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: What is the expected delivery time for the 10 pack of Cake Boxes to Bhopal?
- text: I need to know the status of my recent order. Can you check if it has been
    dispatched?
- text: My order was supposed to arrive yesterday but it hasn't. Can you check the
    delivery status for me?
- text: What options do you have for weight management products?
- text: My order has been shipped 4 days ago but still not out for delivery. Can you
    tell how long will it take to deliver?
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:** 5 classes
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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                                                                                                                                                                                                                                                                                      |
|:------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| product discoverability | <ul><li>'What are the options for dietary wellbeing products?'</li><li>'Do you have any products for weight loss?'</li><li>'What are the available options for male sexual wellness products?'</li></ul>                                                                                      |
| product faq             | <ul><li>'What are the benefits of using Prost Plus for male sexual wellness?'</li><li>'How does the Eladi skin exfoliator help in reducing acne and blemishes?'</li><li>'What are the ingredients in the Organic Breeaze Brew?'</li></ul>                                                     |
| order tracking          | <ul><li>'What is the expected delivery time for the Baking Ingredients I ordered?'</li><li>'Do you provide shipping insurance for high-value orders?'</li><li>'My order has been shipped 6 days ago but still not out for delivery. Can you tell how long will it take to deliver?'</li></ul> |
| general faq             | <ul><li>'What makes Purely Yours products different from other Ayurvedic brands?'</li><li>'How do you ensure the quality and authenticity of your Ayurvedic products?'</li><li>'Can you tell me more about the certifications your products hold?'</li></ul>                                  |
| product policy          | <ul><li>'What are the delivery charges for orders below INR 500?'</li><li>'How do you use the personal information collected on your website?'</li><li>'Are there any delivery charges for orders above INR 499?'</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("What options do you have for weight management products?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 6   | 11.55  | 24  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| Out of Scope            | 0                     |
| general faq             | 4                     |
| order tracking          | 24                    |
| product discoverability | 16                    |
| product faq             | 24                    |
| product policy          | 12                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0033 | 1    | 0.0651        | -               |
| 0.1656 | 50   | 0.0002        | -               |
| 0.3311 | 100  | 0.0003        | -               |
| 0.4967 | 150  | 0.0001        | -               |
| 0.6623 | 200  | 0.0004        | -               |
| 0.8278 | 250  | 0.0001        | -               |
| 0.9934 | 300  | 0.0002        | -               |
| 1.1589 | 350  | 0.0001        | -               |
| 1.3245 | 400  | 0.0002        | -               |
| 1.4901 | 450  | 0.0001        | -               |
| 1.6556 | 500  | 0.0001        | -               |
| 1.8212 | 550  | 0.0001        | -               |
| 1.9868 | 600  | 0.0001        | -               |

### 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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