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 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: 군용 단검 스포츠/레저>무술용품>목검/가검
- text: 에버라스트 초보연습용 스폰지쌍절곤 스포츠/레저 > 무술용품 > 봉/곤/창
- text: 봉술 나무봉 수련용 창술 막대 훈련 등봉 연습용 장봉 블랙 스포츠/레저 > 무술용품 > 봉/곤/창
- text: 홈목검C-27 스포츠/레저>무술용품>목검/가검
- text: 태극권 수련 훈련 무술 장비 나무 봉 창 닌자풍실막대 이소룡 단봉 흑단목 필리핀 스포츠/레저 > 무술용품 > 봉/곤/창
inference: true
---

# 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:** 3 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                                                                                                                                                                                                   |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2.0   | <ul><li>'봉술 나무봉 수련용 창술 막대 훈련 등봉 연습용 장봉 블랙 스포츠/레저 > 무술용품 > 봉/곤/창'</li><li>'에버라스트 초보연습용 스폰지쌍절곤 스포츠/레저 > 무술용품 > 봉/곤/창'</li><li>'프로칸 25cm 스폰지 쌍절곤 쌍절봉 안전 연습용 수련용 호신용 가벼운 초급자용 스포츠/레저 > 무술용품 > 봉/곤/창'</li></ul> |
| 1.0   | <ul><li>'켄신 중도 스포츠/레저>무술용품>목검/가검'</li><li>'초장도 스포츠/레저>무술용품>목검/가검'</li><li>'대조영도검 스포츠/레저>무술용품>목검/가검'</li></ul>                                                                                              |
| 0.0   | <ul><li>'무술 창 연극 소품 기타무술용품 스포츠/레저 > 무술용품 > 기타무술용품'</li><li>'무술 창 연극 소품 기타무술용품 P 1개 스포츠/레저 > 무술용품 > 기타무술용품'</li><li>'죽선 D-1 대나무 부채 부채술 무술용품 스포츠/레저 > 무술용품 > 기타무술용품'</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("mini1013/master_cate_sl10")
# Run inference
preds = model("군용 단검 스포츠/레저>무술용품>목검/가검")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 8.9677 | 18  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 10                    |
| 1.0   | 9                     |
| 2.0   | 12                    |

### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.1429  | 1    | 0.4962        | -               |
| 7.1429  | 50   | 0.1601        | -               |
| 14.2857 | 100  | 0.0001        | -               |
| 21.4286 | 150  | 0.0           | -               |
| 28.5714 | 200  | 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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