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: 지푸드박스 제이엔제이트레이드 코코엘 유기농 엑스트라버진 코코넛오일 필리핀산 415ml  헬시푸드몰
- text: CJ 백설 2통 이상 구매시 할인 쿠폰 콩기름 식용유 대두유 18L 이츠웰 해표 오뚜기 롯데 식용유 말통 전국 최저가판매 식용유_오뚜기식용유
    주식회사 황금알에프앤오
- text: 올리타리아 엑스트라버진 올리브오일 1L  카비스
- text: 커클랜드 시그니춰 카놀라유 오일 2.83L 커클랜드 카놀라유2.83L 베이비파크
- text: 해표)고추맛기름 1.8L  에스엠(SM)식자재도매센터
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: 0.9926900584795322
      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>'사조해표 해표 고급유 2호 선물세트  풀문'</li><li>'CJ 백설 프리미엄 23호  형제종합물류'</li><li>'노브랜드 카놀라유 1L 노브랜드 카놀라유2L 주식회사 유일글로벌'</li></ul>                                                                             |
| 3.0   | <ul><li>'오타비오 아보카도오일 2L 이탈리아 코스트코  포시즌'</li><li>'건강한오늘 아보카도오일 500ml 건강한오늘 아보카도오일 500ml 잇츠설렘'</li><li>'아보퍼시픽 아보카도오일 1L 코스트코 1021670  굿데이'</li></ul>                                                     |
| 4.0   | <ul><li>'만능 올리브유 900ml 청정원 가을 식재료 추석 설날 제사 드레싱 샐러드 파스타  모두감동해'</li><li>'CJ제일제당 백설 압착 올리브유 900ml  준스토리'</li><li>'오로바일렌 엑스트라버진 올리브오일 아르베키나 500ml 500ml (주)운우'</li></ul>                                  |
| 7.0   | <ul><li>'사조 해표 포도씨유 250ML  주식회사 킴벌리마스타'</li><li>'오뚜기 프레스코 포도씨유 900ml  주식회사 삼부'</li><li>'대상 청정원 포도씨유 900ml  주식회사 당장만나'</li></ul>                                                                        |
| 2.0   | <ul><li>'국산 저온압착 들기름 300ml 국내산 아기들기름 저온압착 저온들기름 300ml 농부창고 영농조합법인'</li><li>'미식상회 생들기름 대용량 350ml  에프유니마켓'</li><li>'오뚜기 향긋한 들기름 160ml 1개  (주)하우'</li></ul>                                               |
| 1.0   | <ul><li>'대용량 업소용 식용유 해표 콩 식용유 18L 선택04)오뚜기 콩 식용유 18L 소유앳홈(SO:YOU@Home)'</li><li>'CJ 백설 식용유 1.8L 해표 식용유 1.8L 주식회사 경일종합식품 케이마트몰'</li><li>'CJ 해피스푼 콩식용유 18L 업소용 대용량 저가 식용유 광주 말통  주식회사 케이제이플러스'</li></ul> |
| 0.0   | <ul><li>'캘리포니아골드뉴트리션 슈퍼푸드 오가닉 엑스트라 버진 코코넛 오일 473ml 액상 코코넛기름  에스지샵(SGshop)'</li><li>'참미정 파기름 1.8L 대파 맛기름 참미정 마늘기름 1.8L 주식회사 팜'</li><li>'시아스 불맛기름 화유 500ml 시아스 불맛 고추기름 500ml (주) 식자재민족'</li></ul>        |
| 5.0   | <ul><li>'50년전통 대현상회 저온압착 참기름 350ml 돌려따는 BIG 아빠의주스 양배추사과즙 180 네오카트'</li><li>'오뚜기 고소한 참기름 450ml 오뚜기 고소한 참기름 320ml(병) 삼영유통'</li><li>'국산 저온압착 참기름 180ml 선물세트 이삭방앗간 당일착유 국산 저온압착 참기름_250ml 이삭방앗간'</li></ul> |
| 8.0   | <ul><li>'백설 해바라기씨유 500ml 당일 출발  (주) 바쿰'</li><li>'사조해표 해바라기유 500ml 1개  (주)해피상사'</li><li>'사조 해표 해바라기유 500ml (유통기한 24.01까지) ★유통기한임박특가(24년1월까지) 주식회사 킴벌리마스타'</li></ul>                                     |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9927 |

## 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_fd12")
# Run inference
preds = model("올리타리아 엑스트라버진 올리브오일 1L  카비스")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 8.5356 | 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    | 0.4844        | -               |
| 0.7042  | 50   | 0.3408        | -               |
| 1.4085  | 100  | 0.0769        | -               |
| 2.1127  | 150  | 0.0298        | -               |
| 2.8169  | 200  | 0.023         | -               |
| 3.5211  | 250  | 0.0251        | -               |
| 4.2254  | 300  | 0.0291        | -               |
| 4.9296  | 350  | 0.0156        | -               |
| 5.6338  | 400  | 0.0137        | -               |
| 6.3380  | 450  | 0.0029        | -               |
| 7.0423  | 500  | 0.0001        | -               |
| 7.7465  | 550  | 0.0001        | -               |
| 8.4507  | 600  | 0.0001        | -               |
| 9.1549  | 650  | 0.0           | -               |
| 9.8592  | 700  | 0.0           | -               |
| 10.5634 | 750  | 0.0           | -               |
| 11.2676 | 800  | 0.0           | -               |
| 11.9718 | 850  | 0.0           | -               |
| 12.6761 | 900  | 0.0           | -               |
| 13.3803 | 950  | 0.0           | -               |
| 14.0845 | 1000 | 0.0           | -               |
| 14.7887 | 1050 | 0.0           | -               |
| 15.4930 | 1100 | 0.0           | -               |
| 16.1972 | 1150 | 0.0           | -               |
| 16.9014 | 1200 | 0.0           | -               |
| 17.6056 | 1250 | 0.0           | -               |
| 18.3099 | 1300 | 0.0           | -               |
| 19.0141 | 1350 | 0.0           | -               |
| 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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