---
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: Is the monster free gift currently in stock?
- text: What makes K-beauty skincare routines unique?
- text: 6. What category does this product belong to?
- text: What products do you have for the body?
- text: How is the Kare Taker Neck Cream different from the Kare Taker Neck Cream
    Copy?
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: 1.0
      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                                                                                                                                                                                                                                                                                          |
|:------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| product faq             | <ul><li>'9. Is this product effective for pimples and acne?'</li><li>'What are the key features of the Hydration Combo Kit?'</li><li>'What are the main ingredients in the Daily Spread Moisturiser + Mineral Suncreen SPF 30?'</li></ul>                                                         |
| order tracking          | <ul><li>'What is the delivery status for my order placed using email address test@example.com?'</li><li>'I ordered the Cupcake Cases 3 days ago with order no 34567 how long will it take to deliver?'</li><li>'I need to return an item, what is the return policy for online orders?'</li></ul> |
| general faq             | <ul><li>'What ingredients in K-beauty products help with sensitive skin?'</li><li>'What inspired your founder to start Botnal?'</li><li>'How can I adapt K-beauty routines for dry weather?'</li></ul>                                                                                            |
| product policy          | <ul><li>'What makes BOTNAL skincare products clean and effective?'</li><li>'What is your return policy duration?'</li><li>'What are the accepted payment methods for purchasing BOTNAL products?'</li></ul>                                                                                       |
| product discoverability | <ul><li>'Do you have any products specifically for targeting aging?'</li><li>'Which products are best for acne?'</li><li>'What products do you have for wrinkles?'</li></ul>                                                                                                                      |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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 products do you have for the body?")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 10.7126 | 24  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| general faq             | 16                    |
| order tracking          | 24                    |
| product discoverability | 16                    |
| product faq             | 23                    |
| product policy          | 8                     |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0027 | 1    | 0.0657        | -               |
| 0.1359 | 50   | 0.0053        | -               |
| 0.2717 | 100  | 0.0002        | -               |
| 0.4076 | 150  | 0.0001        | -               |
| 0.5435 | 200  | 0.0003        | -               |
| 0.6793 | 250  | 0.0002        | -               |
| 0.8152 | 300  | 0.0001        | -               |
| 0.9511 | 350  | 0.0001        | -               |
| 1.0870 | 400  | 0.0004        | -               |
| 1.2228 | 450  | 0.0001        | -               |
| 1.3587 | 500  | 0.0001        | -               |
| 1.4946 | 550  | 0.0001        | -               |
| 1.6304 | 600  | 0.0001        | -               |
| 1.7663 | 650  | 0.0001        | -               |
| 1.9022 | 700  | 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->