README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 경중등도 지방간으로 병적 소견이며 간 기능 저하 위험이 우려됩니다
- text: 공복 혈당 상승으로 검사 이상이 확인되었습니다.
- text: 위염(염증 소견), 담낭 용종, 갑상선 초음파의 불균일한 에코 의심 소견 등 임상적으로 의미 있는 이상 소견이 확인됩니다.
- text: 유방에 결절이 관찰되어 병적 소견의 가능성이 있어 추적 관찰이 필요함
- text: 확산강조영상(DWI)에서 뇌실질의 급성 뇌경색 및 특이소견이 관찰되지 않아 정상 소견으로 판단
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: BAAI/bge-m3
model-index:
- name: SetFit with BAAI/bge-m3
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9976095617529881
      name: Accuracy
---

# SetFit with BAAI/bge-m3

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) as the Sentence Transformer embedding model. 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 body:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 8192 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                                                                                                                                                                                          |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'공복 혈당 상승은 검사 이상으로 임상적으로 의미 있음(당뇨 가능성 확인 필요).'</li><li>'CPK 상승으로 근손상 가능성 및 근육 질환 의심이 제시됨.'</li><li>'자궁경부세포검사에서 위축성 세포 변화가 확인되어 검사 이상 소견이 있음'</li></ul>                                    |
| 0     | <ul><li>'소견에 구체적인 이상 소견이나 검사 결과가 명시되어 있지 않고, 결과를 별지참조라고 되어 있어 임상적 이상 여부를 판단할 수 없음.'</li><li>'전립선 석회화는 과거 염증의 흔적일 수 있으며 현재 이상 소견이 없어 임상적 의미 없음'</li><li>'폐경 후 상태에서 정상 소견이므로 임상적으로 이상 없음'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9976   |

## 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("Ja-ck/setfit-medical-binary-classifier")
# Run inference
preds = model("공복 혈당 상승으로 검사 이상이 확인되었습니다.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 10.7004 | 50  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 1404                  |
| 1     | 3613                  |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 16)
- max_steps: 200
- 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 |
|:-----:|:----:|:-------------:|:---------------:|
| 0.005 | 1    | 0.1863        | -               |
| 0.25  | 50   | 0.0461        | -               |
| 0.5   | 100  | 0.0011        | -               |
| 0.75  | 150  | 0.0008        | -               |
| 1.0   | 200  | 0.001         | -               |

### Framework Versions
- Python: 3.12.3
- SetFit: 1.1.3
- Sentence Transformers: 5.1.2
- Transformers: 4.57.1
- PyTorch: 2.8.0+cu128
- Datasets: 4.4.1
- Tokenizers: 0.22.1

## 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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