---
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
### 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 |
- 'The mayor announced a new initiative to improve public transportation.'
- 'The senator is facing criticism for her stance on the recent bill.'
- 'The upcoming election has sparked intense debates among the candidates.'
|
| Health | - 'Regular exercise and a balanced diet are key to maintaining good health.'
- 'The World Health Organization has issued new guidelines on COVID-19.'
- 'A new study reveals the benefits of meditation for mental health.'
|
| Finance | - 'The stock market saw a significant drop following the announcement.'
- 'Investing in real estate can be a profitable venture if done correctly.'
- "The company's profits have doubled since the launch of their new product."
|
| Travel | - 'Visiting the Grand Canyon is a breathtaking experience.'
- 'The tourism industry has been severely impacted by the pandemic.'
- 'Backpacking through Europe is a popular choice for young travelers.'
|
| Food | - 'The new restaurant in town offers a fusion of Italian and Japanese cuisine.'
- 'Drinking eight glasses of water a day is essential for staying hydrated.'
- 'Cooking classes are a fun way to learn new recipes and techniques.'
|
| Education | - 'The school district is implementing a new curriculum for the upcoming year.'
- 'Online learning has become increasingly popular during the pandemic.'
- 'The university is offering scholarships for students in financial need.'
|
| Environment | - 'Climate change is causing a significant rise in sea levels.'
- 'Recycling and composting are effective ways to reduce waste.'
- 'The Amazon rainforest is home to millions of unique species.'
|
| Fashion | - 'The new fashion trend is all about sustainability and eco-friendly materials.'
- 'The annual Met Gala is a major event in the fashion world.'
- 'Vintage clothing has made a comeback in recent years.'
|
| Science | - "NASA's Mars Rover has made significant discoveries about the red planet."
- 'The Nobel Prize in Physics was awarded for breakthroughs in black hole research.'
- 'Genetic engineering is opening up new possibilities in medical treatment.'
|
| Sports | - 'The NBA Finals are set to begin next week with the top two teams in the league.'
- 'Serena Williams continues to dominate the tennis world with her powerful serve.'
- 'The World Cup is the most prestigious tournament in international soccer.'
|
| Technology | - 'Artificial intelligence is changing the way we live and work.'
- 'The latest iPhone has a number of exciting new features.'
- 'Cybersecurity is becoming increasingly important as more and more data moves online.'
|
| Entertainment | - 'The new Marvel movie is breaking box office records.'
- 'The Grammy Awards are a celebration of the best music of the year.'
- 'The latest season of Game of Thrones had fans on the edge of their seats.'
|
## 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.")
```
## 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: (1, 1)
- 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 | 0.2839 | - |
| 0.0727 | 50 | 0.1245 | - |
| 0.1453 | 100 | 0.1334 | - |
| 0.2180 | 150 | 0.0142 | - |
| 0.2907 | 200 | 0.0046 | - |
| 0.3634 | 250 | 0.0024 | - |
| 0.4360 | 300 | 0.0019 | - |
| 0.5087 | 350 | 0.0011 | - |
| 0.5814 | 400 | 0.0013 | - |
| 0.6541 | 450 | 0.0007 | - |
| 0.7267 | 500 | 0.0011 | - |
| 0.7994 | 550 | 0.001 | - |
| 0.8721 | 600 | 0.001 | - |
| 0.9448 | 650 | 0.0004 | - |
### 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}
}
```