---
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: I'm looking for a bracelet as a birthday gift. What do you recommend?
- text: I recently ordered a Leafy Bling Silver Ring but haven't received any update
    on the delivery status. Can you help me track my order?
- text: What is the Bold and Beautiful Link Ring made of, and could you provide information
    on sizing and care instructions?
- text: What are the latest trends in bracelets that you have in stock?
- text: Can you suggest some minimalist necklaces from your 'Best Sellers - Minimalist'
    range?
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: 0.8024691358024691
      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 policy          | <ul><li>'If I receive a defective Choker, what is the process to get a replacement?'</li><li>'Are there any restocking fees for returning a Choker?'</li><li>'What warranty do you offer on Choker products?'</li></ul>                                                                                                                                                                                                                                                                |
| product faq             | <ul><li>'What sizes is the Sheer Heart Ring available in, and can you provide the price for each size?'</li><li>'Is the Silver Eye Pendant nickel-free and hypoallergenic?'</li><li>'What material is used for the Crystal Drop Earring, and how should I take care of it to prevent tarnishing?'</li></ul>                                                                                                                                                                            |
| order tracking          | <ul><li>"I haven't received an update on my order status for the Rosé Bloom Ring. Could you please provide me with the tracking details?"</li><li>"I recently ordered the Pakhi Handcrafted Earring but I haven't received any shipping confirmation. Could you please update me on the status of my order?"</li><li>"I recently ordered a Whispering Star Silver Ring, but I haven't received any shipment updates. Can you please provide me with the status of my order?"</li></ul> |
| product discoveribility | <ul><li>'What are the latest trends in bracelets that you have in stock?'</li><li>"I'm interested in pendant sets from your 'Gold Plated Jewellery' collection. What options do you offer?"</li><li>"I'm interested in silver bracelets. What options are available in that material?"</li></ul>                                                                                                                                                                                       |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8025   |

## 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 are the latest trends in bracelets that you have in stock?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 8   | 16.8438 | 31  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| order tracking          | 8                     |
| product discoveribility | 8                     |
| product faq             | 8                     |
| product policy          | 8                     |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0208 | 1    | 0.1273        | -               |
| 1.0417 | 50   | 0.004         | -               |
| 2.0833 | 100  | 0.0005        | -               |
| 3.125  | 150  | 0.0005        | -               |

### Framework Versions
- Python: 3.9.16
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.1
- PyTorch: 2.3.0
- Datasets: 2.19.0
- 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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