---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: What's today's date?
- text: Yes, please.
- text: I’d like to go to floor 2.
- text: Alright, floor 1 it is.
- text: Which floor can I find Martin Giese on?
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>                             |
| 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> |
| Feedback                | <ul><li>'Okay, going to the 3rd floor.'</li><li>'Sure, heading to floor 5.'</li><li>'Understood, taking you to the 8th floor.'</li></ul>                   |
| Repeat                  | <ul><li>'Can you repeat that?'</li><li>'Sorry, I didn’t get that. Can you say it again?'</li><li>'What was that?'</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")
# Run inference
preds = model("Yes, please.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 5.2267 | 10  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| Confirm                 | 22                    |
| CurrentFloor            | 21                    |
| Feedback                | 22                    |
| OutOfCoverage           | 22                    |
| Repeat                  | 20                    |
| RequestEmployeeLocation | 22                    |
| RequestMoveToFloor      | 23                    |
| 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0012 | 1    | 0.0001        | -               |
| 0.0618 | 50   | 0.0001        | -               |
| 0.1236 | 100  | 0.0001        | -               |
| 0.1854 | 150  | 0.0001        | -               |
| 0.2472 | 200  | 0.0001        | -               |
| 0.3090 | 250  | 0.0001        | -               |
| 0.3708 | 300  | 0.0001        | -               |
| 0.4326 | 350  | 0.0001        | -               |
| 0.4944 | 400  | 0.0001        | -               |
| 0.5562 | 450  | 0.0001        | -               |
| 0.6180 | 500  | 0.0001        | -               |
| 0.6799 | 550  | 0.0001        | -               |
| 0.7417 | 600  | 0.0012        | -               |
| 0.8035 | 650  | 0.0001        | -               |
| 0.8653 | 700  | 0.0001        | -               |
| 0.9271 | 750  | 0.0012        | -               |
| 0.9889 | 800  | 0.0001        | -               |
| 1.0507 | 850  | 0.0001        | -               |
| 1.1125 | 900  | 0.0001        | -               |
| 1.1743 | 950  | 0.0001        | -               |
| 1.2361 | 1000 | 0.0001        | -               |
| 1.2979 | 1050 | 0.0001        | -               |
| 1.3597 | 1100 | 0.0001        | -               |
| 1.4215 | 1150 | 0.0001        | -               |
| 1.4833 | 1200 | 0.0001        | -               |
| 1.5451 | 1250 | 0.0001        | -               |
| 1.6069 | 1300 | 0.0001        | -               |
| 1.6687 | 1350 | 0.0001        | -               |
| 1.7305 | 1400 | 0.0001        | -               |
| 1.7923 | 1450 | 0.0001        | -               |
| 1.8541 | 1500 | 0.0023        | -               |
| 1.9159 | 1550 | 0.0018        | -               |
| 1.9778 | 1600 | 0.0007        | -               |
| 2.0396 | 1650 | 0.0001        | -               |
| 2.1014 | 1700 | 0.0001        | -               |
| 2.1632 | 1750 | 0.0001        | -               |
| 2.2250 | 1800 | 0.0001        | -               |
| 2.2868 | 1850 | 0.0001        | -               |
| 2.3486 | 1900 | 0.0001        | -               |
| 2.4104 | 1950 | 0.0001        | -               |
| 2.4722 | 2000 | 0.0001        | -               |
| 2.5340 | 2050 | 0.0001        | -               |
| 2.5958 | 2100 | 0.0001        | -               |
| 2.6576 | 2150 | 0.0001        | -               |
| 2.7194 | 2200 | 0.0001        | -               |
| 2.7812 | 2250 | 0.0001        | -               |
| 2.8430 | 2300 | 0.0001        | -               |
| 2.9048 | 2350 | 0.0001        | -               |
| 2.9666 | 2400 | 0.0001        | -               |
| 3.0284 | 2450 | 0.0001        | -               |
| 3.0902 | 2500 | 0.0001        | -               |
| 3.1520 | 2550 | 0.0001        | -               |
| 3.2138 | 2600 | 0.0001        | -               |
| 3.2756 | 2650 | 0.0001        | -               |
| 3.3375 | 2700 | 0.0001        | -               |
| 3.3993 | 2750 | 0.0001        | -               |
| 3.4611 | 2800 | 0.0001        | -               |
| 3.5229 | 2850 | 0.0001        | -               |
| 3.5847 | 2900 | 0.0001        | -               |
| 3.6465 | 2950 | 0.0001        | -               |
| 3.7083 | 3000 | 0.0001        | -               |
| 3.7701 | 3050 | 0.0001        | -               |
| 3.8319 | 3100 | 0.0           | -               |
| 3.8937 | 3150 | 0.0           | -               |
| 3.9555 | 3200 | 0.0001        | -               |
| 4.0173 | 3250 | 0.0001        | -               |
| 4.0791 | 3300 | 0.0           | -               |
| 4.1409 | 3350 | 0.0001        | -               |
| 4.2027 | 3400 | 0.0001        | -               |
| 4.2645 | 3450 | 0.0001        | -               |
| 4.3263 | 3500 | 0.0           | -               |
| 4.3881 | 3550 | 0.0001        | -               |
| 4.4499 | 3600 | 0.0001        | -               |
| 4.5117 | 3650 | 0.0           | -               |
| 4.5735 | 3700 | 0.0           | -               |
| 4.6354 | 3750 | 0.0           | -               |
| 4.6972 | 3800 | 0.0001        | -               |
| 4.7590 | 3850 | 0.0           | -               |
| 4.8208 | 3900 | 0.0           | -               |
| 4.8826 | 3950 | 0.0           | -               |
| 4.9444 | 4000 | 0.0           | -               |
| 5.0062 | 4050 | 0.0           | -               |
| 5.0680 | 4100 | 0.0           | -               |
| 5.1298 | 4150 | 0.0001        | -               |
| 5.1916 | 4200 | 0.0148        | -               |
| 5.2534 | 4250 | 0.0258        | -               |
| 5.3152 | 4300 | 0.0147        | -               |
| 5.3770 | 4350 | 0.0015        | -               |
| 5.4388 | 4400 | 0.0001        | -               |
| 5.5006 | 4450 | 0.0001        | -               |
| 5.5624 | 4500 | 0.0001        | -               |
| 5.6242 | 4550 | 0.0001        | -               |
| 5.6860 | 4600 | 0.0001        | -               |
| 5.7478 | 4650 | 0.0001        | -               |
| 5.8096 | 4700 | 0.0001        | -               |
| 5.8714 | 4750 | 0.0001        | -               |
| 5.9333 | 4800 | 0.0001        | -               |
| 5.9951 | 4850 | 0.0001        | -               |
| 6.0569 | 4900 | 0.0001        | -               |
| 6.1187 | 4950 | 0.0001        | -               |
| 6.1805 | 5000 | 0.0001        | -               |
| 6.2423 | 5050 | 0.0001        | -               |
| 6.3041 | 5100 | 0.0001        | -               |
| 6.3659 | 5150 | 0.0001        | -               |
| 6.4277 | 5200 | 0.0001        | -               |
| 6.4895 | 5250 | 0.0001        | -               |
| 6.5513 | 5300 | 0.0001        | -               |
| 6.6131 | 5350 | 0.0001        | -               |
| 6.6749 | 5400 | 0.0001        | -               |
| 6.7367 | 5450 | 0.0001        | -               |
| 6.7985 | 5500 | 0.0001        | -               |
| 6.8603 | 5550 | 0.0001        | -               |
| 6.9221 | 5600 | 0.0001        | -               |
| 6.9839 | 5650 | 0.0001        | -               |
| 7.0457 | 5700 | 0.0001        | -               |
| 7.1075 | 5750 | 0.0001        | -               |
| 7.1693 | 5800 | 0.0001        | -               |
| 7.2311 | 5850 | 0.0001        | -               |
| 7.2930 | 5900 | 0.0001        | -               |
| 7.3548 | 5950 | 0.0001        | -               |
| 7.4166 | 6000 | 0.0001        | -               |
| 7.4784 | 6050 | 0.0001        | -               |
| 7.5402 | 6100 | 0.0001        | -               |
| 7.6020 | 6150 | 0.0001        | -               |
| 7.6638 | 6200 | 0.0001        | -               |
| 7.7256 | 6250 | 0.0001        | -               |
| 7.7874 | 6300 | 0.0001        | -               |
| 7.8492 | 6350 | 0.0001        | -               |
| 7.9110 | 6400 | 0.0001        | -               |
| 7.9728 | 6450 | 0.0001        | -               |
| 8.0346 | 6500 | 0.0001        | -               |
| 8.0964 | 6550 | 0.0001        | -               |
| 8.1582 | 6600 | 0.0001        | -               |
| 8.2200 | 6650 | 0.0001        | -               |
| 8.2818 | 6700 | 0.0001        | -               |
| 8.3436 | 6750 | 0.0001        | -               |
| 8.4054 | 6800 | 0.0001        | -               |
| 8.4672 | 6850 | 0.0           | -               |
| 8.5290 | 6900 | 0.0001        | -               |
| 8.5909 | 6950 | 0.0           | -               |
| 8.6527 | 7000 | 0.0           | -               |
| 8.7145 | 7050 | 0.0           | -               |
| 8.7763 | 7100 | 0.0001        | -               |
| 8.8381 | 7150 | 0.0001        | -               |
| 8.8999 | 7200 | 0.0001        | -               |
| 8.9617 | 7250 | 0.0           | -               |
| 9.0235 | 7300 | 0.0           | -               |
| 9.0853 | 7350 | 0.0           | -               |
| 9.1471 | 7400 | 0.0001        | -               |
| 9.2089 | 7450 | 0.0           | -               |
| 9.2707 | 7500 | 0.0           | -               |
| 9.3325 | 7550 | 0.0           | -               |
| 9.3943 | 7600 | 0.0001        | -               |
| 9.4561 | 7650 | 0.0001        | -               |
| 9.5179 | 7700 | 0.0           | -               |
| 9.5797 | 7750 | 0.0           | -               |
| 9.6415 | 7800 | 0.0           | -               |
| 9.7033 | 7850 | 0.0           | -               |
| 9.7651 | 7900 | 0.0001        | -               |
| 9.8269 | 7950 | 0.0           | -               |
| 9.8888 | 8000 | 0.0001        | -               |
| 9.9506 | 8050 | 0.0           | -               |

### 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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