---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- f1
widget:
- text: 'The Democratic Party was totally corrupted by the Clinton Regime, and now
it is totally insane.
'
- text: 'The media gave scant coverage to Obama’s close relationship with radical
Reverend Jeremiah “God damn America) Wright who blamed the US for 9/11.
'
- text: 'It’s sharia compliance in New Mexico.
'
- text: 'Are you people serious?
'
- text: 'However, I ask, why were you not involved in the first place, Mr. President?
'
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: 0.6720214190093708
name: F1
---
# SetFit
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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
- **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:** 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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0 | - '#ukraine Be careful social media and Google are censoring non-propaganda news story like how Ukrainian defense minister is using video games to give the impression they are defeating the Russians to keep the conflict going ! #biden is warmongering Ceasefire , peace & neutrality NOW HTTPURL'
- 'https://t.co/CjSFJmng7Z — Sen. Patrick Leahy (@SenatorLeahy) August 1, 2018\n'
- 'On Monday afternoon, Homeland Security Secretary Kirstjen Nielsen tweeted out photos of CBP officers in riot gear as well as the barbed wire and barriers citing the reports about plans to “rush” the border.\n'
|
| 0.0 | - 'President Trump noted that President Obama and his advisers had information that the Russians had been working to interfere in the election and they ignored it, because they thought Hillary Clinton was going to win.\n'
- 'Once the truth is accepted that jihadis are inspired and sanctioned by their Islamic texts, it must logically become required that mosques, Islamic schools and groups have to immediately curtail any teaching that motivates sedition, violence, and hatred of unbelievers (i.e.\n'
- '“However, no nation has a more talented, more dedicated group of law enforcement investigators and prosecutors than the United States.”\n'
|
## Evaluation
### Metrics
| Label | F1 |
|:--------|:-------|
| **all** | 0.6720 |
## 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("anismahmahi/G3-setfit-model")
# Run inference
preds = model("Are you people serious?
")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 28.3246 | 129 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 2362 |
| 1 | 2518 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0003 | 1 | 0.3302 | - |
| 0.0164 | 50 | 0.2709 | - |
| 0.0328 | 100 | 0.2545 | - |
| 0.0492 | 150 | 0.229 | - |
| 0.0656 | 200 | 0.2463 | - |
| 0.0820 | 250 | 0.2934 | - |
| 0.0984 | 300 | 0.2735 | - |
| 0.1148 | 350 | 0.2837 | - |
| 0.1311 | 400 | 0.2364 | - |
| 0.1475 | 450 | 0.2379 | - |
| 0.1639 | 500 | 0.188 | - |
| 0.1803 | 550 | 0.2443 | - |
| 0.1967 | 600 | 0.1274 | - |
| 0.2131 | 650 | 0.2106 | - |
| 0.2295 | 700 | 0.3211 | - |
| 0.2459 | 750 | 0.2443 | - |
| 0.2623 | 800 | 0.1979 | - |
| 0.2787 | 850 | 0.1679 | - |
| 0.2951 | 900 | 0.1208 | - |
| 0.3115 | 950 | 0.0594 | - |
| 0.3279 | 1000 | 0.11 | - |
| 0.3443 | 1050 | 0.0951 | - |
| 0.3607 | 1100 | 0.1059 | - |
| 0.3770 | 1150 | 0.1027 | - |
| 0.3934 | 1200 | 0.0771 | - |
| 0.4098 | 1250 | 0.0295 | - |
| 0.4262 | 1300 | 0.0696 | - |
| 0.4426 | 1350 | 0.104 | - |
| 0.4590 | 1400 | 0.13 | - |
| 0.4754 | 1450 | 0.1287 | - |
| 0.4918 | 1500 | 0.0264 | - |
| 0.5082 | 1550 | 0.0651 | - |
| 0.5246 | 1600 | 0.113 | - |
| 0.5410 | 1650 | 0.07 | - |
| 0.5574 | 1700 | 0.0016 | - |
| 0.5738 | 1750 | 0.1001 | - |
| 0.5902 | 1800 | 0.0116 | - |
| 0.6066 | 1850 | 0.01 | - |
| 0.6230 | 1900 | 0.0115 | - |
| 0.6393 | 1950 | 0.0053 | - |
| 0.6557 | 2000 | 0.0585 | - |
| 0.6721 | 2050 | 0.0034 | - |
| 0.6885 | 2100 | 0.0171 | - |
| 0.7049 | 2150 | 0.0141 | - |
| 0.7213 | 2200 | 0.0549 | - |
| 0.7377 | 2250 | 0.0026 | - |
| 0.7541 | 2300 | 0.1239 | - |
| 0.7705 | 2350 | 0.0121 | - |
| 0.7869 | 2400 | 0.0589 | - |
| 0.8033 | 2450 | 0.0042 | - |
| 0.8197 | 2500 | 0.0026 | - |
| 0.8361 | 2550 | 0.003 | - |
| 0.8525 | 2600 | 0.0004 | - |
| 0.8689 | 2650 | 0.0003 | - |
| 0.8852 | 2700 | 0.1 | - |
| 0.9016 | 2750 | 0.0567 | - |
| 0.9180 | 2800 | 0.0311 | - |
| 0.9344 | 2850 | 0.0404 | - |
| 0.9508 | 2900 | 0.0002 | - |
| 0.9672 | 2950 | 0.0008 | - |
| 0.9836 | 3000 | 0.0006 | - |
| **1.0** | **3050** | **0.0003** | **0.3187** |
| 1.0164 | 3100 | 0.0003 | - |
| 1.0328 | 3150 | 0.0002 | - |
| 1.0492 | 3200 | 0.0002 | - |
| 1.0656 | 3250 | 0.002 | - |
| 1.0820 | 3300 | 0.0002 | - |
| 1.0984 | 3350 | 0.0003 | - |
| 1.1148 | 3400 | 0.005 | - |
| 1.1311 | 3450 | 0.0613 | - |
| 1.1475 | 3500 | 0.0002 | - |
| 1.1639 | 3550 | 0.0002 | - |
| 1.1803 | 3600 | 0.0005 | - |
| 1.1967 | 3650 | 0.0001 | - |
| 1.2131 | 3700 | 0.0609 | - |
| 1.2295 | 3750 | 0.0003 | - |
| 1.2459 | 3800 | 0.0005 | - |
| 1.2623 | 3850 | 0.0006 | - |
| 1.2787 | 3900 | 0.0003 | - |
| 1.2951 | 3950 | 0.0014 | - |
| 1.3115 | 4000 | 0.0002 | - |
| 1.3279 | 4050 | 0.0001 | - |
| 1.3443 | 4100 | 0.0002 | - |
| 1.3607 | 4150 | 0.001 | - |
| 1.3770 | 4200 | 0.0004 | - |
| 1.3934 | 4250 | 0.0004 | - |
| 1.4098 | 4300 | 0.0002 | - |
| 1.4262 | 4350 | 0.0612 | - |
| 1.4426 | 4400 | 0.0613 | - |
| 1.4590 | 4450 | 0.0002 | - |
| 1.4754 | 4500 | 0.0603 | - |
| 1.4918 | 4550 | 0.0001 | - |
| 1.5082 | 4600 | 0.0011 | - |
| 1.5246 | 4650 | 0.0576 | - |
| 1.5410 | 4700 | 0.0001 | - |
| 1.5574 | 4750 | 0.0002 | - |
| 1.5738 | 4800 | 0.0002 | - |
| 1.5902 | 4850 | 0.0012 | - |
| 1.6066 | 4900 | 0.0003 | - |
| 1.6230 | 4950 | 0.0001 | - |
| 1.6393 | 5000 | 0.0001 | - |
| 1.6557 | 5050 | 0.0001 | - |
| 1.6721 | 5100 | 0.0001 | - |
| 1.6885 | 5150 | 0.0001 | - |
| 1.7049 | 5200 | 0.0002 | - |
| 1.7213 | 5250 | 0.0001 | - |
| 1.7377 | 5300 | 0.0002 | - |
| 1.7541 | 5350 | 0.0001 | - |
| 1.7705 | 5400 | 0.0001 | - |
| 1.7869 | 5450 | 0.0001 | - |
| 1.8033 | 5500 | 0.0001 | - |
| 1.8197 | 5550 | 0.0003 | - |
| 1.8361 | 5600 | 0.0001 | - |
| 1.8525 | 5650 | 0.0001 | - |
| 1.8689 | 5700 | 0.0001 | - |
| 1.8852 | 5750 | 0.0001 | - |
| 1.9016 | 5800 | 0.0002 | - |
| 1.9180 | 5850 | 0.0 | - |
| 1.9344 | 5900 | 0.0001 | - |
| 1.9508 | 5950 | 0.0 | - |
| 1.9672 | 6000 | 0.0 | - |
| 1.9836 | 6050 | 0.0001 | - |
| 2.0 | 6100 | 0.0001 | 0.3313 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0
## 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}
}
```