setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2 region:us
---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: I'd like to go up one floor
- text: I’d like to go to floor 2.
- text: Which office is Yngvar located in?
- text: Yes, proceed.
- text: Absolutely.
inference: true
---

# 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:** 7 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** 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>                             |
| RequestMoveToFloorByX   | <ul><li>'Go one floor up'</li><li>'Take me up two floors'</li><li>'Move me down one level'</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-2")
# Run inference
preds = model("Absolutely.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 5.1533 | 9   |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| Confirm                 | 22                    |
| CurrentFloor            | 21                    |
| OutOfCoverage           | 22                    |
| RequestEmployeeLocation | 22                    |
| RequestMoveToFloor      | 23                    |
| RequestMoveToFloorByX   | 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0017 | 1    | 0.1415        | -               |
| 0.0829 | 50   | 0.1863        | -               |
| 0.1658 | 100  | 0.1559        | -               |
| 0.2488 | 150  | 0.0966        | -               |
| 0.3317 | 200  | 0.0363        | -               |
| 0.4146 | 250  | 0.009         | -               |
| 0.4975 | 300  | 0.0035        | -               |
| 0.5804 | 350  | 0.0024        | -               |
| 0.6633 | 400  | 0.0017        | -               |
| 0.7463 | 450  | 0.0015        | -               |
| 0.8292 | 500  | 0.0011        | -               |
| 0.9121 | 550  | 0.0009        | -               |
| 0.9950 | 600  | 0.0008        | -               |
| 1.0779 | 650  | 0.0007        | -               |
| 1.1609 | 700  | 0.0006        | -               |
| 1.2438 | 750  | 0.0005        | -               |
| 1.3267 | 800  | 0.0005        | -               |
| 1.4096 | 850  | 0.0005        | -               |
| 1.4925 | 900  | 0.0007        | -               |
| 1.5755 | 950  | 0.0004        | -               |
| 1.6584 | 1000 | 0.0004        | -               |
| 1.7413 | 1050 | 0.0004        | -               |
| 1.8242 | 1100 | 0.0004        | -               |
| 1.9071 | 1150 | 0.0003        | -               |
| 1.9900 | 1200 | 0.0003        | -               |
| 2.0730 | 1250 | 0.0003        | -               |
| 2.1559 | 1300 | 0.0003        | -               |
| 2.2388 | 1350 | 0.0003        | -               |
| 2.3217 | 1400 | 0.0003        | -               |
| 2.4046 | 1450 | 0.0003        | -               |
| 2.4876 | 1500 | 0.0003        | -               |
| 2.5705 | 1550 | 0.0002        | -               |
| 2.6534 | 1600 | 0.0002        | -               |
| 2.7363 | 1650 | 0.0004        | -               |
| 2.8192 | 1700 | 0.0002        | -               |
| 2.9022 | 1750 | 0.0002        | -               |
| 2.9851 | 1800 | 0.0002        | -               |
| 3.0680 | 1850 | 0.0002        | -               |
| 3.1509 | 1900 | 0.0002        | -               |
| 3.2338 | 1950 | 0.0002        | -               |
| 3.3167 | 2000 | 0.0002        | -               |
| 3.3997 | 2050 | 0.0002        | -               |
| 3.4826 | 2100 | 0.0002        | -               |
| 3.5655 | 2150 | 0.0002        | -               |
| 3.6484 | 2200 | 0.0002        | -               |
| 3.7313 | 2250 | 0.0002        | -               |
| 3.8143 | 2300 | 0.0002        | -               |
| 3.8972 | 2350 | 0.0002        | -               |
| 3.9801 | 2400 | 0.0002        | -               |
| 4.0630 | 2450 | 0.0002        | -               |
| 4.1459 | 2500 | 0.0002        | -               |
| 4.2289 | 2550 | 0.0002        | -               |
| 4.3118 | 2600 | 0.0002        | -               |
| 4.3947 | 2650 | 0.0002        | -               |
| 4.4776 | 2700 | 0.0002        | -               |
| 4.5605 | 2750 | 0.0002        | -               |
| 4.6434 | 2800 | 0.0001        | -               |
| 4.7264 | 2850 | 0.0001        | -               |
| 4.8093 | 2900 | 0.0001        | -               |
| 4.8922 | 2950 | 0.0001        | -               |
| 4.9751 | 3000 | 0.0001        | -               |
| 5.0580 | 3050 | 0.0001        | -               |
| 5.1410 | 3100 | 0.0001        | -               |
| 5.2239 | 3150 | 0.0001        | -               |
| 5.3068 | 3200 | 0.0001        | -               |
| 5.3897 | 3250 | 0.0001        | -               |
| 5.4726 | 3300 | 0.0001        | -               |
| 5.5556 | 3350 | 0.0003        | -               |
| 5.6385 | 3400 | 0.0004        | -               |
| 5.7214 | 3450 | 0.0001        | -               |
| 5.8043 | 3500 | 0.0001        | -               |
| 5.8872 | 3550 | 0.0001        | -               |
| 5.9701 | 3600 | 0.0001        | -               |
| 6.0531 | 3650 | 0.0001        | -               |
| 6.1360 | 3700 | 0.0001        | -               |
| 6.2189 | 3750 | 0.0001        | -               |
| 6.3018 | 3800 | 0.0001        | -               |
| 6.3847 | 3850 | 0.0001        | -               |
| 6.4677 | 3900 | 0.0001        | -               |
| 6.5506 | 3950 | 0.0001        | -               |
| 6.6335 | 4000 | 0.0001        | -               |
| 6.7164 | 4050 | 0.0001        | -               |
| 6.7993 | 4100 | 0.0001        | -               |
| 6.8823 | 4150 | 0.0001        | -               |
| 6.9652 | 4200 | 0.0001        | -               |
| 7.0481 | 4250 | 0.0001        | -               |
| 7.1310 | 4300 | 0.0001        | -               |
| 7.2139 | 4350 | 0.0001        | -               |
| 7.2968 | 4400 | 0.0001        | -               |
| 7.3798 | 4450 | 0.0001        | -               |
| 7.4627 | 4500 | 0.0001        | -               |
| 7.5456 | 4550 | 0.0001        | -               |
| 7.6285 | 4600 | 0.0001        | -               |
| 7.7114 | 4650 | 0.0001        | -               |
| 7.7944 | 4700 | 0.0001        | -               |
| 7.8773 | 4750 | 0.0001        | -               |
| 7.9602 | 4800 | 0.0001        | -               |
| 8.0431 | 4850 | 0.0001        | -               |
| 8.1260 | 4900 | 0.0001        | -               |
| 8.2090 | 4950 | 0.0001        | -               |
| 8.2919 | 5000 | 0.0001        | -               |
| 8.3748 | 5050 | 0.0001        | -               |
| 8.4577 | 5100 | 0.0001        | -               |
| 8.5406 | 5150 | 0.0001        | -               |
| 8.6235 | 5200 | 0.0001        | -               |
| 8.7065 | 5250 | 0.0001        | -               |
| 8.7894 | 5300 | 0.0001        | -               |
| 8.8723 | 5350 | 0.0001        | -               |
| 8.9552 | 5400 | 0.0001        | -               |
| 9.0381 | 5450 | 0.0001        | -               |
| 9.1211 | 5500 | 0.0001        | -               |
| 9.2040 | 5550 | 0.0001        | -               |
| 9.2869 | 5600 | 0.0001        | -               |
| 9.3698 | 5650 | 0.0001        | -               |
| 9.4527 | 5700 | 0.0001        | -               |
| 9.5357 | 5750 | 0.0001        | -               |
| 9.6186 | 5800 | 0.0001        | -               |
| 9.7015 | 5850 | 0.0001        | -               |
| 9.7844 | 5900 | 0.0001        | -               |
| 9.8673 | 5950 | 0.0001        | -               |
| 9.9502 | 6000 | 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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