---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- f1
widget:
- text: What could possibly go wrong?
- text: We may have faith that human inventiveness will prevail in the long run.
- text: That can happen again.
- text: But in fact it was intensely rational.
- text: Chinese crime, like Chinese cuisine, varies according to regional origin.
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: f1
value: 0.7526132404181185
name: F1
---
# 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
- **Classification head:** a SVC instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 2 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| SUBJ | - 'Gone are the days when they led the world in recession-busting'
- '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?'
- '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!'
|
| OBJ | - 'Is this a warning of what’s to come?'
- '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.'
- 'Socialists believe that, if everyone cannot have something, no one shall.'
|
## Evaluation
### Metrics
| Label | F1 |
|:--------|:-------|
| **all** | 0.7526 |
## 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_subj_SVC")
# Run inference
preds = model("That can happen again.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 35.9834 | 97 |
| Label | Training Sample Count |
|:------|:----------------------|
| OBJ | 117 |
| SUBJ | 124 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (1e-05, 1e-05)
- head_learning_rate: 1e-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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0008 | 1 | 0.3862 | - |
| 0.0415 | 50 | 0.4092 | - |
| 0.0830 | 100 | 0.3596 | - |
| 0.1245 | 150 | 0.2618 | - |
| 0.1660 | 200 | 0.2447 | - |
| 0.2075 | 250 | 0.263 | - |
| 0.2490 | 300 | 0.2583 | - |
| 0.2905 | 350 | 0.3336 | - |
| 0.3320 | 400 | 0.2381 | - |
| 0.3734 | 450 | 0.2454 | - |
| 0.4149 | 500 | 0.259 | - |
| 0.4564 | 550 | 0.2083 | - |
| 0.4979 | 600 | 0.2437 | - |
| 0.5394 | 650 | 0.2231 | - |
| 0.5809 | 700 | 0.0891 | - |
| 0.6224 | 750 | 0.1164 | - |
| 0.6639 | 800 | 0.0156 | - |
| 0.7054 | 850 | 0.0394 | - |
| 0.7469 | 900 | 0.0065 | - |
| 0.7884 | 950 | 0.0024 | - |
| 0.8299 | 1000 | 0.0012 | - |
| 0.8714 | 1050 | 0.0014 | - |
| 0.9129 | 1100 | 0.0039 | - |
| 0.9544 | 1150 | 0.0039 | - |
| 0.9959 | 1200 | 0.001 | - |
| 1.0373 | 1250 | 0.0007 | - |
| 1.0788 | 1300 | 0.0003 | - |
| 1.1203 | 1350 | 0.001 | - |
| 1.1618 | 1400 | 0.0003 | - |
| 1.2033 | 1450 | 0.0003 | - |
| 1.2448 | 1500 | 0.0014 | - |
| 1.2863 | 1550 | 0.0003 | - |
| 1.3278 | 1600 | 0.0003 | - |
| 1.3693 | 1650 | 0.0001 | - |
| 1.4108 | 1700 | 0.0004 | - |
| 1.4523 | 1750 | 0.0003 | - |
| 1.4938 | 1800 | 0.0008 | - |
| 1.5353 | 1850 | 0.0002 | - |
| 1.5768 | 1900 | 0.0005 | - |
| 1.6183 | 1950 | 0.0002 | - |
| 1.6598 | 2000 | 0.0004 | - |
| 1.7012 | 2050 | 0.0001 | - |
| 1.7427 | 2100 | 0.0002 | - |
| 1.7842 | 2150 | 0.0002 | - |
| 1.8257 | 2200 | 0.0002 | - |
| 1.8672 | 2250 | 0.0003 | - |
| 1.9087 | 2300 | 0.0001 | - |
| 1.9502 | 2350 | 0.0002 | - |
| 1.9917 | 2400 | 0.0001 | - |
| 2.0332 | 2450 | 0.0003 | - |
| 2.0747 | 2500 | 0.0002 | - |
| 2.1162 | 2550 | 0.0001 | - |
| 2.1577 | 2600 | 0.0001 | - |
| 2.1992 | 2650 | 0.0004 | - |
| 2.2407 | 2700 | 0.0002 | - |
| 2.2822 | 2750 | 0.0001 | - |
| 2.3237 | 2800 | 0.0005 | - |
| 2.3651 | 2850 | 0.0002 | - |
| 2.4066 | 2900 | 0.0003 | - |
| 2.4481 | 2950 | 0.0001 | - |
| 2.4896 | 3000 | 0.0001 | - |
| 2.5311 | 3050 | 0.0001 | - |
| 2.5726 | 3100 | 0.0001 | - |
| 2.6141 | 3150 | 0.0002 | - |
| 2.6556 | 3200 | 0.0001 | - |
| 2.6971 | 3250 | 0.0002 | - |
| 2.7386 | 3300 | 0.0002 | - |
| 2.7801 | 3350 | 0.0001 | - |
| 2.8216 | 3400 | 0.0001 | - |
| 2.8631 | 3450 | 0.0001 | - |
| 2.9046 | 3500 | 0.0001 | - |
| 2.9461 | 3550 | 0.0 | - |
| 2.9876 | 3600 | 0.0002 | - |
| 3.0290 | 3650 | 0.0001 | - |
| 3.0705 | 3700 | 0.0 | - |
| 3.1120 | 3750 | 0.0001 | - |
| 3.1535 | 3800 | 0.0001 | - |
| 3.1950 | 3850 | 0.0001 | - |
| 3.2365 | 3900 | 0.0001 | - |
| 3.2780 | 3950 | 0.0001 | - |
| 3.3195 | 4000 | 0.0001 | - |
| 3.3610 | 4050 | 0.0001 | - |
| 3.4025 | 4100 | 0.0 | - |
| 3.4440 | 4150 | 0.0001 | - |
| 3.4855 | 4200 | 0.0001 | - |
| 3.5270 | 4250 | 0.0001 | - |
| 3.5685 | 4300 | 0.0001 | - |
| 3.6100 | 4350 | 0.0002 | - |
| 3.6515 | 4400 | 0.0001 | - |
| 3.6929 | 4450 | 0.0001 | - |
| 3.7344 | 4500 | 0.0 | - |
| 3.7759 | 4550 | 0.0 | - |
| 3.8174 | 4600 | 0.0001 | - |
| 3.8589 | 4650 | 0.0001 | - |
| 3.9004 | 4700 | 0.0001 | - |
| 3.9419 | 4750 | 0.0 | - |
| 3.9834 | 4800 | 0.0001 | - |
| 4.0249 | 4850 | 0.0001 | - |
| 4.0664 | 4900 | 0.0001 | - |
| 4.1079 | 4950 | 0.0001 | - |
| 4.1494 | 5000 | 0.0 | - |
| 4.1909 | 5050 | 0.0 | - |
| 4.2324 | 5100 | 0.0 | - |
| 4.2739 | 5150 | 0.0 | - |
| 4.3154 | 5200 | 0.0001 | - |
| 4.3568 | 5250 | 0.0001 | - |
| 4.3983 | 5300 | 0.0001 | - |
| 4.4398 | 5350 | 0.0 | - |
| 4.4813 | 5400 | 0.0001 | - |
| 4.5228 | 5450 | 0.0 | - |
| 4.5643 | 5500 | 0.0001 | - |
| 4.6058 | 5550 | 0.0001 | - |
| 4.6473 | 5600 | 0.0001 | - |
| 4.6888 | 5650 | 0.0 | - |
| 4.7303 | 5700 | 0.0001 | - |
| 4.7718 | 5750 | 0.0001 | - |
| 4.8133 | 5800 | 0.0001 | - |
| 4.8548 | 5850 | 0.0 | - |
| 4.8963 | 5900 | 0.0 | - |
| 4.9378 | 5950 | 0.0 | - |
| 4.9793 | 6000 | 0.0001 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## 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}
}
```