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 model-index 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: Do you have the Nike Blazer Mid sacai Snow Beach in size 9?
- text: How can I adapt K-beauty routines for dry weather?
- text: I like to listen to classical music
- text: If this product is for weight management, what is the sub-category?
- text: How long does it take to receive a refund after returning a product?
inference: true
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: 0.8711340206185567
      name: Accuracy
---

# 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:** 6 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                                                                                                                                                                                                                                                                                       |
|:------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| product policy          | <ul><li>'Do you offer a gift wrapping service for sneakers?'</li><li>'What are the consequences if my account is suspended or terminated for any reason?'</li><li>'Do you share my personal information with third parties?'</li></ul>                                                         |
| general faq             | <ul><li>'Can you explain why Mashru silk is considered more comfortable to wear compared to pure silk sarees?'</li><li>'What are some tips for maximizing the antioxidant content when brewing green tea?'</li><li>'Can you recommend K-beauty products for hot and humid climates?'</li></ul> |
| product discoverability | <ul><li>'Are there any sarees with Kadwa Weave technique?'</li><li>'cookie boxes with dividers'</li><li>'Are there any products for dry skin?'</li></ul>                                                                                                                                       |
| Out of Scope            | <ul><li>'Is this website secure?'</li><li>'How do you handle intellectual property disputes?'</li><li>'Do you know how to play the piano?'</li></ul>                                                                                                                                           |
| order tracking          | <ul><li>'I want to deliver candle supplies to Jaipur, how many days will it take to deliver?'</li><li>'I want to deliver bags to Pune, how many days will it take to deliver?'</li><li>'I need to change the delivery address for my recent order, how can I do that?'</li></ul>               |
| product faq             | <ul><li>'Does this product help with dark spots?'</li><li>'3. Is this product currently in stock?'</li><li>'Is the product in stock?'</li></ul>                                                                                                                                                |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8711   |

## 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("I like to listen to classical music")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 10.66  | 28  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| Out of Scope            | 50                    |
| general faq             | 50                    |
| order tracking          | 50                    |
| product discoverability | 50                    |
| product faq             | 50                    |
| product policy          | 50                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0002 | 1    | 0.2592        | -               |
| 0.0107 | 50   | 0.2424        | -               |
| 0.0213 | 100  | 0.1506        | -               |
| 0.0320 | 150  | 0.222         | -               |
| 0.0427 | 200  | 0.1227        | -               |
| 0.0533 | 250  | 0.1801        | -               |
| 0.0640 | 300  | 0.1111        | -               |
| 0.0747 | 350  | 0.0346        | -               |
| 0.0853 | 400  | 0.0313        | -               |
| 0.0960 | 450  | 0.0048        | -               |
| 0.1067 | 500  | 0.0023        | -               |
| 0.1173 | 550  | 0.0018        | -               |
| 0.1280 | 600  | 0.0133        | -               |
| 0.1387 | 650  | 0.0008        | -               |
| 0.1493 | 700  | 0.0006        | -               |
| 0.1600 | 750  | 0.0005        | -               |
| 0.1706 | 800  | 0.0008        | -               |
| 0.1813 | 850  | 0.0007        | -               |
| 0.1920 | 900  | 0.0006        | -               |
| 0.2026 | 950  | 0.0006        | -               |
| 0.2133 | 1000 | 0.0003        | -               |
| 0.2240 | 1050 | 0.0026        | -               |
| 0.2346 | 1100 | 0.0004        | -               |
| 0.2453 | 1150 | 0.0004        | -               |
| 0.2560 | 1200 | 0.0004        | -               |
| 0.2666 | 1250 | 0.0005        | -               |
| 0.2773 | 1300 | 0.0005        | -               |
| 0.2880 | 1350 | 0.0003        | -               |
| 0.2986 | 1400 | 0.0001        | -               |
| 0.3093 | 1450 | 0.0001        | -               |
| 0.3200 | 1500 | 0.0002        | -               |
| 0.3306 | 1550 | 0.0002        | -               |
| 0.3413 | 1600 | 0.0002        | -               |
| 0.3520 | 1650 | 0.0001        | -               |
| 0.3626 | 1700 | 0.0004        | -               |
| 0.3733 | 1750 | 0.0002        | -               |
| 0.3840 | 1800 | 0.0005        | -               |
| 0.3946 | 1850 | 0.0002        | -               |
| 0.4053 | 1900 | 0.0002        | -               |
| 0.4160 | 1950 | 0.0001        | -               |
| 0.4266 | 2000 | 0.0001        | -               |
| 0.4373 | 2050 | 0.0001        | -               |
| 0.4480 | 2100 | 0.0001        | -               |
| 0.4586 | 2150 | 0.0001        | -               |
| 0.4693 | 2200 | 0.0002        | -               |
| 0.4799 | 2250 | 0.0048        | -               |
| 0.4906 | 2300 | 0.0001        | -               |
| 0.5013 | 2350 | 0.001         | -               |
| 0.5119 | 2400 | 0.0002        | -               |
| 0.5226 | 2450 | 0.0002        | -               |
| 0.5333 | 2500 | 0.0001        | -               |
| 0.5439 | 2550 | 0.0001        | -               |
| 0.5546 | 2600 | 0.0001        | -               |
| 0.5653 | 2650 | 0.0001        | -               |
| 0.5759 | 2700 | 0.0001        | -               |
| 0.5866 | 2750 | 0.0001        | -               |
| 0.5973 | 2800 | 0.0001        | -               |
| 0.6079 | 2850 | 0.0001        | -               |
| 0.6186 | 2900 | 0.0001        | -               |
| 0.6293 | 2950 | 0.0001        | -               |
| 0.6399 | 3000 | 0.0001        | -               |
| 0.6506 | 3050 | 0.0001        | -               |
| 0.6613 | 3100 | 0.0001        | -               |
| 0.6719 | 3150 | 0.0001        | -               |
| 0.6826 | 3200 | 0.0001        | -               |
| 0.6933 | 3250 | 0.0001        | -               |
| 0.7039 | 3300 | 0.0001        | -               |
| 0.7146 | 3350 | 0.0001        | -               |
| 0.7253 | 3400 | 0.0001        | -               |
| 0.7359 | 3450 | 0.0001        | -               |
| 0.7466 | 3500 | 0.0001        | -               |
| 0.7573 | 3550 | 0.0001        | -               |
| 0.7679 | 3600 | 0.0001        | -               |
| 0.7786 | 3650 | 0.0001        | -               |
| 0.7892 | 3700 | 0.0001        | -               |
| 0.7999 | 3750 | 0.0001        | -               |
| 0.8106 | 3800 | 0.0001        | -               |
| 0.8212 | 3850 | 0.0           | -               |
| 0.8319 | 3900 | 0.0001        | -               |
| 0.8426 | 3950 | 0.0001        | -               |
| 0.8532 | 4000 | 0.0001        | -               |
| 0.8639 | 4050 | 0.0001        | -               |
| 0.8746 | 4100 | 0.0001        | -               |
| 0.8852 | 4150 | 0.0           | -               |
| 0.8959 | 4200 | 0.0001        | -               |
| 0.9066 | 4250 | 0.0001        | -               |
| 0.9172 | 4300 | 0.0001        | -               |
| 0.9279 | 4350 | 0.0001        | -               |
| 0.9386 | 4400 | 0.0001        | -               |
| 0.9492 | 4450 | 0.0001        | -               |
| 0.9599 | 4500 | 0.0001        | -               |
| 0.9706 | 4550 | 0.0001        | -               |
| 0.9812 | 4600 | 0.0           | -               |
| 0.9919 | 4650 | 0.0001        | -               |
| 1.0026 | 4700 | 0.0           | -               |
| 1.0132 | 4750 | 0.0001        | -               |
| 1.0239 | 4800 | 0.0001        | -               |
| 1.0346 | 4850 | 0.0001        | -               |
| 1.0452 | 4900 | 0.0001        | -               |
| 1.0559 | 4950 | 0.0001        | -               |
| 1.0666 | 5000 | 0.0           | -               |
| 1.0772 | 5050 | 0.0           | -               |
| 1.0879 | 5100 | 0.0001        | -               |
| 1.0985 | 5150 | 0.0           | -               |
| 1.1092 | 5200 | 0.0           | -               |
| 1.1199 | 5250 | 0.0           | -               |
| 1.1305 | 5300 | 0.0001        | -               |
| 1.1412 | 5350 | 0.0001        | -               |
| 1.1519 | 5400 | 0.0           | -               |
| 1.1625 | 5450 | 0.0001        | -               |
| 1.1732 | 5500 | 0.0001        | -               |
| 1.1839 | 5550 | 0.0002        | -               |
| 1.1945 | 5600 | 0.0           | -               |
| 1.2052 | 5650 | 0.0           | -               |
| 1.2159 | 5700 | 0.0           | -               |
| 1.2265 | 5750 | 0.0           | -               |
| 1.2372 | 5800 | 0.0001        | -               |
| 1.2479 | 5850 | 0.0001        | -               |
| 1.2585 | 5900 | 0.0001        | -               |
| 1.2692 | 5950 | 0.0           | -               |
| 1.2799 | 6000 | 0.0           | -               |
| 1.2905 | 6050 | 0.0           | -               |
| 1.3012 | 6100 | 0.0001        | -               |
| 1.3119 | 6150 | 0.0           | -               |
| 1.3225 | 6200 | 0.0           | -               |
| 1.3332 | 6250 | 0.0           | -               |
| 1.3439 | 6300 | 0.0           | -               |
| 1.3545 | 6350 | 0.0           | -               |
| 1.3652 | 6400 | 0.0           | -               |
| 1.3759 | 6450 | 0.0           | -               |
| 1.3865 | 6500 | 0.0           | -               |
| 1.3972 | 6550 | 0.0           | -               |
| 1.4078 | 6600 | 0.0           | -               |
| 1.4185 | 6650 | 0.0           | -               |
| 1.4292 | 6700 | 0.0           | -               |
| 1.4398 | 6750 | 0.0           | -               |
| 1.4505 | 6800 | 0.0           | -               |
| 1.4612 | 6850 | 0.0           | -               |
| 1.4718 | 6900 | 0.0001        | -               |
| 1.4825 | 6950 | 0.0001        | -               |
| 1.4932 | 7000 | 0.0           | -               |
| 1.5038 | 7050 | 0.0           | -               |
| 1.5145 | 7100 | 0.0001        | -               |
| 1.5252 | 7150 | 0.0001        | -               |
| 1.5358 | 7200 | 0.0001        | -               |
| 1.5465 | 7250 | 0.0001        | -               |
| 1.5572 | 7300 | 0.0           | -               |
| 1.5678 | 7350 | 0.0           | -               |
| 1.5785 | 7400 | 0.0           | -               |
| 1.5892 | 7450 | 0.0001        | -               |
| 1.5998 | 7500 | 0.0           | -               |
| 1.6105 | 7550 | 0.0           | -               |
| 1.6212 | 7600 | 0.0           | -               |
| 1.6318 | 7650 | 0.0           | -               |
| 1.6425 | 7700 | 0.0           | -               |
| 1.6532 | 7750 | 0.0           | -               |
| 1.6638 | 7800 | 0.0           | -               |
| 1.6745 | 7850 | 0.0           | -               |
| 1.6852 | 7900 | 0.0           | -               |
| 1.6958 | 7950 | 0.0           | -               |
| 1.7065 | 8000 | 0.0           | -               |
| 1.7172 | 8050 | 0.0           | -               |
| 1.7278 | 8100 | 0.0           | -               |
| 1.7385 | 8150 | 0.0001        | -               |
| 1.7491 | 8200 | 0.0           | -               |
| 1.7598 | 8250 | 0.0           | -               |
| 1.7705 | 8300 | 0.0           | -               |
| 1.7811 | 8350 | 0.0001        | -               |
| 1.7918 | 8400 | 0.0           | -               |
| 1.8025 | 8450 | 0.0           | -               |
| 1.8131 | 8500 | 0.0           | -               |
| 1.8238 | 8550 | 0.0           | -               |
| 1.8345 | 8600 | 0.0001        | -               |
| 1.8451 | 8650 | 0.0           | -               |
| 1.8558 | 8700 | 0.0           | -               |
| 1.8665 | 8750 | 0.0001        | -               |
| 1.8771 | 8800 | 0.0           | -               |
| 1.8878 | 8850 | 0.0           | -               |
| 1.8985 | 8900 | 0.0           | -               |
| 1.9091 | 8950 | 0.0001        | -               |
| 1.9198 | 9000 | 0.0           | -               |
| 1.9305 | 9050 | 0.0           | -               |
| 1.9411 | 9100 | 0.0           | -               |
| 1.9518 | 9150 | 0.0           | -               |
| 1.9625 | 9200 | 0.0           | -               |
| 1.9731 | 9250 | 0.0           | -               |
| 1.9838 | 9300 | 0.0           | -               |
| 1.9945 | 9350 | 0.0           | -               |

### Framework Versions
- Python: 3.10.16
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.2
- PyTorch: 2.2.2
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## 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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