---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Can you set an alarm?
- text: Bring me one floor higher
- text: I’d like to go to floor 2.
- text: Okay, go ahead.
- text: I’d like to go down two floors
pipeline_tag: text-classification
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:** 8 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                                                                                                                                                   |
|:------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|
| RequestMoveToFloor      | <ul><li>'Please go to the 3rd floor.'</li><li>'Can you take me to floor 5?'</li><li>'I need to go to the 8th floor.'</li></ul>                             |
| RequestMoveUp           | <ul><li>'Go one floor up'</li><li>'Take me up two floors'</li><li>'Go up three floors, please'</li></ul>                                                   |
| RequestMoveDown         | <ul><li>'Move me down one level'</li><li>'Can you take me down two floors?'</li><li>'Go down three levels'</li></ul>                                       |
| Confirm                 | <ul><li>"Yes, that's right."</li><li>'Sure.'</li><li>'Exactly.'</li></ul>                                                                                  |
| RequestEmployeeLocation | <ul><li>'Where is Erik Velldal’s office?'</li><li>'Which floor is Andreas Austeng on?'</li><li>'Can you tell me where Birthe Soppe’s office is?'</li></ul> |
| CurrentFloor            | <ul><li>'Which floor are we on?'</li><li>'What floor is this?'</li><li>'Are we on the 5th floor?'</li></ul>                                                |
| Stop                    | <ul><li>'Stop the elevator.'</li><li>"Wait, don't go to that floor."</li><li>'No, not that floor.'</li></ul>                                               |
| OutOfCoverage           | <ul><li>"What's the capital of France?"</li><li>'How many floors does this building have?'</li><li>'Can you make a phone call for me?'</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("victomoe/setfit-intent-classifier-3")
# Run inference
preds = model("Okay, go ahead.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 5.2118 | 9   |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| Confirm                 | 22                    |
| CurrentFloor            | 21                    |
| OutOfCoverage           | 22                    |
| RequestEmployeeLocation | 22                    |
| RequestMoveDown         | 20                    |
| RequestMoveToFloor      | 23                    |
| RequestMoveUp           | 20                    |
| Stop                    | 20                    |

### Training Hyperparameters
- batch_size: (32, 32)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0013 | 1    | 0.195         | -               |
| 0.0633 | 50   | 0.1877        | -               |
| 0.1266 | 100  | 0.1592        | -               |
| 0.1899 | 150  | 0.1141        | -               |
| 0.2532 | 200  | 0.0603        | -               |
| 0.3165 | 250  | 0.0283        | -               |
| 0.3797 | 300  | 0.0104        | -               |
| 0.4430 | 350  | 0.0043        | -               |
| 0.5063 | 400  | 0.0027        | -               |
| 0.5696 | 450  | 0.0021        | -               |
| 0.6329 | 500  | 0.0017        | -               |
| 0.6962 | 550  | 0.0015        | -               |
| 0.7595 | 600  | 0.0011        | -               |
| 0.8228 | 650  | 0.001         | -               |
| 0.8861 | 700  | 0.0011        | -               |
| 0.9494 | 750  | 0.0008        | -               |
| 1.0127 | 800  | 0.0007        | -               |
| 1.0759 | 850  | 0.0006        | -               |
| 1.1392 | 900  | 0.0006        | -               |
| 1.2025 | 950  | 0.0005        | -               |
| 1.2658 | 1000 | 0.0005        | -               |
| 1.3291 | 1050 | 0.0005        | -               |
| 1.3924 | 1100 | 0.0004        | -               |
| 1.4557 | 1150 | 0.0004        | -               |
| 1.5190 | 1200 | 0.0004        | -               |
| 1.5823 | 1250 | 0.0004        | -               |
| 1.6456 | 1300 | 0.0004        | -               |
| 1.7089 | 1350 | 0.0003        | -               |
| 1.7722 | 1400 | 0.0003        | -               |
| 1.8354 | 1450 | 0.0003        | -               |
| 1.8987 | 1500 | 0.0003        | -               |
| 1.9620 | 1550 | 0.0003        | -               |
| 2.0253 | 1600 | 0.0003        | -               |
| 2.0886 | 1650 | 0.0003        | -               |
| 2.1519 | 1700 | 0.0003        | -               |
| 2.2152 | 1750 | 0.0003        | -               |
| 2.2785 | 1800 | 0.0003        | -               |
| 2.3418 | 1850 | 0.0002        | -               |
| 2.4051 | 1900 | 0.0002        | -               |
| 2.4684 | 1950 | 0.0002        | -               |
| 2.5316 | 2000 | 0.0002        | -               |
| 2.5949 | 2050 | 0.0002        | -               |
| 2.6582 | 2100 | 0.0002        | -               |
| 2.7215 | 2150 | 0.0002        | -               |
| 2.7848 | 2200 | 0.0002        | -               |
| 2.8481 | 2250 | 0.0002        | -               |
| 2.9114 | 2300 | 0.0002        | -               |
| 2.9747 | 2350 | 0.0002        | -               |
| 3.0380 | 2400 | 0.0002        | -               |
| 3.1013 | 2450 | 0.0009        | -               |
| 3.1646 | 2500 | 0.0003        | -               |
| 3.2278 | 2550 | 0.0002        | -               |
| 3.2911 | 2600 | 0.0002        | -               |
| 3.3544 | 2650 | 0.0002        | -               |
| 3.4177 | 2700 | 0.0002        | -               |
| 3.4810 | 2750 | 0.0002        | -               |
| 3.5443 | 2800 | 0.0002        | -               |
| 3.6076 | 2850 | 0.0002        | -               |
| 3.6709 | 2900 | 0.0002        | -               |
| 3.7342 | 2950 | 0.0002        | -               |
| 3.7975 | 3000 | 0.0002        | -               |
| 3.8608 | 3050 | 0.0002        | -               |
| 3.9241 | 3100 | 0.0001        | -               |
| 3.9873 | 3150 | 0.0002        | -               |
| 4.0506 | 3200 | 0.0001        | -               |
| 4.1139 | 3250 | 0.0001        | -               |
| 4.1772 | 3300 | 0.0001        | -               |
| 4.2405 | 3350 | 0.0001        | -               |
| 4.3038 | 3400 | 0.0001        | -               |
| 4.3671 | 3450 | 0.0001        | -               |
| 4.4304 | 3500 | 0.0005        | -               |
| 4.4937 | 3550 | 0.0001        | -               |
| 4.5570 | 3600 | 0.0001        | -               |
| 4.6203 | 3650 | 0.0001        | -               |
| 4.6835 | 3700 | 0.0001        | -               |
| 4.7468 | 3750 | 0.0001        | -               |
| 4.8101 | 3800 | 0.0001        | -               |
| 4.8734 | 3850 | 0.0001        | -               |
| 4.9367 | 3900 | 0.0001        | -               |
| 5.0    | 3950 | 0.0001        | -               |
| 5.0633 | 4000 | 0.0001        | -               |
| 5.1266 | 4050 | 0.0001        | -               |
| 5.1899 | 4100 | 0.0001        | -               |
| 5.2532 | 4150 | 0.0001        | -               |
| 5.3165 | 4200 | 0.0001        | -               |
| 5.3797 | 4250 | 0.0001        | -               |
| 5.4430 | 4300 | 0.0001        | -               |
| 5.5063 | 4350 | 0.0001        | -               |
| 5.5696 | 4400 | 0.0001        | -               |
| 5.6329 | 4450 | 0.0001        | -               |
| 5.6962 | 4500 | 0.0001        | -               |
| 5.7595 | 4550 | 0.0001        | -               |
| 5.8228 | 4600 | 0.0001        | -               |
| 5.8861 | 4650 | 0.0001        | -               |
| 5.9494 | 4700 | 0.0001        | -               |
| 6.0127 | 4750 | 0.0001        | -               |
| 6.0759 | 4800 | 0.0001        | -               |
| 6.1392 | 4850 | 0.0001        | -               |
| 6.2025 | 4900 | 0.0001        | -               |
| 6.2658 | 4950 | 0.0001        | -               |
| 6.3291 | 5000 | 0.0001        | -               |
| 6.3924 | 5050 | 0.0001        | -               |
| 6.4557 | 5100 | 0.0001        | -               |
| 6.5190 | 5150 | 0.0001        | -               |
| 6.5823 | 5200 | 0.0001        | -               |
| 6.6456 | 5250 | 0.0001        | -               |
| 6.7089 | 5300 | 0.0001        | -               |
| 6.7722 | 5350 | 0.0001        | -               |
| 6.8354 | 5400 | 0.0001        | -               |
| 6.8987 | 5450 | 0.0001        | -               |
| 6.9620 | 5500 | 0.0001        | -               |
| 7.0253 | 5550 | 0.0001        | -               |
| 7.0886 | 5600 | 0.0001        | -               |
| 7.1519 | 5650 | 0.0001        | -               |
| 7.2152 | 5700 | 0.0001        | -               |
| 7.2785 | 5750 | 0.0001        | -               |
| 7.3418 | 5800 | 0.0001        | -               |
| 7.4051 | 5850 | 0.0001        | -               |
| 7.4684 | 5900 | 0.0001        | -               |
| 7.5316 | 5950 | 0.0001        | -               |
| 7.5949 | 6000 | 0.0001        | -               |
| 7.6582 | 6050 | 0.0001        | -               |
| 7.7215 | 6100 | 0.0001        | -               |
| 7.7848 | 6150 | 0.0001        | -               |
| 7.8481 | 6200 | 0.0001        | -               |
| 7.9114 | 6250 | 0.0001        | -               |
| 7.9747 | 6300 | 0.0001        | -               |
| 8.0380 | 6350 | 0.0001        | -               |
| 8.1013 | 6400 | 0.0001        | -               |
| 8.1646 | 6450 | 0.0001        | -               |
| 8.2278 | 6500 | 0.0001        | -               |
| 8.2911 | 6550 | 0.0001        | -               |
| 8.3544 | 6600 | 0.0001        | -               |
| 8.4177 | 6650 | 0.0001        | -               |
| 8.4810 | 6700 | 0.0001        | -               |
| 8.5443 | 6750 | 0.0001        | -               |
| 8.6076 | 6800 | 0.0001        | -               |
| 8.6709 | 6850 | 0.0001        | -               |
| 8.7342 | 6900 | 0.0001        | -               |
| 8.7975 | 6950 | 0.0001        | -               |
| 8.8608 | 7000 | 0.0001        | -               |
| 8.9241 | 7050 | 0.0001        | -               |
| 8.9873 | 7100 | 0.0001        | -               |
| 9.0506 | 7150 | 0.0001        | -               |
| 9.1139 | 7200 | 0.0001        | -               |
| 9.1772 | 7250 | 0.0001        | -               |
| 9.2405 | 7300 | 0.0001        | -               |
| 9.3038 | 7350 | 0.0001        | -               |
| 9.3671 | 7400 | 0.0001        | -               |
| 9.4304 | 7450 | 0.0001        | -               |
| 9.4937 | 7500 | 0.0001        | -               |
| 9.5570 | 7550 | 0.0001        | -               |
| 9.6203 | 7600 | 0.0001        | -               |
| 9.6835 | 7650 | 0.0001        | -               |
| 9.7468 | 7700 | 0.0001        | -               |
| 9.8101 | 7750 | 0.0001        | -               |
| 9.8734 | 7800 | 0.0001        | -               |
| 9.9367 | 7850 | 0.0001        | -               |
| 10.0   | 7900 | 0.0001        | -               |

### Framework Versions
- Python: 3.10.8
- SetFit: 1.1.0
- Sentence Transformers: 3.1.1
- Transformers: 4.38.2
- PyTorch: 2.1.2
- Datasets: 2.17.1
- 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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