README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Multiple non-calcified nodules in left lower lobe, ranging from 3-6mm. Follow-up
    CT scan recommended.
- text: Heart size and mediastinal contours normal. Lungs clear, no consolidation
    or acute abnormality. No pleural effusion or pneumothorax. No acute osseous abnormalities.
    Small 0.5 cm calcified granuloma in right upper lobe, likely benign from prior
    infection. No acute cardiopulmonary disease. Incidental right upper lobe granuloma.
    No follow-up needed unless clinically indicated. nan
- text: Patchy consolidation in RLL consistent with pneumonia. Recommend clinical
    follow-up.
- text: Calcified granuloma in the left lower lobe, incidental finding.  Findings
    consistent with COPD exacerbation.
- text: Moderate pleural effusion and patchy consolidation in bilateral lung bases.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
---

# SetFit

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A OneVsRestClassifier 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 OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)

## 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("Moderate pleural effusion and patchy consolidation in bilateral lung bases.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 7   | 17.5143 | 53  |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0096 | 1    | 0.1417        | -               |
| 0.4808 | 50   | 0.1824        | -               |
| 0.9615 | 100  | 0.1083        | -               |
| 1.4423 | 150  | 0.0817        | -               |
| 1.9231 | 200  | 0.0777        | -               |
| 2.4038 | 250  | 0.0636        | -               |
| 2.8846 | 300  | 0.0649        | -               |
| 3.3654 | 350  | 0.0603        | -               |
| 3.8462 | 400  | 0.0713        | -               |
| 4.3269 | 450  | 0.0507        | -               |
| 4.8077 | 500  | 0.0569        | -               |
| 5.2885 | 550  | 0.0553        | -               |
| 5.7692 | 600  | 0.0614        | -               |
| 6.25   | 650  | 0.0512        | -               |
| 6.7308 | 700  | 0.0559        | -               |
| 7.2115 | 750  | 0.0512        | -               |
| 7.6923 | 800  | 0.0464        | -               |
| 8.1731 | 850  | 0.0547        | -               |
| 8.6538 | 900  | 0.0455        | -               |
| 9.1346 | 950  | 0.0524        | -               |
| 9.6154 | 1000 | 0.0526        | -               |

### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.2
- Sentence Transformers: 4.0.2
- Transformers: 4.51.2
- PyTorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1

## 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}
}
```

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