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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 RandomForestClassifier 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 RandomForestClassifier 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | <ul><li>'Gone are the days when they led the world in recession-busting'</li><li>'Who so mean that he will not himself be taxed, who so mindful of wealth that he will not favor increasing the popular taxes, in aid of these defective children?'</li><li>'That state has sixty-two counties and sixty cities … In addition there are 932 towns, 507 villages, and, at the last count, 9,600 school districts … Just try to render efficient service … amid the diffused identities and inevitable jealousies of, roughly, 11,000 independent administrative officers or boards!'</li></ul> |
| 0 | <ul><li>'Is this a warning of what’s to come?'</li><li>'This unique set of circumstances has brought PCL back into focus as the safe haven of choice for global players seeking somewhere to stash their cash.'</li><li>'Socialists believe that, if everyone cannot have something, no one shall.'</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_designed_sample_random_forest_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 | 36.5327 | 97 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 100 |
| 1 | 114 |
### 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 | 0.3958 | - |
| 0.0172 | 50 | 0.343 | - |
| 0.0345 | 100 | 0.2775 | - |
| 0.0517 | 150 | 0.2861 | - |
| 0.0689 | 200 | 0.1937 | - |
| 0.0861 | 250 | 0.0891 | - |
| 0.1034 | 300 | 0.0089 | - |
| 0.1206 | 350 | 0.0179 | - |
| 0.1378 | 400 | 0.0002 | - |
| 0.1551 | 450 | 0.0004 | - |
| 0.1723 | 500 | 0.0002 | - |
| 0.1895 | 550 | 0.0001 | - |
| 0.2068 | 600 | 0.0001 | - |
| 0.2240 | 650 | 0.0002 | - |
| 0.2412 | 700 | 0.0001 | - |
| 0.2584 | 750 | 0.0001 | - |
| 0.2757 | 800 | 0.0001 | - |
| 0.2929 | 850 | 0.0001 | - |
| 0.3101 | 900 | 0.0001 | - |
| 0.3274 | 950 | 0.0002 | - |
| 0.3446 | 1000 | 0.0 | - |
| 0.3618 | 1050 | 0.0001 | - |
| 0.3790 | 1100 | 0.0001 | - |
| 0.3963 | 1150 | 0.0001 | - |
| 0.4135 | 1200 | 0.0001 | - |
| 0.4307 | 1250 | 0.0001 | - |
| 0.4480 | 1300 | 0.0001 | - |
| 0.4652 | 1350 | 0.0 | - |
| 0.4824 | 1400 | 0.0 | - |
| 0.4997 | 1450 | 0.0 | - |
| 0.5169 | 1500 | 0.0 | - |
| 0.5341 | 1550 | 0.0001 | - |
| 0.5513 | 1600 | 0.0 | - |
| 0.5686 | 1650 | 0.0 | - |
| 0.5858 | 1700 | 0.0 | - |
| 0.6030 | 1750 | 0.0 | - |
| 0.6203 | 1800 | 0.0 | - |
| 0.6375 | 1850 | 0.0 | - |
| 0.6547 | 1900 | 0.0 | - |
| 0.6720 | 1950 | 0.0 | - |
| 0.6892 | 2000 | 0.0 | - |
| 0.7064 | 2050 | 0.0 | - |
| 0.7236 | 2100 | 0.0 | - |
| 0.7409 | 2150 | 0.0 | - |
| 0.7581 | 2200 | 0.0 | - |
| 0.7753 | 2250 | 0.0 | - |
| 0.7926 | 2300 | 0.0001 | - |
| 0.8098 | 2350 | 0.0001 | - |
| 0.8270 | 2400 | 0.0 | - |
| 0.8442 | 2450 | 0.0001 | - |
| 0.8615 | 2500 | 0.0 | - |
| 0.8787 | 2550 | 0.0 | - |
| 0.8959 | 2600 | 0.0 | - |
| 0.9132 | 2650 | 0.0 | - |
| 0.9304 | 2700 | 0.0 | - |
| 0.9476 | 2750 | 0.0 | - |
| 0.9649 | 2800 | 0.0 | - |
| 0.9821 | 2850 | 0.0 | - |
| 0.9993 | 2900 | 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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