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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: The development of smart cities is leveraging technology to improve urban
living conditions.
- text: Climate change is causing a significant rise in sea levels.
- text: Fans are speculating about the plot of the upcoming season of Stranger Things.
- text: Fashion branding and marketing campaigns shape consumer perceptions and influence
purchasing decisions.
- text: Volunteering abroad provides a unique opportunity to experience different
cultures while giving back to society.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---
# 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:** 12 classes
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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 |
|:--------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Politics | <ul><li>'The mayor announced a new initiative to improve public transportation.'</li><li>'The senator is facing criticism for her stance on the recent bill.'</li><li>'The upcoming election has sparked intense debates among the candidates.'</li></ul> |
| Health | <ul><li>'Regular exercise and a balanced diet are key to maintaining good health.'</li><li>'The World Health Organization has issued new guidelines on COVID-19.'</li><li>'A new study reveals the benefits of meditation for mental health.'</li></ul> |
| Finance | <ul><li>'The stock market saw a significant drop following the announcement.'</li><li>'Investing in real estate can be a profitable venture if done correctly.'</li><li>"The company's profits have doubled since the launch of their new product."</li></ul> |
| Travel | <ul><li>'Visiting the Grand Canyon is a breathtaking experience.'</li><li>'The tourism industry has been severely impacted by the pandemic.'</li><li>'Backpacking through Europe is a popular choice for young travelers.'</li></ul> |
| Food | <ul><li>'The new restaurant in town offers a fusion of Italian and Japanese cuisine.'</li><li>'Drinking eight glasses of water a day is essential for staying hydrated.'</li><li>'Cooking classes are a fun way to learn new recipes and techniques.'</li></ul> |
| Education | <ul><li>'The school district is implementing a new curriculum for the upcoming year.'</li><li>'Online learning has become increasingly popular during the pandemic.'</li><li>'The university is offering scholarships for students in financial need.'</li></ul> |
| Environment | <ul><li>'Climate change is causing a significant rise in sea levels.'</li><li>'Recycling and composting are effective ways to reduce waste.'</li><li>'The Amazon rainforest is home to millions of unique species.'</li></ul> |
| Fashion | <ul><li>'The new fashion trend is all about sustainability and eco-friendly materials.'</li><li>'The annual Met Gala is a major event in the fashion world.'</li><li>'Vintage clothing has made a comeback in recent years.'</li></ul> |
| Science | <ul><li>"NASA's Mars Rover has made significant discoveries about the red planet."</li><li>'The Nobel Prize in Physics was awarded for breakthroughs in black hole research.'</li><li>'Genetic engineering is opening up new possibilities in medical treatment.'</li></ul> |
| Sports | <ul><li>'The NBA Finals are set to begin next week with the top two teams in the league.'</li><li>'Serena Williams continues to dominate the tennis world with her powerful serve.'</li><li>'The World Cup is the most prestigious tournament in international soccer.'</li></ul> |
| Technology | <ul><li>'Artificial intelligence is changing the way we live and work.'</li><li>'The latest iPhone has a number of exciting new features.'</li><li>'Cybersecurity is becoming increasingly important as more and more data moves online.'</li></ul> |
| Entertainment | <ul><li>'The new Marvel movie is breaking box office records.'</li><li>'The Grammy Awards are a celebration of the best music of the year.'</li><li>'The latest season of Game of Thrones had fans on the edge of their seats.'</li></ul> |
## 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("EmeraldMP/ANLP_kaggle")
# Run inference
preds = model("Climate change is causing a significant rise in sea levels.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 7 | 12.8073 | 24 |
| Label | Training Sample Count |
|:--------------|:----------------------|
| Education | 23 |
| Entertainment | 23 |
| Environment | 23 |
| Fashion | 23 |
| Finance | 23 |
| Food | 23 |
| Health | 23 |
| Politics | 22 |
| Science | 23 |
| Sports | 23 |
| Technology | 23 |
| Travel | 23 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0015 | 1 | 0.2748 | - |
| 0.0727 | 50 | 0.2537 | - |
| 0.1453 | 100 | 0.1734 | - |
| 0.2180 | 150 | 0.1086 | - |
| 0.2907 | 200 | 0.062 | - |
| 0.3634 | 250 | 0.046 | - |
| 0.4360 | 300 | 0.017 | - |
| 0.5087 | 350 | 0.0104 | - |
| 0.5814 | 400 | 0.006 | - |
| 0.6541 | 450 | 0.0021 | - |
| 0.7267 | 500 | 0.0052 | - |
| 0.7994 | 550 | 0.0045 | - |
| 0.8721 | 600 | 0.0012 | - |
| 0.9448 | 650 | 0.0007 | - |
| 1.0174 | 700 | 0.0006 | - |
| 1.0901 | 750 | 0.0006 | - |
| 1.1628 | 800 | 0.0006 | - |
| 1.2355 | 850 | 0.0005 | - |
| 1.3081 | 900 | 0.0004 | - |
| 1.3808 | 950 | 0.0003 | - |
| 1.4535 | 1000 | 0.0004 | - |
| 1.5262 | 1050 | 0.0004 | - |
| 1.5988 | 1100 | 0.0004 | - |
| 1.6715 | 1150 | 0.0003 | - |
| 1.7442 | 1200 | 0.0002 | - |
| 1.8169 | 1250 | 0.0002 | - |
| 1.8895 | 1300 | 0.0005 | - |
| 1.9622 | 1350 | 0.0004 | - |
| 2.0349 | 1400 | 0.0002 | - |
| 2.1076 | 1450 | 0.0004 | - |
| 2.1802 | 1500 | 0.0002 | - |
| 2.2529 | 1550 | 0.0002 | - |
| 2.3256 | 1600 | 0.0004 | - |
| 2.3983 | 1650 | 0.0002 | - |
| 2.4709 | 1700 | 0.0002 | - |
| 2.5436 | 1750 | 0.0002 | - |
| 2.6163 | 1800 | 0.0002 | - |
| 2.6890 | 1850 | 0.0002 | - |
| 2.7616 | 1900 | 0.0003 | - |
| 2.8343 | 1950 | 0.0001 | - |
| 2.9070 | 2000 | 0.0002 | - |
| 2.9797 | 2050 | 0.0002 | - |
| 3.0523 | 2100 | 0.0003 | - |
| 3.125 | 2150 | 0.0002 | - |
| 3.1977 | 2200 | 0.0002 | - |
| 3.2703 | 2250 | 0.0001 | - |
| 3.3430 | 2300 | 0.0002 | - |
| 3.4157 | 2350 | 0.0002 | - |
| 3.4884 | 2400 | 0.0002 | - |
| 3.5610 | 2450 | 0.0001 | - |
| 3.6337 | 2500 | 0.0001 | - |
| 3.7064 | 2550 | 0.0001 | - |
| 3.7791 | 2600 | 0.0001 | - |
| 3.8517 | 2650 | 0.0001 | - |
| 3.9244 | 2700 | 0.0001 | - |
| 3.9971 | 2750 | 0.0001 | - |
| 4.0698 | 2800 | 0.0001 | - |
| 4.1424 | 2850 | 0.0001 | - |
| 4.2151 | 2900 | 0.0001 | - |
| 4.2878 | 2950 | 0.0001 | - |
| 4.3605 | 3000 | 0.0001 | - |
| 4.4331 | 3050 | 0.0001 | - |
| 4.5058 | 3100 | 0.0001 | - |
| 4.5785 | 3150 | 0.0001 | - |
| 4.6512 | 3200 | 0.0001 | - |
| 4.7238 | 3250 | 0.0001 | - |
| 4.7965 | 3300 | 0.0001 | - |
| 4.8692 | 3350 | 0.0001 | - |
| 4.9419 | 3400 | 0.0001 | - |
| 5.0145 | 3450 | 0.0001 | - |
| 5.0872 | 3500 | 0.0001 | - |
| 5.1599 | 3550 | 0.0001 | - |
| 5.2326 | 3600 | 0.0001 | - |
| 5.3052 | 3650 | 0.0001 | - |
| 5.3779 | 3700 | 0.0001 | - |
| 5.4506 | 3750 | 0.0001 | - |
| 5.5233 | 3800 | 0.0001 | - |
| 5.5959 | 3850 | 0.0001 | - |
| 5.6686 | 3900 | 0.0001 | - |
| 5.7413 | 3950 | 0.0001 | - |
| 5.8140 | 4000 | 0.0001 | - |
| 5.8866 | 4050 | 0.0001 | - |
| 5.9593 | 4100 | 0.0001 | - |
| 6.0320 | 4150 | 0.0001 | - |
| 6.1047 | 4200 | 0.0001 | - |
| 6.1773 | 4250 | 0.0001 | - |
| 6.25 | 4300 | 0.0001 | - |
| 6.3227 | 4350 | 0.0001 | - |
| 6.3953 | 4400 | 0.0001 | - |
| 6.4680 | 4450 | 0.0001 | - |
| 6.5407 | 4500 | 0.0001 | - |
| 6.6134 | 4550 | 0.0001 | - |
| 6.6860 | 4600 | 0.0001 | - |
| 6.7587 | 4650 | 0.0001 | - |
| 6.8314 | 4700 | 0.0001 | - |
| 6.9041 | 4750 | 0.0001 | - |
| 6.9767 | 4800 | 0.0 | - |
| 7.0494 | 4850 | 0.0001 | - |
| 7.1221 | 4900 | 0.0001 | - |
| 7.1948 | 4950 | 0.0001 | - |
| 7.2674 | 5000 | 0.0001 | - |
| 7.3401 | 5050 | 0.0001 | - |
| 7.4128 | 5100 | 0.0001 | - |
| 7.4855 | 5150 | 0.0001 | - |
| 7.5581 | 5200 | 0.0001 | - |
| 7.6308 | 5250 | 0.0001 | - |
| 7.7035 | 5300 | 0.0001 | - |
| 7.7762 | 5350 | 0.0001 | - |
| 7.8488 | 5400 | 0.0001 | - |
| 7.9215 | 5450 | 0.0001 | - |
| 7.9942 | 5500 | 0.0 | - |
| 8.0669 | 5550 | 0.0001 | - |
| 8.1395 | 5600 | 0.0001 | - |
| 8.2122 | 5650 | 0.0001 | - |
| 8.2849 | 5700 | 0.0 | - |
| 8.3576 | 5750 | 0.0001 | - |
| 8.4302 | 5800 | 0.0001 | - |
| 8.5029 | 5850 | 0.0001 | - |
| 8.5756 | 5900 | 0.0001 | - |
| 8.6483 | 5950 | 0.0001 | - |
| 8.7209 | 6000 | 0.0001 | - |
| 8.7936 | 6050 | 0.0001 | - |
| 8.8663 | 6100 | 0.0 | - |
| 8.9390 | 6150 | 0.0 | - |
| 9.0116 | 6200 | 0.0001 | - |
| 9.0843 | 6250 | 0.0001 | - |
| 9.1570 | 6300 | 0.0 | - |
| 9.2297 | 6350 | 0.0 | - |
| 9.3023 | 6400 | 0.0 | - |
| 9.375 | 6450 | 0.0001 | - |
| 9.4477 | 6500 | 0.0001 | - |
| 9.5203 | 6550 | 0.0001 | - |
| 9.5930 | 6600 | 0.0001 | - |
| 9.6657 | 6650 | 0.0001 | - |
| 9.7384 | 6700 | 0.0001 | - |
| 9.8110 | 6750 | 0.0001 | - |
| 9.8837 | 6800 | 0.0001 | - |
| 9.9564 | 6850 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- 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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