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