  | 0.0017        | -               |
| 0.3125 | 150  | 0.0003        | -               |
| 0.4167 | 200  | 0.0004        | -               |
| 0.5208 | 250  | 0.0003        | -               |
| 0.625  | 300  | 0.0003        | -               |
| 0.7292 | 350  | 0.0002        | -               |
| 0.8333 | 400  | 0.0003        | -               |
| 0.9375 | 450  | 0.0001        | -               |
| 1.0417 | 500  | 0.0002        | -               |
| 1.1458 | 550  | 0.0003        | -               |
| 1.25   | 600  | 0.0002        | -               |
| 1.3542 | 650  | 0.0002        | -               |
| 1.4583 | 700  | 0.0001        | -               |
| 1.5625 | 750  | 0.0002        | -               |
| 1.6667 | 800  | 0.0001        | -               |
| 1.7708 | 850  | 0.0001        | -               |
| 1.875  | 900  | 0.0001        | -               |
| 1.9792 | 950  | 0.0002        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- 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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{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "A gentle nudge to complete the healthcare webinar questionnaire sent last week."}, {"text": "Sudden severe chest pain, suspecting a cardiac emergency."}, {"text": "Annual physical examination due in Tuesday, March 05. Please book an appointment."}, {"text": "Please confirm your attendance at the lifestyle next month."}, {"text": "Could you verify your emergency contact details in our records?"}], "pipeline_tag": "text-classification", "inference": true, "base_model": "sentence-transformers/paraphrase-mpnet-base-v2", "model-index": [{"name": "SetFit with sentence-transformers/paraphrase-mpnet-base-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.85, "name": "Accuracy"}]}]}]}
setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us