---
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 ordered the Cake Decorating Kit 4 days ago, can you provide the tracking
    information?
- text: Do you have any smart accessories in black color?
- text: What products are included in the Smart-Combo Wallet bot Classic + Smart FingerLock
    Backpack?
- text: What smart luggage options do you have for men?
- text: I want to deliver candle supplies to Jaipur, how many days will it take to
    deliver?
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: 0.9565217391304348
      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:** 5 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                                                                                                                                                                                                                                                                                                |
|:------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| order tracking          | <ul><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><li>'I want to deliver packaging to Surat, how many days will it take to deliver?'</li><li>'Do you provide shipping insurance for high-value orders?'</li></ul>       |
| product faq             | <ul><li>'Is the Smart Keychain in Navy Blue available for women?'</li><li>'Is the Smart Fingerlock Backpack Leather in Black targeted towards men, women, or unisex?'</li><li>'1. Can you tell me the price of the smart luggage?'</li></ul>                                                            |
| general faq             | <ul><li>"Can you explain how the 'Follow Me' feature works in Arista Vault's smart luggage?"</li><li>"What makes Arista Vault's smart luggage a good investment for business travelers?"</li><li>"How does the fingerprint lock technology in Arista Vault's smart luggage enhance security?"</li></ul> |
| product policy          | <ul><li>'Can I register multiple accounts for the Arista Vault Affiliate Program?'</li><li>'What is your return and exchange policy?'</li><li>'How do I receive my referral fees and commissions?'</li></ul>                                                                                            |
| product discoverability | <ul><li>'Do you have any smart accessories in blue color?'</li><li>'What is the price of the smart luggage for women?'</li><li>'Which smart backpacks are available in nylon material?'</li></ul>                                                                                                       |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9565   |

## 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 smart luggage options do you have for men?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 11.6304 | 24  |

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

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0025 | 1    | 0.2167        | -               |
| 0.1238 | 50   | 0.0573        | -               |
| 0.2475 | 100  | 0.0002        | -               |
| 0.3713 | 150  | 0.0009        | -               |
| 0.4950 | 200  | 0.0001        | -               |
| 0.6188 | 250  | 0.0           | -               |
| 0.7426 | 300  | 0.0           | -               |
| 0.8663 | 350  | 0.0           | -               |
| 0.9901 | 400  | 0.0           | -               |
| 1.1139 | 450  | 0.0           | -               |
| 1.2376 | 500  | 0.0           | -               |
| 1.3614 | 550  | 0.0           | -               |
| 1.4851 | 600  | 0.0001        | -               |
| 1.6089 | 650  | 0.0           | -               |
| 1.7327 | 700  | 0.0           | -               |
| 1.8564 | 750  | 0.0           | -               |
| 1.9802 | 800  | 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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