---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
- accuracy
widget:
- text: cookie boxes for gifting under $20
- text: Are there any restrictions on returning candle supplies?
- text: special features for bakery boxes
- text: I need to confirm the shipping date for my recent purchase. Can you help me
    with that?
- text: different types of bakery boxes available
pipeline_tag: text-classification
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: 0.8380952380952381
      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:** 4 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 discoverability | <ul><li>'Do you have Adidas Superstar shoes?'</li><li>'Do you have any running shoes in pink color?'</li><li>'Do you have black Yeezy sneakers in size 9?'</li></ul>                                                                                                                                         |
| order tracking          | <ul><li>"I'm concerned about the delay in the delivery of my order. Can you please provide me with the status?"</li><li>'What is the estimated delivery time for orders within the same city?'</li><li>"I placed an order last week and it still hasn't arrived. Can you check the status for me?"</li></ul> |
| product policy          | <ul><li>'Are there any exceptions to the return policy for items that were purchased with a student discount?'</li><li>'Do you offer a try-and-buy option for sneakers?'</li><li>'Do you offer a price adjustment for sneakers if the price drops after purchase?'</li></ul>                                 |
| product faq             | <ul><li>'Do you have any limited edition sneakers available?'</li><li>'Are the Adidas Yeezy Foam Runner available in size 7?'</li><li>"Are the Nike Air Force 1 sneakers available in women's sizes?"</li></ul>                                                                                              |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8381   |

## 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("special features for bakery boxes")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 11.6415 | 24  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| order tracking          | 30                    |
| product discoverability | 30                    |
| product faq             | 16                    |
| product policy          | 30                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0019 | 1    | 0.1782        | -               |
| 0.0965 | 50   | 0.0628        | -               |
| 0.1931 | 100  | 0.0036        | -               |
| 0.2896 | 150  | 0.0013        | -               |
| 0.3861 | 200  | 0.0012        | -               |
| 0.4826 | 250  | 0.0003        | -               |
| 0.5792 | 300  | 0.0002        | -               |
| 0.6757 | 350  | 0.0003        | -               |
| 0.7722 | 400  | 0.0002        | -               |
| 0.8687 | 450  | 0.0005        | -               |
| 0.9653 | 500  | 0.0003        | -               |
| 1.0618 | 550  | 0.0001        | -               |
| 1.1583 | 600  | 0.0002        | -               |
| 1.2548 | 650  | 0.0002        | -               |
| 1.3514 | 700  | 0.0002        | -               |
| 1.4479 | 750  | 0.0001        | -               |
| 1.5444 | 800  | 0.0001        | -               |
| 1.6409 | 850  | 0.0001        | -               |
| 1.7375 | 900  | 0.0002        | -               |
| 1.8340 | 950  | 0.0001        | -               |
| 1.9305 | 1000 | 0.0001        | -               |

### Framework Versions
- Python: 3.9.16
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.2
- PyTorch: 2.3.0
- 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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