---
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: 14k 18k 1.5g 꼬임 꽈배기 반지 레이어드 실반지 심플 우정반지 커플링 골든시크릿 14k_화이트골드_1 세건인터내셔널
- text: 은귀걸이+은목걸이 에센스 세트 실버 순은 여자 여성 고급케이스 오엑스골드
- text: 유닉크한느낌 우아한반지 화려한 AAAA 10 11mm 남해 라운드 담수 한복반지진주반지 WHITE 리마106
- text: 종로웨딩밴드 14K 18K 투톤 가드링세트 커플링 결혼반지 18K남자반지_리얼화이트골드(무도금)_18 최실
- text: 로이드 10k 더블라인 7월 탄생석 반지 LRT1952GT 옐로우(YG)_6 동아쇼핑점
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: 0.8263560686728597
      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>'VENTFILLE95 스털링 실버 스타 플래시 스탈 작은 신선한 개 05 heart 베스트C몰'</li><li>'판도라 592313C01 시그니처 ID 파베 뱅글 SIZE2 뮤직 인트 (MUSIC INT)'</li><li>'VENTFILLE95 스털링 실버 스타 플래시 스탈 작은 신선한 개 02 Gold 베스트C몰'</li></ul>                                                                                   |
| 3.0   | <ul><li>'두줄발찌 여자 발찌 두줄 실 남자 JUJIE 316L 스테인레스 스틸 트위스트 로프 체인 YF15692_은 상생COMMERCIAL'</li><li>'[현대백화점]스타일러스 A 허니허니13(14K) 251400187  (주)현대백화점'</li><li>'스네이크발찌 두줄발찌 패션 A Z 필기체 초기 편지 쿠바 캐주얼디자인 컷팅팔찌 Letter E_은 와이컴퍼니'</li></ul>                                                           |
| 4.0   | <ul><li>'(스와로브스키) (E+N+B) 라포드 스왈 세트 Rose gold_Amethys 펀스토리지'</li><li>'실버 아르도 미노스 백 3종SET S02-83  (주)루루골드'</li><li>'45%[골드피아] 골드정품 쥬얼리 24K/18K/14K 100여종 모음전 08) 14K 씬 컷팅 원터치 귀걸이_특대_PG 제메이스'</li></ul>                                                                                 |
| 7.0   | <ul><li>'데님 라피스 캐보션스톤 타원 16x12mm 뒤가 평평한  영진재료'</li><li>'질스튜어트 뉴욕 액세서리 화이트 스팽글장식 미니토트백 JABA4E451WT FREE OC홀딩스'</li><li>'고딕 100 925 스털링 실버 해골 펜던트 유행 펑크 스타일 태국 모자  아이마커'</li></ul>                                                                                                       |
| 2.0   | <ul><li>'[스타일러스](천안아산점)트라이위시D(여)14K _211500038 15 신세계백화점'</li><li>'갤러리아 [HAZZYS ACC] [P.JUBILEE] 블랙 레터링 배색 클러치백 - HJBA7E372  갤러리아백화점'</li><li>'벨라뷰 다이아몬드 반지 0.2ct (F/VS2) 옵션에없는사이즈 (주)미꼬쥬얼리'</li></ul>                                                                                 |
| 1.0   | <ul><li>'[Hei] SWAROVSKI PEARL NECKLACE White (주)더블유컨셉코리아'</li><li>'[Hei]여자)아이들 미연, 태연, 트와이스 지효, 김민주, 송해나착용] swarovski pearl necklace White 신세계몰'</li><li>'윌헬미나 가르시아 I LOVE ME NECKLACE / HRT038-BLACK OS LFmall02'</li></ul>                                                          |
| 0.0   | <ul><li>'[티오유] [silver925] TB014 2 way black ball earrings ivory/FREE 주식회사 서울쇼룸'</li><li>'[🧡 ] 비비안웨스트우드 런던 오알비 싱글 스터드 62010239 / VIVIENNE WESTWOOD LONDON ORB SINGLE STUD GUNMETAL-TONE ONE UK TRADING LTD'</li><li>'[ANNAFLAIR1986] PEARL LONG EARRINGS SILVER (주)더블유컨셉코리아'</li></ul> |
| 5.0   | <ul><li>'수납 정리함 회전식 머리끈 액세서리 곱창 03.아이보리+화이트 행운의 주인공'</li><li>'폴리싱천 은세척천 광택천 실버천  에크미'</li><li>'아크릴 큐브 정사각형50x50x20mm 2302_037  맥스박스'</li></ul>                                                                                                                                         |
| 8.0   | <ul><li>'14K 데일리 트라거스 라블렛 피어싱 귀걸이 14K골드_29.입체큐빅 십자가_바길이 4mm 욜로 컴퍼니(YOLO Company)'</li><li>'딜리셔스 진저쿠키 실버핑크 피어싱 TESSVP13959MPC-4 [0001]기본상품 CJONSTYLE'</li><li>'[비앤비골드] 14K 볼볼 탄생석 3mm 11월 시츄린 링 피어싱 귀걸이 한쪽 JNE26627 14K 옐로우골드 KT알파쇼핑_온라인몰'</li></ul>                                    |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8264 |

## 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_ac13")
# Run inference
preds = model("은귀걸이+은목걸이 에센스 세트 실버 순은 여자 여성 고급케이스 오엑스골드")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 10.3556 | 23  |

| 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    | 0.386         | -               |
| 0.7042  | 50   | 0.3306        | -               |
| 1.4085  | 100  | 0.1325        | -               |
| 2.1127  | 150  | 0.0469        | -               |
| 2.8169  | 200  | 0.0185        | -               |
| 3.5211  | 250  | 0.0014        | -               |
| 4.2254  | 300  | 0.0006        | -               |
| 4.9296  | 350  | 0.0003        | -               |
| 5.6338  | 400  | 0.0002        | -               |
| 6.3380  | 450  | 0.0002        | -               |
| 7.0423  | 500  | 0.0002        | -               |
| 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 | 0.0001        | -               |
| 17.6056 | 1250 | 0.0001        | -               |
| 18.3099 | 1300 | 0.0001        | -               |
| 19.0141 | 1350 | 0.0001        | -               |
| 19.7183 | 1400 | 0.0001        | -               |

### 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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