---
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Point out any dull descriptions that need more color
- text: Find places where I repeat my main points unnecessarily
- text: What's a compelling method to reveal a secret in my plot
- text: How do I handle flashbacks in a non-linear story
- text: Suggest some comedic elements to lighten a dark plot
inference: true
---

# 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:** 3 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>'Can you identify specific areas that need improvement in my text'</li><li>'Point out the flaws in my writing style, please'</li><li>'Which parts of my draft are the weakest'</li></ul>                                |
| 0     | <ul><li>"How do I make my character's driving force more compelling"</li><li>"Any tips to deepen my protagonist's underlying goals"</li><li>"Suggestions for strengthening the reasons behind my character's actions"</li></ul> |
| 2     | <ul><li>'How does the Pro version elevate my writing experience'</li><li>'Could you list the premium perks of Quarkle Pro'</li><li>'What special advantages come with upgrading to Pro'</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("setfit_model_id")
# Run inference
preds = model("How do I handle flashbacks in a non-linear story")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 8.7947 | 14  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 153                   |
| 1     | 144                   |
| 2     | 117                   |

### Framework Versions
- Python: 3.10.15
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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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