---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Dadon Hotel
- text: Joyi Homeo Hall
- text: Masum Egg Supplier
- text: Salam Automobiles
- text: Shoumik Enterprise
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
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: 0.33
      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:** 28 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                                                                                                                                                 |
|:-----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|
| Relegious        | <ul><li>'Badc Jame Masjid'</li><li>'Modina Masjid'</li><li>'Baitul Ehsan Jame Masjid'</li></ul>                                                          |
| Food             | <ul><li>'Bombay Biriyani Restaurant'</li><li>'Sanim Ghorowa Reatora'</li><li>'Attel Mati Restaurant'</li></ul>                                           |
| Religious PLAce  | <ul><li>'Darbar Sharif(Dorbeshe Badsha)'</li><li>'Mazar'</li></ul>                                                                                       |
| Education        | <ul><li>'The English Academy'</li><li>'Economics Batch'</li><li>'Al Manar Model School'</li></ul>                                                        |
| Health Care      | <ul><li>'Hope Haspital'</li><li>'North Para Community Clinic'</li><li>'Al Sami Medical Hall'</li></ul>                                                   |
| Office           | <ul><li>'Nari Maitri Dholpur Branch'</li><li>'Techsam IT And Computer'</li><li>'Chandpur It'</li></ul>                                                   |
| Landmark         | <ul><li>'Godaun Moar'</li><li>'Kuril Flyover U Turn Bridge'</li><li>'Manik Miya Avenue Moar'</li></ul>                                                   |
| Fuel             | <ul><li>'Mimi Enterprise'</li><li>'Sariful Filling Station'</li><li>'M/s Aruja Enterprise'</li></ul>                                                     |
| Religious Place  | <ul><li>'Kabbir Khan Jame Masjid'</li><li>'Sri Sri Nayanta Babar Mandir'</li><li>'Jordan Church of Christ'</li></ul>                                     |
| Transportation   | <ul><li>'Lala Khal Ferry Terminal'</li><li>'Porshuram Cng Stand'</li><li>'Riad Cycle Garage'</li></ul>                                                   |
| Agricultural     | <ul><li>'Catlle Farm'</li><li>'Pushon Narsari'</li><li>'Vegetable garden'</li></ul>                                                                      |
| Residential      | <ul><li>'Ovinondon Chattrabas'</li><li>'TH Chattrabas'</li><li>'Seven Star Chattrabas'</li></ul>                                                         |
| shop             | <ul><li>'Mayer Doya Store'</li></ul>                                                                                                                     |
| Bank             | <ul><li>'Dutch Bangla Bank Limited Maijde (DBBL)'</li><li>'Jamuna Bank Limited Dholaikhal Branch'</li><li>'Prime Bank Limited Elephant Branch'</li></ul> |
| Utility          | <ul><li>'Shahi Eidgah Water Tank'</li><li>'Pole No 31'</li><li>'Kalmilata Kacha Bazar'</li></ul>                                                         |
| Healthcare       | <ul><li>'Oloukik'</li><li>'Burhanuddin Upazila Health Complex'</li><li>'Dr Nazmin Akter Najma'</li></ul>                                                 |
| Government       | <ul><li>'Zilla Parishad Karjaloy Bhola'</li><li>"Sub Police Commissioner's Bhaban (Tejgaon Branch)"</li><li>'Family Planning Office Satkhira'</li></ul>  |
| Recreation       | <ul><li>'Shaikh Rasel Sriti Shongho'</li><li>'Beraid Camping And Kayaking Zone (BCKZ)'</li><li>'Shohag Palli Picnic Spot & Resort'</li></ul>             |
| Religious        | <ul><li>'Baitul Mamur Jame Masjid'</li><li>'Petrol Pump Jame Masjid'</li><li>'Opsonnin Pharma Ltd Jame Masjid'</li></ul>                                 |
| Religious  Place | <ul><li>'Jame  Masjid'</li><li>'Hospital Masjid'</li><li>'Badar Mokam Jame Masjid'</li></ul>                                                             |
| Shop             | <ul><li>'Nayeem General Store'</li><li>'Bazlu Engineering & Refrigeration'</li><li>'Mukta Dulal'</li></ul>                                               |
| Commercial       | <ul><li>'Mazar Kacha Bazar'</li><li>'Fall Bazar Kola Potti'</li><li>'Venus Autos'</li></ul>                                                              |
| Industry         | <ul><li>'Rn Integrated Argo'</li><li>'Fresh Dairy Firm'</li><li>'Hemple Rhee Mfg Limited'</li></ul>                                                      |
| Hotel            | <ul><li>'Warisan'</li><li>'Hotel New London Palace Abashik'</li><li>'Sada Vat'</li></ul>                                                                 |
| construction     | <ul><li>'Fahim Hardware Store'</li><li>'O A Frame Gallery'</li></ul>                                                                                     |
| Construction     | <ul><li>'Khalil Steel'</li><li>'Sanaullah Tiles And Sanitary House'</li><li>'Mukta Glass And Thai Aluminum'</li></ul>                                    |
| Relegious Place  | <ul><li>'Baitul Atiq Jam-E Masjid'</li><li>'Hathazari Bus Stand Baitussalam Jame Masjid'</li><li>'Osman Bin Affan  Jame Masjid'</li></ul>                |
| education        | <ul><li>'Masum Electronic'</li></ul>                                                                                                                     |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.33     |

## 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("rafi138/setfit-paraphrase-mpnet-base-v2-type")
# Run inference
preds = model("Dadon Hotel")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 3.5    | 7   |

| Label                                                                                                                                                               | Training Sample Count |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------|
| ShopCommercialGovernmentHealthcareEducationFoodOfficeReligious PlaceBankTransportationConstructionIndustryResidentialLandmarkRecreationFuelHotelUtilityAgricultural | 0                     |

### Training Hyperparameters
- batch_size: (32, 32)
- 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0006  | 1       | 0.1851        | -               |
| 0.0282  | 50      | 0.1697        | -               |
| 0.0564  | 100     | 0.1876        | -               |
| 0.0032  | 1       | 0.169         | -               |
| 0.1597  | 50      | 0.081         | -               |
| 0.3195  | 100     | 0.0641        | -               |
| 0.4792  | 150     | 0.033         | -               |
| 0.6390  | 200     | 0.0128        | -               |
| 0.7987  | 250     | 0.0089        | -               |
| 0.9585  | 300     | 0.0106        | -               |
| **1.0** | **313** | **-**         | **0.3235**      |
| 1.1182  | 350     | 0.0215        | -               |
| 1.2780  | 400     | 0.017         | -               |
| 1.4377  | 450     | 0.0057        | -               |
| 1.5974  | 500     | 0.0047        | -               |
| 1.7572  | 550     | 0.0064        | -               |
| 1.9169  | 600     | 0.003         | -               |
| 2.0     | 626     | -             | 0.3481          |
| 2.0767  | 650     | 0.0043        | -               |
| 2.2364  | 700     | 0.0022        | -               |
| 2.3962  | 750     | 0.0014        | -               |
| 2.5559  | 800     | 0.0028        | -               |
| 2.7157  | 850     | 0.0018        | -               |
| 2.8754  | 900     | 0.002         | -               |
| 3.0     | 939     | -             | 0.3393          |
| 3.0351  | 950     | 0.0294        | -               |
| 3.1949  | 1000    | 0.002         | -               |
| 3.3546  | 1050    | 0.0017        | -               |
| 3.5144  | 1100    | 0.0017        | -               |
| 3.6741  | 1150    | 0.0015        | -               |
| 3.8339  | 1200    | 0.0013        | -               |
| 3.9936  | 1250    | 0.0014        | -               |
| 4.0     | 1252    | -             | 0.348           |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0

## 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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