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 model-index 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 are the different types of zari used in the sarees?
- text: I need to change the delivery address for my recent order, how can I do that?
- text: I need to return an item, what is the return policy for online orders?
- text: Are there any sarees with Fekwa Weave technique?
- text: What are the different colors in the Air Jordan 1 Retro High OG Volt Gold?
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: 0.8666666666666667
      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:** 6 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                                                                                                                                                                                                                                                                                                             |
|:------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Out of Scope            | <ul><li>'Why is your website so slow?'</li><li>'Can I get a shoutout on your social media?'</li><li>'I like to listen to classical music'</li></ul>                                                                                                                                                                  |
| product faq             | <ul><li>'What is the price of the Temple Butidaar Multi Color Border Pure Silk Chiffon Georgette Saree?'</li><li>'Do you have the Air Jordan 1 Low Shadow Brown/Brown Kelp- Sail in size 7?'</li><li>'Is the lakadong turmeric powder available for purchase?'</li></ul>                                             |
| order tracking          | <ul><li>'What is the expected delivery time for the 10 pack of Cake Boxes to Bhopal?'</li><li>'What is the delivery status for my order placed using email address test@example.com?'</li><li>'I havent received my order'</li></ul>                                                                                 |
| product policy          | <ul><li>'What is the policy for returning a product that was part of a Cyber Monday sale?'</li><li>'Are there any exceptions to the return policy for items that were purchased with a special occasion promotion?'</li><li>'Are there any restrictions on returning sneakers with added fur or fur trim?'</li></ul> |
| product discoverability | <ul><li>'Suggest me some high ankle sneakers'</li><li>'Do you have any grocery & gourmet honey available?'</li><li>'Do you have any sneaker collaborations with artists?'</li></ul>                                                                                                                                  |
| general faq             | <ul><li>'How many cups of green tea should I drink daily to achieve the recommended therapeutic dosage of ECGC?'</li><li>'what is mashru silk'</li><li>'What specific compounds in Green Tea contribute to its antioxidant properties?'</li></ul>                                                                    |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8667   |

## 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("Are there any sarees with Fekwa Weave technique?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 11.1737 | 28  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| Out of Scope            | 35                    |
| general faq             | 24                    |
| order tracking          | 34                    |
| product discoverability | 40                    |
| product faq             | 40                    |
| product policy          | 40                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1    | 0.256         | -               |
| 0.0213 | 50   | 0.2639        | -               |
| 0.0425 | 100  | 0.2341        | -               |
| 0.0638 | 150  | 0.0407        | -               |
| 0.0851 | 200  | 0.0698        | -               |
| 0.1063 | 250  | 0.014         | -               |
| 0.1276 | 300  | 0.0069        | -               |
| 0.1489 | 350  | 0.0099        | -               |
| 0.1701 | 400  | 0.0014        | -               |
| 0.1914 | 450  | 0.0007        | -               |
| 0.2127 | 500  | 0.0006        | -               |
| 0.2339 | 550  | 0.0005        | -               |
| 0.2552 | 600  | 0.0006        | -               |
| 0.2765 | 650  | 0.0005        | -               |
| 0.2977 | 700  | 0.0002        | -               |
| 0.3190 | 750  | 0.0005        | -               |
| 0.3403 | 800  | 0.0003        | -               |
| 0.3615 | 850  | 0.0003        | -               |
| 0.3828 | 900  | 0.0002        | -               |
| 0.4041 | 950  | 0.0003        | -               |
| 0.4254 | 1000 | 0.0002        | -               |
| 0.4466 | 1050 | 0.0002        | -               |
| 0.4679 | 1100 | 0.0001        | -               |
| 0.4892 | 1150 | 0.0002        | -               |
| 0.5104 | 1200 | 0.0002        | -               |
| 0.5317 | 1250 | 0.0001        | -               |
| 0.5530 | 1300 | 0.0002        | -               |
| 0.5742 | 1350 | 0.0002        | -               |
| 0.5955 | 1400 | 0.0001        | -               |
| 0.6168 | 1450 | 0.0002        | -               |
| 0.6380 | 1500 | 0.0002        | -               |
| 0.6593 | 1550 | 0.0001        | -               |
| 0.6806 | 1600 | 0.0001        | -               |
| 0.7018 | 1650 | 0.0001        | -               |
| 0.7231 | 1700 | 0.0001        | -               |
| 0.7444 | 1750 | 0.0001        | -               |
| 0.7656 | 1800 | 0.0001        | -               |
| 0.7869 | 1850 | 0.0001        | -               |
| 0.8082 | 1900 | 0.0001        | -               |
| 0.8294 | 1950 | 0.0001        | -               |
| 0.8507 | 2000 | 0.0001        | -               |
| 0.8720 | 2050 | 0.0001        | -               |
| 0.8932 | 2100 | 0.0001        | -               |
| 0.9145 | 2150 | 0.0002        | -               |
| 0.9358 | 2200 | 0.0002        | -               |
| 0.9570 | 2250 | 0.0002        | -               |
| 0.9783 | 2300 | 0.0001        | -               |
| 0.9996 | 2350 | 0.0001        | -               |
| 1.0208 | 2400 | 0.0001        | -               |
| 1.0421 | 2450 | 0.0002        | -               |
| 1.0634 | 2500 | 0.0001        | -               |
| 1.0846 | 2550 | 0.0001        | -               |
| 1.1059 | 2600 | 0.0001        | -               |
| 1.1272 | 2650 | 0.0002        | -               |
| 1.1484 | 2700 | 0.0001        | -               |
| 1.1697 | 2750 | 0.0001        | -               |
| 1.1910 | 2800 | 0.0001        | -               |
| 1.2123 | 2850 | 0.0001        | -               |
| 1.2335 | 2900 | 0.0001        | -               |
| 1.2548 | 2950 | 0.0001        | -               |
| 1.2761 | 3000 | 0.0001        | -               |
| 1.2973 | 3050 | 0.0001        | -               |
| 1.3186 | 3100 | 0.0001        | -               |
| 1.3399 | 3150 | 0.0001        | -               |
| 1.3611 | 3200 | 0.0001        | -               |
| 1.3824 | 3250 | 0.0001        | -               |
| 1.4037 | 3300 | 0.0001        | -               |
| 1.4249 | 3350 | 0.0001        | -               |
| 1.4462 | 3400 | 0.0001        | -               |
| 1.4675 | 3450 | 0.0001        | -               |
| 1.4887 | 3500 | 0.0001        | -               |
| 1.5100 | 3550 | 0.0001        | -               |
| 1.5313 | 3600 | 0.0001        | -               |
| 1.5525 | 3650 | 0.0001        | -               |
| 1.5738 | 3700 | 0.0001        | -               |
| 1.5951 | 3750 | 0.0001        | -               |
| 1.6163 | 3800 | 0.0001        | -               |
| 1.6376 | 3850 | 0.0           | -               |
| 1.6589 | 3900 | 0.0001        | -               |
| 1.6801 | 3950 | 0.0001        | -               |
| 1.7014 | 4000 | 0.0001        | -               |
| 1.7227 | 4050 | 0.0001        | -               |
| 1.7439 | 4100 | 0.0001        | -               |
| 1.7652 | 4150 | 0.0001        | -               |
| 1.7865 | 4200 | 0.0001        | -               |
| 1.8077 | 4250 | 0.0001        | -               |
| 1.8290 | 4300 | 0.0001        | -               |
| 1.8503 | 4350 | 0.0001        | -               |
| 1.8715 | 4400 | 0.0           | -               |
| 1.8928 | 4450 | 0.0001        | -               |
| 1.9141 | 4500 | 0.0001        | -               |
| 1.9353 | 4550 | 0.0001        | -               |
| 1.9566 | 4600 | 0.0001        | -               |
| 1.9779 | 4650 | 0.0001        | -               |
| 1.9991 | 4700 | 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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