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: Pulsar X2V2 미니 무선 게이밍 마우스 (블랙)  와이에스비투비
- text: TOSHIBA B-EX4T2 바코드프린터 산업용프린터 라벨프린터 203DPI_USB ㈜비티에스홀딩스
- text: '[당일출고]삼성전자 SL-J1680 컬러잉크젯 복합기 인쇄+복사+스캔 [정품잉크포함]  제일프린텍'
- text: 지클릭커 슈퍼히어로 SPK100 저소음 유선 무선 블루투스 레인보우 백라이트 기계식 게임용 키보드 (레트로 레드)  (주)피씨베이스
- text: NIIMBOT 님봇 D110 라벨기 휴대용 라벨프린터 라벨1롤포함  빅마운트앤컴퍼니
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: 0.8548111301103685
      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                                                                                                                                                                                                                                     |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7     | <ul><li>'와콤 CTL-472 웹툰 입문용 타블렛 펜 온라인강의  주식회사 지디스엠알오'</li><li>'와콤 타블렛 CTL-4100 와콤인튜어스 웹툰  (주)코티니'</li><li>'와콤 신티크16 DTK-1660  케이에이씨앤씨'</li></ul>                                                                                              |
| 1     | <ul><li>'브라더공식판매대리점 DCP-T426W 무한잉크복합기 인쇄 복사 스캔 무선 AS연장  (주)대명아이티'</li><li>'교세라 ECOSYS M5521cdn 컬러레이저복합기 정품토너포함  한라테크'</li><li>'DCP-T720DW 브라더정품 무한잉크복합기 인쇄 복사 스캔 자동양면인쇄  (주)진전산시스템'</li></ul>                                                |
| 4     | <ul><li>'로지텍 코리아 미니멀 무선 일루미네이티드 키보드 MX KEYS MINI 블랙(그라파이트)  주식회사 자강정보통신'</li><li>'앱코 K660 축교환 완전방수 게이밍 카일광축 레인보우LED 블랙,리니어  에스티에스컴퍼니'</li><li>'ABKO HACKER K523 기계식 축교환 LED 키패드  주식회사 브라보세컨즈'</li></ul>                                      |
| 2     | <ul><li>'브라더 TN-2380 정품토너 2.6K HL L2365DW HL L2360dn MFC L2700D MFC L2700DW  주식회사 휴먼아이티'</li><li>'삼성전자정품 폐토너통 CLT-W406/ C510W/ C513W/ C563W/ C563FW  엘케이솔루션'</li><li>'(HP) No.680 정품 F6V27AA 검정 정품잉크 검정 총1개만구매(2개이상주문시발송안됨) 밀알시스템'</li></ul> |
| 6     | <ul><li>'와콤원 펜 CP91300B2Z 삼성갤럭시탭,갤럭시노트,오닉스 호환 펜  '</li><li>'드로잉장갑 와콤 신티크 XP-PEN 휴이온 액정타블렛 아이패드 태블릿 터치오류방지  '</li><li>'드로잉장갑 와콤 신티크 XP-PEN 휴이온 액정타블렛 아이패드 태블릿 터치오류방지  '</li></ul>                                                             |
| 8     | <ul><li>'◆◆ 정품 샘플테이프 + ◆◆ 브라더 正品 이름 라벨스티커기계 PT-P900W QR코드 wifi ◀正品▶ PT-P900W 탑정보기술'</li><li>'가제트 3D펜 GP3000+5M PLA 필라멘트 세트(24색)  (주)위드피플즈'</li><li>'인스탁스 와이드 링크 포토프린터 모카 그레이(+아크릴액자) 한국후지필름 (주)'</li></ul>                                   |
| 3     | <ul><li>'엡손 DS-30000, 양면 스캐너 A3  주식회사 케이에스샵'</li><li>'엡손 WorkForce DS-50000  (주)테드이십일'</li><li>'엡손스캐너 ES-580WMLP 미니멀 라이프 패키지(ES-580W+재단기+롤러)북스캐너  (주)에이엔에이코리아'</li></ul>                                                                     |
| 5     | <ul><li>'로지텍 MK295 SILENT WIRELESS COMBO (화이트)  (주)아토닉스'</li><li>'로지텍 MK275 영문자판 병행수입  제이제이 인터내셔널'</li><li>'로지텍코리아 시그니처 MK650 무선 합본 (그래파이트)  주식회사 지엠샤이'</li></ul>                                                                            |
| 0     | <ul><li>'ROCCAT KONE PRO AIR (블랙)  (주)디아씨앤씨'</li><li>'[Logitech]로지텍 Trackman Marble USB 마우스 트랙맨 트랙볼 마블 마우스 벌크 /택배/병행/ 당일출고 Trackman Marble USB 허브포스트'</li><li>'로지텍 G402 Hyperion Fury  (주)케이엘시스템'</li></ul>                                  |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8548 |

## 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_el18")
# Run inference
preds = model("Pulsar X2V2 미니 무선 게이밍 마우스 (블랙)  와이에스비투비")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 10.5569 | 27  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 50                    |
| 2     | 50                    |
| 3     | 50                    |
| 4     | 50                    |
| 5     | 50                    |
| 6     | 13                    |
| 7     | 50                    |
| 8     | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0154  | 1    | 0.4961        | -               |
| 0.7692  | 50   | 0.1923        | -               |
| 1.5385  | 100  | 0.0615        | -               |
| 2.3077  | 150  | 0.0532        | -               |
| 3.0769  | 200  | 0.0513        | -               |
| 3.8462  | 250  | 0.0283        | -               |
| 4.6154  | 300  | 0.0313        | -               |
| 5.3846  | 350  | 0.0258        | -               |
| 6.1538  | 400  | 0.0174        | -               |
| 6.9231  | 450  | 0.0053        | -               |
| 7.6923  | 500  | 0.0021        | -               |
| 8.4615  | 550  | 0.0039        | -               |
| 9.2308  | 600  | 0.0059        | -               |
| 10.0    | 650  | 0.0001        | -               |
| 10.7692 | 700  | 0.0001        | -               |
| 11.5385 | 750  | 0.0001        | -               |
| 12.3077 | 800  | 0.0001        | -               |
| 13.0769 | 850  | 0.0001        | -               |
| 13.8462 | 900  | 0.0           | -               |
| 14.6154 | 950  | 0.0001        | -               |
| 15.3846 | 1000 | 0.0           | -               |
| 16.1538 | 1050 | 0.0           | -               |
| 16.9231 | 1100 | 0.0           | -               |
| 17.6923 | 1150 | 0.0           | -               |
| 18.4615 | 1200 | 0.0           | -               |
| 19.2308 | 1250 | 0.0           | -               |
| 20.0    | 1300 | 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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