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 is the process for exchanging sneakers?
- text: Do you offer a satisfaction guarantee for sneakers purchased with a store
    promotional code?
- text: cookie boxes with dividers
- text: What is the optimal brewing time for green tea to ensure the highest health
    benefits?
- text: What information might be shared with third parties, and in what situations
    would this occur?
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: 0.84
      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>'What is the delivery status for my order placed using phone number 123456789?'</li><li>'I ordered the Cake Decorating Kit 4 days ago, can you provide the tracking information?'</li><li>'I ordered the Cake Stands 2 days ago with order no 54321 how long will it take to deliver?'</li></ul>                                                                          |
| general faq             | <ul><li>'How do the traditional hand-woven Banarasi sarees from HKV Benaras differ from those made by machine-driven industries?'</li><li>'What are the key factors to consider when developing a personalized diet plan for weight loss?'</li><li>"Are there any scientific studies that support Green Tea's role in preventing Alzheimer's and Parkinson's diseases?"</li></ul> |
| product policy          | <ul><li>'How do you use the information collected through tracking tools like Google Analytics and cookies?'</li><li>'How does bakeyy handle returns for items that were purchased with a thank you discount?'</li><li>'What is the procedure for returning a product that was part of a special occasion promotion?'</li></ul>                                                   |
| product discoverability | <ul><li>'What is the price of the organic honey?'</li><li>'Variety of cookie boxes'</li><li>'what apparells do you have from Drew House'</li></ul>                                                                                                                                                                                                                                |
| product faq             | <ul><li>'What is the price of the bestseller honey?'</li><li>'Do you offer any bulk discounts on organic honey?'</li><li>'Are the big plum cake boxes available in packs of 30?'</li></ul>                                                                                                                                                                                        |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.84     |

## 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_updated")
# Run inference
preds = model("cookie boxes with dividers")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 11.9760 | 28  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| general faq             | 24                    |
| order tracking          | 34                    |
| product discoverability | 50                    |
| product faq             | 50                    |
| product policy          | 50                    |

### 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    | 0.2048        | -               |
| 0.0235 | 50   | 0.2874        | -               |
| 0.0470 | 100  | 0.126         | -               |
| 0.0705 | 150  | 0.0388        | -               |
| 0.0940 | 200  | 0.0786        | -               |
| 0.1175 | 250  | 0.0049        | -               |
| 0.1410 | 300  | 0.0048        | -               |
| 0.1646 | 350  | 0.0018        | -               |
| 0.1881 | 400  | 0.0011        | -               |
| 0.2116 | 450  | 0.0004        | -               |
| 0.2351 | 500  | 0.0006        | -               |
| 0.2586 | 550  | 0.0005        | -               |
| 0.2821 | 600  | 0.0012        | -               |
| 0.3056 | 650  | 0.0004        | -               |
| 0.3291 | 700  | 0.0003        | -               |
| 0.3526 | 750  | 0.0002        | -               |
| 0.3761 | 800  | 0.0002        | -               |
| 0.3996 | 850  | 0.0002        | -               |
| 0.4231 | 900  | 0.0002        | -               |
| 0.4466 | 950  | 0.0008        | -               |
| 0.4701 | 1000 | 0.0002        | -               |
| 0.4937 | 1050 | 0.0003        | -               |
| 0.5172 | 1100 | 0.0001        | -               |
| 0.5407 | 1150 | 0.0002        | -               |
| 0.5642 | 1200 | 0.0001        | -               |
| 0.5877 | 1250 | 0.0001        | -               |
| 0.6112 | 1300 | 0.0001        | -               |
| 0.6347 | 1350 | 0.0004        | -               |
| 0.6582 | 1400 | 0.0002        | -               |
| 0.6817 | 1450 | 0.0001        | -               |
| 0.7052 | 1500 | 0.0002        | -               |
| 0.7287 | 1550 | 0.0001        | -               |
| 0.7522 | 1600 | 0.0001        | -               |
| 0.7757 | 1650 | 0.0001        | -               |
| 0.7992 | 1700 | 0.0001        | -               |
| 0.8228 | 1750 | 0.0001        | -               |
| 0.8463 | 1800 | 0.0001        | -               |
| 0.8698 | 1850 | 0.0001        | -               |
| 0.8933 | 1900 | 0.0001        | -               |
| 0.9168 | 1950 | 0.0001        | -               |
| 0.9403 | 2000 | 0.0001        | -               |
| 0.9638 | 2050 | 0.0001        | -               |
| 0.9873 | 2100 | 0.0002        | -               |
| 1.0108 | 2150 | 0.0001        | -               |
| 1.0343 | 2200 | 0.0001        | -               |
| 1.0578 | 2250 | 0.0001        | -               |
| 1.0813 | 2300 | 0.0001        | -               |
| 1.1048 | 2350 | 0.0001        | -               |
| 1.1283 | 2400 | 0.0           | -               |
| 1.1519 | 2450 | 0.0001        | -               |
| 1.1754 | 2500 | 0.0           | -               |
| 1.1989 | 2550 | 0.0001        | -               |
| 1.2224 | 2600 | 0.0007        | -               |
| 1.2459 | 2650 | 0.0001        | -               |
| 1.2694 | 2700 | 0.0001        | -               |
| 1.2929 | 2750 | 0.0001        | -               |
| 1.3164 | 2800 | 0.0001        | -               |
| 1.3399 | 2850 | 0.0001        | -               |
| 1.3634 | 2900 | 0.0001        | -               |
| 1.3869 | 2950 | 0.0001        | -               |
| 1.4104 | 3000 | 0.0001        | -               |
| 1.4339 | 3050 | 0.0001        | -               |
| 1.4575 | 3100 | 0.0001        | -               |
| 1.4810 | 3150 | 0.0001        | -               |
| 1.5045 | 3200 | 0.0001        | -               |
| 1.5280 | 3250 | 0.0001        | -               |
| 1.5515 | 3300 | 0.0001        | -               |
| 1.5750 | 3350 | 0.0001        | -               |
| 1.5985 | 3400 | 0.0001        | -               |
| 1.6220 | 3450 | 0.0001        | -               |
| 1.6455 | 3500 | 0.0001        | -               |
| 1.6690 | 3550 | 0.0001        | -               |
| 1.6925 | 3600 | 0.0001        | -               |
| 1.7160 | 3650 | 0.0           | -               |
| 1.7395 | 3700 | 0.0001        | -               |
| 1.7630 | 3750 | 0.0001        | -               |
| 1.7866 | 3800 | 0.0           | -               |
| 1.8101 | 3850 | 0.0001        | -               |
| 1.8336 | 3900 | 0.0001        | -               |
| 1.8571 | 3950 | 0.0           | -               |
| 1.8806 | 4000 | 0.0001        | -               |
| 1.9041 | 4050 | 0.0001        | -               |
| 1.9276 | 4100 | 0.0001        | -               |
| 1.9511 | 4150 | 0.0001        | -               |
| 1.9746 | 4200 | 0.0001        | -               |
| 1.9981 | 4250 | 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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