README.md

---
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: (시흥점)루이까또즈 여성 3단 반지갑 SP3HT03IV 아이보리_ONE SIZE 신세계프리미엄아울렛
- text: 닥스 악세서리 남성 22FW populet 로고패턴 소가죽 반지갑 WBWA2F729BK  정품(Best Quality)스토어
- text: '베노베로 (23FW) 알렉스 소프트 엠보 소가죽 미니중지갑 BJF1ACP1201K1-BS 블랙(선물아님) '
- text: '[갤러리아] [헤지스ACC] HIHO2F602G2 [LEENA] 그레이 배색 가죽 목걸이카드홀더(한화갤러리아㈜ 센터시티)  한화갤러리아(주)'
- text: '[롯데백화점]라코스테 24SS (여성) 데일리 라이프스타일 지퍼 반지갑 [NF4375D54G 000 YDP]  롯데백화점_'
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: 0.7924514420247204
      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:** 8 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>'해킹방지 카본 카드지갑 RFID 도난방지 자석오토지갑 블랙 화인트레이드'</li><li>'[라코스테](천안아산점)더 블렌드 포켓 오거나이저(NH4134L54GH45)  신세계백화점'</li><li>'닥스_핸드백 (선물포장)(DAKS X DISNEY) 미키마우스 가죽배색 체크 여성 카드  롯데백화점2관'</li></ul>                                                          |
| 1.0   | <ul><li>'이케아 KNOLIG 크뇔리그 동전지갑 소품 가방 주머니 참 인테리어 색상_옐로우 호랑이스토어5'</li><li>'레오파드 미니 동전지갑 캐리어파우치  폰토스(Pontos)'</li><li>'[비비안웨스트우드][비비안 웨스트우드] 조르단 더블 프레임 동전지갑 52020041 L001J N403(김해점) ONE SIZE 신세계백화점'</li></ul>                                          |
| 5.0   | <ul><li>'BEANPOLE] 빈폴 ACC 스트랩 파우치/카드 SET 블랙/핑크(BE04A4W995) 블랙 메가 세일'</li><li>'지갑& 벨트01G1295Z8K외5종/피에르가르뎅_핸드백 01G1295Z8K 롯데쇼핑(주)'</li><li>'[빈폴 ACC] 스트랩 파우치/카드 SET 블랙 (BE04A4W995) 블랙_one size 윈아이'</li></ul>                                          |
| 4.0   | <ul><li>'[헤지스ACC]HJHO3F332W2/[23FW] 브라운 로고패턴 가죽 키링  에이케이에스앤디 (주) AK인터넷쇼핑몰'</li><li>'[롯데백화점]닥스ACC [선물포장/쇼핑백동봉] 블랙 로고패턴 가죽 키링 DBHO4E138  롯데백화점_'</li><li>'[선물포장] HJHO3E281BK_남성 블랙 퍼피로고 체크배색 키링/헤지스ACC  롯데쇼핑(주)'</li></ul>                              |
| 0.0   | <ul><li>'타미힐피거 타미힐피거 남성반지갑 31TL22X046 블랙 네이비 네이비 SK스토아모바일'</li><li>'[선물포장] DBWA3F717W3 브라운 악어가죽/닥스ACC  롯데쇼핑(주)'</li><li>'[헤지스 액세서리] [24SS] HJWA4E906BK Online 한정판BASIC 블랙 솔리드 퍼피로고 소 XXX '</li></ul>                                                  |
| 3.0   | <ul><li>'여성반지갑 SL3AL04BL/루이까또즈 BLACK 롯데쇼핑(주)'</li><li>'MINI POCKET - BLACK  주식회사 이코컴퍼니'</li><li>'[롯데백화점]닥스ACC [선물포장/쇼핑백동봉]브라운 체크 가죽 핸드폰케이스 DCHO2F328W2  롯데백화점_'</li></ul>                                                                             |
| 7.0   | <ul><li>'동지갑 베트남 환전 통장 여행 슬림 파우치 다낭 해외 지퍼 여권 03. 블랙 동쯔몰'</li><li>'도장 가방 인감 스탬프 케이스 수납 문서 보관 통장 V번 인감 수납가방 홍마켓(hong)'</li><li>'여행용 여권 파우치 목걸이 수납 휴대용 보호커버 블루 나이스쇼핑'</li></ul>                                                                          |
| 2.0   | <ul><li>'[갤러리아] 8059461 MS CHASE GC9 B2871 ONE SIZE 한화갤러리아(주)'</li><li>'국내발송 MATIN KIM 마땡킴 GLOSSY CAMP WALLET IN WHITE MK2311WL001M0WH FREE 말로스'</li><li>'[헤지스](신세계본점)[HAZZYS ACC] [GOLDEN LANE] 블랙 로고패턴 소가죽 반지갑 HJWA1F562BK  주식회사 에스에스지닷컴'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.7925 |

## 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_ac14")
# Run inference
preds = model("(시흥점)루이까또즈 여성 3단 반지갑 SP3HT03IV 아이보리_ONE SIZE 신세계프리미엄아울렛")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.21   | 19  |

| 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                    |

### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0159  | 1    | 0.3853        | -               |
| 0.7937  | 50   | 0.2743        | -               |
| 1.5873  | 100  | 0.1039        | -               |
| 2.3810  | 150  | 0.0564        | -               |
| 3.1746  | 200  | 0.0306        | -               |
| 3.9683  | 250  | 0.0124        | -               |
| 4.7619  | 300  | 0.0146        | -               |
| 5.5556  | 350  | 0.0008        | -               |
| 6.3492  | 400  | 0.0007        | -               |
| 7.1429  | 450  | 0.0001        | -               |
| 7.9365  | 500  | 0.0001        | -               |
| 8.7302  | 550  | 0.0001        | -               |
| 9.5238  | 600  | 0.0001        | -               |
| 10.3175 | 650  | 0.0001        | -               |
| 11.1111 | 700  | 0.0001        | -               |
| 11.9048 | 750  | 0.0001        | -               |
| 12.6984 | 800  | 0.0001        | -               |
| 13.4921 | 850  | 0.0001        | -               |
| 14.2857 | 900  | 0.0001        | -               |
| 15.0794 | 950  | 0.0           | -               |
| 15.8730 | 1000 | 0.0001        | -               |
| 16.6667 | 1050 | 0.0           | -               |
| 17.4603 | 1100 | 0.0           | -               |
| 18.2540 | 1150 | 0.0           | -               |
| 19.0476 | 1200 | 0.0           | -               |
| 19.8413 | 1250 | 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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