---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Your service is bad
- text: Your products are not good
- text: What is the capital of France?
- text: Bye
- text: order email
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---

# 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:** 11 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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### 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                                                                                                                           |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------|
| 6     | <ul><li>'Why did my payment fail?'</li><li>'Why is my payment not processing?'</li><li>'What are common payment issues?'</li></ul> |
| 0     | <ul><li>'Your products are not good'</li><li>'Your service is bad'</li></ul>                                                       |
| 8     | <ul><li>'Good Bye'</li><li>'Take care'</li><li>'Bye'</li></ul>                                                                     |
| 9     | <ul><li>'Thanks'</li></ul>                                                                                                         |
| 10    | <ul><li>'23234dfdff'</li><li>'abc'</li><li>'djfnknf'</li></ul>                                                                     |
| 4     | <ul><li>'My order ref is '</li><li>'Order Ref is C-123-P'</li><li>'Order id is C-123-P'</li></ul>                                  |
| 5     | <ul><li>'How can I track my order?'</li><li>'When will my order arrive?'</li><li>"I didn't receive my COD points"</li></ul>        |
| 7     | <ul><li>'What is your refund policy?'</li><li>'How can I get a refund?'</li></ul>                                                  |
| 3     | <ul><li>'order email'</li><li>'resend order email'</li><li>'I didnt receive my order email'</li></ul>                              |
| 2     | <ul><li>'How are you'</li></ul>                                                                                                    |
| 1     | <ul><li>'Hi'</li><li>'Wassup'</li><li>'Hello'</li></ul>                                                                            |

## 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("setfit_model_id")
# Run inference
preds = model("Bye")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 3.4737 | 6   |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 2                     |
| 1     | 3                     |
| 2     | 1                     |
| 3     | 3                     |
| 4     | 5                     |
| 5     | 7                     |
| 6     | 3                     |
| 7     | 2                     |
| 8     | 3                     |
| 9     | 1                     |
| 10    | 8                     |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (5, 5)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0105 | 1    | 0.3591        | -               |
| 0.5263 | 50   | 0.1235        | -               |
| 1.0526 | 100  | 0.0233        | -               |
| 1.5789 | 150  | 0.0057        | -               |
| 2.1053 | 200  | 0.0032        | -               |
| 2.6316 | 250  | 0.0025        | -               |
| 3.1579 | 300  | 0.002         | -               |
| 3.6842 | 350  | 0.0017        | -               |
| 4.2105 | 400  | 0.0019        | -               |
| 4.7368 | 450  | 0.0016        | -               |

### Framework Versions
- Python: 3.10.13
- SetFit: 1.1.1
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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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