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---
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Now that the baffling, elongated, hyperreal coronation has occurred—no, not
that one—and Liz Truss has become Prime Minister, a degree of intervention and
action on energy bills has emerged, ahead of the looming socioeconomic catastrophe
facing the country this winter.
- text: But it needs to go much further.
- text: What could possibly go wrong?
- text: If you are White you might feel bad about hurting others or you might feel
afraid to lose this privilege….Overcoming White privilege is a job that must start
with the White community….
- text: '[JF: Obviously, immigration wasn’t stopped: the current population of the
United States is 329.5 million—it passed 300 million in 2006.'
inference: true
---
# SetFit
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A SVC 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:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a SVC instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>'ESG funds often charge many times more for investment funds that are nearly indistinguishable from those without the ESG title.'</li><li>'They are California, Florida, Illinois, Nebraska, New York, and Wyoming.'</li><li>'And so it goes.'</li></ul> |
| 1 | <ul><li>'Republicans attempted to pass a resolution that would have enabled Congress to force workers to accept a deal, which was fortunately blocked by (who else) Senator Bernie Sanders.'</li><li>'No government ever surrenders power, even its emergency powers—not really.'</li><li>'No citizen in a democratic society should want executives from $10trn financial institutions to play a larger role than they already do in defining and implementing social values.'</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("SOUMYADEEPSAR/Setfit_random_sample_svm_head")
# Run inference
preds = model("What could possibly go wrong?")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 23.4159 | 68 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 136 |
| 1 | 78 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1 | 0.3597 | - |
| 0.0161 | 50 | 0.2693 | - |
| 0.0323 | 100 | 0.2501 | - |
| 0.0484 | 150 | 0.2691 | - |
| 0.0645 | 200 | 0.063 | - |
| 0.0806 | 250 | 0.0179 | - |
| 0.0968 | 300 | 0.0044 | - |
| 0.1129 | 350 | 0.0003 | - |
| 0.1290 | 400 | 0.0005 | - |
| 0.1452 | 450 | 0.0002 | - |
| 0.1613 | 500 | 0.0003 | - |
| 0.1774 | 550 | 0.0001 | - |
| 0.1935 | 600 | 0.0001 | - |
| 0.2097 | 650 | 0.0001 | - |
| 0.2258 | 700 | 0.0001 | - |
| 0.2419 | 750 | 0.0001 | - |
| 0.2581 | 800 | 0.0 | - |
| 0.2742 | 850 | 0.0001 | - |
| 0.2903 | 900 | 0.0002 | - |
| 0.3065 | 950 | 0.0 | - |
| 0.3226 | 1000 | 0.0 | - |
| 0.3387 | 1050 | 0.0002 | - |
| 0.3548 | 1100 | 0.0 | - |
| 0.3710 | 1150 | 0.0001 | - |
| 0.3871 | 1200 | 0.0001 | - |
| 0.4032 | 1250 | 0.0 | - |
| 0.4194 | 1300 | 0.0 | - |
| 0.4355 | 1350 | 0.0 | - |
| 0.4516 | 1400 | 0.0001 | - |
| 0.4677 | 1450 | 0.0 | - |
| 0.4839 | 1500 | 0.0 | - |
| 0.5 | 1550 | 0.0001 | - |
| 0.5161 | 1600 | 0.0001 | - |
| 0.5323 | 1650 | 0.0 | - |
| 0.5484 | 1700 | 0.0 | - |
| 0.5645 | 1750 | 0.0 | - |
| 0.5806 | 1800 | 0.0 | - |
| 0.5968 | 1850 | 0.0 | - |
| 0.6129 | 1900 | 0.0 | - |
| 0.6290 | 1950 | 0.0001 | - |
| 0.6452 | 2000 | 0.0 | - |
| 0.6613 | 2050 | 0.0 | - |
| 0.6774 | 2100 | 0.0 | - |
| 0.6935 | 2150 | 0.0001 | - |
| 0.7097 | 2200 | 0.0 | - |
| 0.7258 | 2250 | 0.0 | - |
| 0.7419 | 2300 | 0.0001 | - |
| 0.7581 | 2350 | 0.0001 | - |
| 0.7742 | 2400 | 0.0001 | - |
| 0.7903 | 2450 | 0.0 | - |
| 0.8065 | 2500 | 0.0 | - |
| 0.8226 | 2550 | 0.0 | - |
| 0.8387 | 2600 | 0.0 | - |
| 0.8548 | 2650 | 0.0001 | - |
| 0.8710 | 2700 | 0.0001 | - |
| 0.8871 | 2750 | 0.0 | - |
| 0.9032 | 2800 | 0.0 | - |
| 0.9194 | 2850 | 0.0 | - |
| 0.9355 | 2900 | 0.0001 | - |
| 0.9516 | 2950 | 0.0 | - |
| 0.9677 | 3000 | 0.0001 | - |
| 0.9839 | 3050 | 0.0 | - |
| 1.0 | 3100 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.0+cu121
- Datasets: 2.20.0
- 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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