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:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 명절선물 동원참치 S12호 참치선물세트 설선물 한가위 동원참치 S12호 제이에스포
- text: 동원참치 덕용 업소용 대용량 덕용 참치 1.88kg  주식회사 이너피스(inner peace)
- text: 사조 자연산 골뱅이 400g  주식회사 당장만나
- text: 목우촌 뚝심 340g  장보고가
- text: 농심 알쿠니아 황도 2절 통조림 850g 알쿠니아 황도 통조림 200g x 3개입 지에스(GS) 금성상회
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: metric
      value: 0.9854036341971999
      name: Metric
---

# 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:** 9 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                                                                                                                                                                                                    |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6.0   | <ul><li>'그린올리브 365g 동서 리치스 올리브 샐러드  화남F.C'</li><li>'동서 리치스 슬라이스 오이피클 3kg  무성유통'</li><li>'리치스 슬라이스 오이피클 3kg 피클  화남F.C'</li></ul>                                                                             |
| 3.0   | <ul><li>'CJ제일제당 스팸12호 1세트  위드'</li><li>'CJ제일제당 스팸 복합 5호 선물세트  보담유통'</li><li>'스팸복합5호 햄 카놀라유 선물세트 복합 명절 추석 세트  땡그리나'</li></ul>                                                                                |
| 4.0   | <ul><li>'동원 스위트콘 340g 골든 동원 저스트 스위트콘 340g(리뉴얼) 중앙 리테일'</li><li>'오뚜기 스위트콘 옥수수통조림 340g 스위트콘 340g x 1개 주식회사 로씨네'</li><li>'동서 리치스 홀커널 스위트콘 425g 원터치 옥수수 캔 통조림  주식회사 당장만나'</li></ul>                             |
| 7.0   | <ul><li>'스팸 마일드 25% 라이트 340g 외 스팸 4종 1. 스팸 클래식 200g 주식회사 하포테크'</li><li>'CJ제일제당 스팸 싱글 클래식 80g CJ제일제당 스팸 싱글 25% 라이트 80g 삼영유통'</li><li>'통조림 CJ제일제당 스팸 클래식 200g/햄통조림 ~통조림/캔햄_쿡샵 스위트콘 (태국산) 420g 단비마켓'</li></ul> |
| 2.0   | <ul><li>'샘표 김치찌개용꽁치280g/김치찌개전용꽁치통조림  주식회사 달인식자재'</li><li>'샘표 고등어 원터치 400g  조이텍'</li><li>'통조림 오뚜기 고등어 400g/참치캔 ~150g이상참치_동원 고추참치 150g 모두유통주식회사'</li></ul>                                                    |
| 1.0   | <ul><li>'화풍 양송이 편 2.8Kg  다유몰'</li><li>'디벨라 렌틸스 400g /렌즈콩  (주)푸드올마켓'</li><li>'몬 코코넛밀크 400ml 02_콕_코코넛밀크_400ml 정앤남'</li></ul>                                                                                  |
| 0.0   | <ul><li>'유동 자연산 골뱅이 230g /s/ 번데기 술안주 비빔면 소면 무침 국수 야식 통조림  (주)강남상사'</li><li>'동원에프앤비 동원 자연산 골뱅이 230g  주식회사 진현유통'</li><li>'자연산 골뱅이캔삼포140g  스완인터내셔널'</li></ul>                                                  |
| 5.0   | <ul><li>'동원참치 고추참치 통조림 100g 동원 참치 12종_17.동원 고추 참치 150g (주)다누림글로벌'</li><li>'오뚜기 참치빅캔 살코기 1.88kg  플랜트더퓨처'</li><li>'동원 참치 3kg 대용량 참치캔 업소용 코스트코  태양팜스'</li></ul>                                                |
| 8.0   | <ul><li>'샘표 통조림캔 황도 400g 조림용고등어 400g (주)두배로'</li><li>'동서 리치스 파인애플 슬라이스 836g  (주)푸드팜'</li><li>'동서 리치스 후르츠칵테일 3kg 미동의 제이모리'</li></ul>                                                                         |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9854 |

## 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_fd21")
# Run inference
preds = model("목우촌 뚝심 340g  장보고가")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 8.4489 | 22  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 50                    |
| 6.0   | 50                    |
| 7.0   | 50                    |
| 8.0   | 50                    |

### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch   | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0141  | 1    | 0.4416        | -               |
| 0.7042  | 50   | 0.297         | -               |
| 1.4085  | 100  | 0.1016        | -               |
| 2.1127  | 150  | 0.0599        | -               |
| 2.8169  | 200  | 0.0339        | -               |
| 3.5211  | 250  | 0.0256        | -               |
| 4.2254  | 300  | 0.0235        | -               |
| 4.9296  | 350  | 0.0019        | -               |
| 5.6338  | 400  | 0.0113        | -               |
| 6.3380  | 450  | 0.0002        | -               |
| 7.0423  | 500  | 0.0001        | -               |
| 7.7465  | 550  | 0.0001        | -               |
| 8.4507  | 600  | 0.0001        | -               |
| 9.1549  | 650  | 0.0001        | -               |
| 9.8592  | 700  | 0.0001        | -               |
| 10.5634 | 750  | 0.0001        | -               |
| 11.2676 | 800  | 0.0001        | -               |
| 11.9718 | 850  | 0.0001        | -               |
| 12.6761 | 900  | 0.0001        | -               |
| 13.3803 | 950  | 0.0001        | -               |
| 14.0845 | 1000 | 0.0001        | -               |
| 14.7887 | 1050 | 0.0001        | -               |
| 15.4930 | 1100 | 0.0001        | -               |
| 16.1972 | 1150 | 0.0001        | -               |
| 16.9014 | 1200 | 0.0           | -               |
| 17.6056 | 1250 | 0.0001        | -               |
| 18.3099 | 1300 | 0.0001        | -               |
| 19.0141 | 1350 | 0.0001        | -               |
| 19.7183 | 1400 | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.20.0

## 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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