---
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: What is the price of the organic honey?
- text: Variety of cookie boxes
- text: Is the Popcorn Box available in a pack of 50?
- text: What is the price range for the sugarfree chocolate heart sugarfree chocolate
    box pack of 5?
- text: Do you have the Off-White x Air Jordan 2 Retro Low SP Black Varsity Royal
    in size 10?
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: 0.8533333333333334
      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>'Does the Meenakari jal jangla -Rani saree have meenakari?'</li><li>'Is the Nike Dunk Low Premium Bacon available in size 7?'</li><li>'What is the best way to recycle the packaging boxes for wholesale orders for wholesale orders?'</li></ul>                                                                      |
| order tracking          | <ul><li>'I ordered the Cake Boards 7 days ago with order no 43210 how long will it take to deliver?'</li><li>'I want to deliver bags to Pune, how many days will it take to deliver?'</li><li>'I want to deliver packaging to Surat, how many days will it take to deliver?'</li></ul>                                        |
| product policy          | <ul><li>'What is the procedure for returning a product that was part of a special promotion occasion?'</li><li>'Can I return an item if it was damaged during delivery preparation?'</li><li>'What is the procedure for returning a product that was part of a special occasion promotion?'</li></ul>                         |
| general faq             | <ul><li>'What are the key factors to consider when developing a personalized diet plan for weight loss?'</li><li>'What are some tips for maximizing the antioxidant content when brewing green tea?'</li><li>'Can you explain why Mashru silk is considered more comfortable to wear compared to pure silk sarees?'</li></ul> |
| product discoverability | <ul><li>'Can you show me sarees in bright colors suitable for weddings?'</li><li>'Do you have adidas Superstar shoes?'</li><li>'Do you have any bestseller teas available?'</li></ul>                                                                                                                                         |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8533   |

## 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("Shankhdhar/classifier_woog_firstbud")
# Run inference
preds = model("Variety of cookie boxes")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 12.1961 | 28  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| general faq             | 24                    |
| order tracking          | 32                    |
| product discoverability | 50                    |
| product faq             | 50                    |
| product policy          | 48                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0005 | 1    | 0.2265        | -               |
| 0.0244 | 50   | 0.1831        | -               |
| 0.0489 | 100  | 0.1876        | -               |
| 0.0733 | 150  | 0.1221        | -               |
| 0.0978 | 200  | 0.0228        | -               |
| 0.1222 | 250  | 0.0072        | -               |
| 0.1467 | 300  | 0.0282        | -               |
| 0.1711 | 350  | 0.0015        | -               |
| 0.1956 | 400  | 0.0005        | -               |
| 0.2200 | 450  | 0.0008        | -               |
| 0.2445 | 500  | 0.0004        | -               |
| 0.2689 | 550  | 0.0003        | -               |
| 0.2934 | 600  | 0.0003        | -               |
| 0.3178 | 650  | 0.0002        | -               |
| 0.3423 | 700  | 0.0002        | -               |
| 0.3667 | 750  | 0.0002        | -               |
| 0.3912 | 800  | 0.0003        | -               |
| 0.4156 | 850  | 0.0002        | -               |
| 0.4401 | 900  | 0.0002        | -               |
| 0.4645 | 950  | 0.0001        | -               |
| 0.4890 | 1000 | 0.0001        | -               |
| 0.5134 | 1050 | 0.0001        | -               |
| 0.5379 | 1100 | 0.0001        | -               |
| 0.5623 | 1150 | 0.0002        | -               |
| 0.5868 | 1200 | 0.0002        | -               |
| 0.6112 | 1250 | 0.0001        | -               |
| 0.6357 | 1300 | 0.0001        | -               |
| 0.6601 | 1350 | 0.0001        | -               |
| 0.6846 | 1400 | 0.0001        | -               |
| 0.7090 | 1450 | 0.0001        | -               |
| 0.7335 | 1500 | 0.0001        | -               |
| 0.7579 | 1550 | 0.0001        | -               |
| 0.7824 | 1600 | 0.0001        | -               |
| 0.8068 | 1650 | 0.0001        | -               |
| 0.8313 | 1700 | 0.0001        | -               |
| 0.8557 | 1750 | 0.0011        | -               |
| 0.8802 | 1800 | 0.0002        | -               |
| 0.9046 | 1850 | 0.0001        | -               |
| 0.9291 | 1900 | 0.0001        | -               |
| 0.9535 | 1950 | 0.0002        | -               |
| 0.9780 | 2000 | 0.0001        | -               |
| 1.0024 | 2050 | 0.0001        | -               |
| 1.0269 | 2100 | 0.0002        | -               |
| 1.0513 | 2150 | 0.0001        | -               |
| 1.0758 | 2200 | 0.0001        | -               |
| 1.1002 | 2250 | 0.0001        | -               |
| 1.1247 | 2300 | 0.0001        | -               |
| 1.1491 | 2350 | 0.0001        | -               |
| 1.1736 | 2400 | 0.0001        | -               |
| 1.1980 | 2450 | 0.0001        | -               |
| 1.2225 | 2500 | 0.0001        | -               |
| 1.2469 | 2550 | 0.0001        | -               |
| 1.2714 | 2600 | 0.0001        | -               |
| 1.2958 | 2650 | 0.0001        | -               |
| 1.3203 | 2700 | 0.0001        | -               |
| 1.3447 | 2750 | 0.0001        | -               |
| 1.3692 | 2800 | 0.0001        | -               |
| 1.3936 | 2850 | 0.0001        | -               |
| 1.4181 | 2900 | 0.0001        | -               |
| 1.4425 | 2950 | 0.0001        | -               |
| 1.4670 | 3000 | 0.0001        | -               |
| 1.4914 | 3050 | 0.0001        | -               |
| 1.5159 | 3100 | 0.0001        | -               |
| 1.5403 | 3150 | 0.0001        | -               |
| 1.5648 | 3200 | 0.0001        | -               |
| 1.5892 | 3250 | 0.0001        | -               |
| 1.6137 | 3300 | 0.0001        | -               |
| 1.6381 | 3350 | 0.0001        | -               |
| 1.6626 | 3400 | 0.0001        | -               |
| 1.6870 | 3450 | 0.0001        | -               |
| 1.7115 | 3500 | 0.0001        | -               |
| 1.7359 | 3550 | 0.0           | -               |
| 1.7604 | 3600 | 0.0001        | -               |
| 1.7848 | 3650 | 0.0001        | -               |
| 1.8093 | 3700 | 0.0001        | -               |
| 1.8337 | 3750 | 0.0           | -               |
| 1.8582 | 3800 | 0.0001        | -               |
| 1.8826 | 3850 | 0.0001        | -               |
| 1.9071 | 3900 | 0.0001        | -               |
| 1.9315 | 3950 | 0.0           | -               |
| 1.9560 | 4000 | 0.0           | -               |
| 1.9804 | 4050 | 0.0001        | -               |

### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.2.2+cu121
- Datasets: 2.19.2
- Tokenizers: 0.15.2

## 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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