SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
Model Sources
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
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
model = SetFitModel.from_pretrained("rafi138/setfit-paraphrase-mpnet-base-v2-type")
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
@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}
}
