setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:mini1013/master_domain base_model:finetune:mini1013/master_domain model-index region:us
---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 셀프 다리찢기 기구 스트레칭 골반 내전근 요가 발레 스포츠/레저>댄스>댄스소품
- text: 발레바 발레봉 무용 난간대 스트레칭 학원 무용바 스포츠/레저>댄스>댄스소품
- text: 필라테스 다리찢는 스트레칭 피트니스 요가 발레 체조 I 스포츠/레저>댄스>댄스소품
- text: 댄스 발레바 스트레칭바 인용 일체형 튼튼한 유연성 바 일자형 스포츠/레저>댄스>댄스소품
- text: 발레 학원 홈 폴 바 1인 파드샤 워크 프레스 레그 봉 더블 레이어 스포츠/레저>댄스>댄스소품
inference: true
model-index:
- name: SetFit with mini1013/master_domain
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with mini1013/master_domain

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) 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:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
- **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) -->
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### 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   | <ul><li>'2단 발레바 댄스 무용바 스트레칭 연습 레그 프레스 스포츠/레저>댄스>댄스소품'</li><li>'발레바 폴댄스봉 실내 가정용 학원 스튜디오 폴봉 폴-D 50 0cm 스포츠/레저>댄스>댄스소품'</li><li>'댄스 바 스트레칭 무용 플로어 학원 발레 연습 스포츠/레저>댄스>댄스소품'</li></ul> |
| 0.0   | <ul><li>'코코랑 맨디 밸리탑 밸리댄스복 라인 줌바 댄스티 밸리복 스포츠/레저>댄스>댄스복'</li><li>'댄스 힙스카프 라인 스팽글 밸리 랩스커트 공연복 스포츠/레저>댄스>댄스복'</li><li>'코코랑 코코레이스 벨리 힙스카프 성인 밸리댄스복 라인 스포츠/레저>댄스>댄스복'</li></ul>         |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("mini1013/master_cate_sl7")
# Run inference
preds = model("발레바 발레봉 무용 난간대 스트레칭 학원 무용바 스포츠/레저>댄스>댄스소품")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 9.9786 | 18  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |

### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0357  | 1    | 0.4782        | -               |
| 1.7857  | 50   | 0.3827        | -               |
| 3.5714  | 100  | 0.0001        | -               |
| 5.3571  | 150  | 0.0           | -               |
| 7.1429  | 200  | 0.0           | -               |
| 8.9286  | 250  | 0.0           | -               |
| 10.7143 | 300  | 0.0           | -               |
| 12.5    | 350  | 0.0           | -               |
| 14.2857 | 400  | 0.0           | -               |
| 16.0714 | 450  | 0.0           | -               |
| 17.8571 | 500  | 0.0           | -               |
| 19.6429 | 550  | 0.0           | -               |
| 21.4286 | 600  | 0.0           | -               |
| 23.2143 | 650  | 0.0           | -               |
| 25.0    | 700  | 0.0           | -               |
| 26.7857 | 750  | 0.0           | -               |
| 28.5714 | 800  | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## 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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