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G1-setfit-model

Text Classification
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---
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: 0.7514450867052023
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
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
- **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
<!-- - **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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0.0 | <ul><li>'A Jewish student at McGill University has been kicked off the student government board for having “conflicts of interest” due to his pro-Israel activism.\n'</li><li>'How else to describe the decision by Big Brother USA and junior sidekick South Korea to stage major air force exercises on North Korea’s border.\n'</li><li>'DB: It was hysterical to watch these four armed guards who kept shouting “Stop resisting, stop resisting!” and they are beating the hell out of him!\n'</li></ul> |
| 1.0 | <ul><li>'The UK should never become a stage for inflammatory speakers who promote hate."\n'</li><li>'In a nation guided by fairness and law, a person is innocent until proven guilty.\n'</li><li>'Speaking of Mastercard, the David Horowitz Freedom Center just recently won a major battle with the credit card, defeating well-financed leftwing groups that are trying to run the Center out of business and suffocate free speech in America.\n'</li></ul> |
## Evaluation
### Metrics
| Label | F1 |
|:--------|:-------|
| **all** | 0.7514 |
## 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/G1-setfit-model")
# Run inference
preds = model("Are you people serious?
")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 26.2775 | 129 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 3919 |
| 1 | 240 |
### 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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0004 | 1 | 0.3542 | - |
| 0.0192 | 50 | 0.2957 | - |
| 0.0385 | 100 | 0.2509 | - |
| 0.0577 | 150 | 0.1691 | - |
| 0.0769 | 200 | 0.2145 | - |
| 0.0962 | 250 | 0.0861 | - |
| 0.1154 | 300 | 0.0677 | - |
| 0.1346 | 350 | 0.0554 | - |
| 0.1538 | 400 | 0.0169 | - |
| 0.1731 | 450 | 0.0621 | - |
| 0.1923 | 500 | 0.0024 | - |
| 0.2115 | 550 | 0.0405 | - |
| 0.2308 | 600 | 0.0724 | - |
| 0.25 | 650 | 0.0557 | - |
| 0.2692 | 700 | 0.0007 | - |
| 0.2885 | 750 | 0.0011 | - |
| 0.3077 | 800 | 0.0005 | - |
| 0.3269 | 850 | 0.0103 | - |
| 0.3462 | 900 | 0.0618 | - |
| 0.3654 | 950 | 0.0003 | - |
| 0.3846 | 1000 | 0.0046 | - |
| 0.4038 | 1050 | 0.0006 | - |
| 0.4231 | 1100 | 0.0003 | - |
| 0.4423 | 1150 | 0.0004 | - |
| 0.4615 | 1200 | 0.0006 | - |
| 0.4808 | 1250 | 0.0002 | - |
| 0.5 | 1300 | 0.0001 | - |
| 0.5192 | 1350 | 0.0002 | - |
| 0.5385 | 1400 | 0.0003 | - |
| 0.5577 | 1450 | 0.0002 | - |
| 0.5769 | 1500 | 0.0002 | - |
| 0.5962 | 1550 | 0.0003 | - |
| 0.6154 | 1600 | 0.0001 | - |
| 0.6346 | 1650 | 0.0067 | - |
| 0.6538 | 1700 | 0.0003 | - |
| 0.6731 | 1750 | 0.0001 | - |
| 0.6923 | 1800 | 0.0003 | - |
| 0.7115 | 1850 | 0.0001 | - |
| 0.7308 | 1900 | 0.0001 | - |
| 0.75 | 1950 | 0.0006 | - |
| 0.7692 | 2000 | 0.0001 | - |
| 0.7885 | 2050 | 0.0001 | - |
| 0.8077 | 2100 | 0.0 | - |
| 0.8269 | 2150 | 0.0 | - |
| 0.8462 | 2200 | 0.0 | - |
| 0.8654 | 2250 | 0.0 | - |
| 0.8846 | 2300 | 0.0002 | - |
| 0.9038 | 2350 | 0.0001 | - |
| 0.9231 | 2400 | 0.0001 | - |
| 0.9423 | 2450 | 0.0003 | - |
| 0.9615 | 2500 | 0.0001 | - |
| 0.9808 | 2550 | 0.0005 | - |
| 1.0 | 2600 | 0.0 | 0.1875 |
| 1.0192 | 2650 | 0.0 | - |
| 1.0385 | 2700 | 0.0003 | - |
| 1.0577 | 2750 | 0.0 | - |
| 1.0769 | 2800 | 0.0001 | - |
| 1.0962 | 2850 | 0.0472 | - |
| 1.1154 | 2900 | 0.0 | - |
| 1.1346 | 2950 | 0.0 | - |
| 1.1538 | 3000 | 0.0001 | - |
| 1.1731 | 3050 | 0.0001 | - |
| 1.1923 | 3100 | 0.0 | - |
| 1.2115 | 3150 | 0.0003 | - |
| 1.2308 | 3200 | 0.0 | - |
| 1.25 | 3250 | 0.0 | - |
| 1.2692 | 3300 | 0.0245 | - |
| 1.2885 | 3350 | 0.0 | - |
| 1.3077 | 3400 | 0.0 | - |
| 1.3269 | 3450 | 0.0 | - |
| 1.3462 | 3500 | 0.0001 | - |
| 1.3654 | 3550 | 0.0 | - |
| 1.3846 | 3600 | 0.0 | - |
| 1.4038 | 3650 | 0.0 | - |
| 1.4231 | 3700 | 0.0 | - |
| 1.4423 | 3750 | 0.0 | - |
| 1.4615 | 3800 | 0.0 | - |
| 1.4808 | 3850 | 0.0 | - |
| 1.5 | 3900 | 0.0 | - |
| 1.5192 | 3950 | 0.0 | - |
| 1.5385 | 4000 | 0.0 | - |
| 1.5577 | 4050 | 0.0 | - |
| 1.5769 | 4100 | 0.0 | - |
| 1.5962 | 4150 | 0.0 | - |
| 1.6154 | 4200 | 0.0 | - |
| 1.6346 | 4250 | 0.0001 | - |
| 1.6538 | 4300 | 0.0 | - |
| 1.6731 | 4350 | 0.0 | - |
| 1.6923 | 4400 | 0.0 | - |
| 1.7115 | 4450 | 0.0 | - |
| 1.7308 | 4500 | 0.0 | - |
| 1.75 | 4550 | 0.0 | - |
| 1.7692 | 4600 | 0.0 | - |
| 1.7885 | 4650 | 0.0 | - |
| 1.8077 | 4700 | 0.0 | - |
| 1.8269 | 4750 | 0.0 | - |
| 1.8462 | 4800 | 0.0001 | - |
| 1.8654 | 4850 | 0.0 | - |
| 1.8846 | 4900 | 0.0 | - |
| 1.9038 | 4950 | 0.0 | - |
| 1.9231 | 5000 | 0.0 | - |
| 1.9423 | 5050 | 0.0 | - |
| 1.9615 | 5100 | 0.0 | - |
| 1.9808 | 5150 | 0.0 | - |
| **2.0** | **5200** | **0.0** | **0.1393** |
* 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}
}
```
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