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---
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 -->
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### 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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