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mini1013
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master_cate_fd8

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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master_cate_fd8
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---
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: '[CJ](신세계 의정부점) 비비고 누룽지닭다리삼계탕 550g  주식회사 에스에스지닷컴'
- text: 고객 후기로 만들어진 밀푀유 쇼유 나베 밀키트 (2인) 2월27일(화) 주식회사 아내의쉐프
- text: 룸서비스 홈파티음식 케이터링 생일팩15종 서울출장뷔페 집들이 배달 돌잔치상 손님초대요리 3.룸서비스파티팩15종(고급박스용기)_6월_19일
    주식회사 룸서비스딜리버리
- text: 홈파티음식 케이터링 생일팩15종 인천출장뷔페 집들이 배달 돌잔치상 소규모 손님초대요리 01.룸서비스 생일팩 15종_1월_20일 (주)셀루체
- text: 홈파티음식 케이터링 생일팩15종 인천출장뷔페 집들이 배달 돌잔치상 소규모 손님초대요리 3.룸서비스파티팩15종(고급박스용기)_4월_19일
    (주)셀루체
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: 0.9173203883495146
      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>'듬뿍담은 안동식 순살 찜닭 밀키트 711g  주식회사 프레시지'</li><li>'우렁쌈장 (2인분) 밀키트 쿠킹박스 우렁살 2개 추가(100g) 농업회사법인 주식회사 아임셰프'</li><li>'홍수계 매콤 당면듬뿍 순살 찜닭 850g 2인분 냉동 밀키트  셀린'</li></ul>                                                       |
| 1.0   | <ul><li>'[마이셰프] 찹스테이크(1인)(프리미엄박스)  주식회사 마이셰프'</li><li>'소문난 청정원 호밍스 마포식 돼지양념구이 210g 정원이샵 홈파티음식 캠핑요리 맥주안주 야식 간편식 홈캉스 풍미업 모에모에큥  에스더블유디자인'</li><li>'심쿡 슈페리어 연어 스테이크 455g 밀키트 쿠킹박스  인영이네'</li></ul>                             |
| 5.0   | <ul><li>'[골든벨통상](신세계센텀점)골든벨 심영순쇠고기국간장250ml  주식회사 에스에스지닷컴'</li><li>'[CJ](신세계센텀점) 튀김가루 1kg 1개  주식회사 에스에스지닷컴'</li><li>'(치즈박스)쉐프가 만든 캠핑 와인안주세트(고기 포함 안됨 X) 캘리포니아 키친 실속형(-2500)_11/20 월요일 캘리포니아키친(california kitchen)'</li></ul> |
| 4.0   | <ul><li>'소고기 버섯 잡채 (2인분)  주식회사 프레시지'</li><li>'야식메뉴 청정원 호밍스 춘천식 치즈닭갈비 220g 저녁반찬 자취요리  규비에스코퍼레이션'</li><li>'하림 궁중 국물 닭떡볶이 700g 밀키트  바이라이프'</li></ul>                                                                           |
| 0.0   | <ul><li>'올바르고반듯한 떡볶이 원조시장 떡볶이 (냉동), 575g, 1개  하누코지'</li><li>'두끼 즉석떡볶이 560G 아이스박스 포장/선택 인터드림'</li><li>'두끼 매콤 고소 로제떡볶이 3팩 450g  주식회사 다른'</li></ul>                                                                            |
| 3.0   | <ul><li>'[강원팜] 홈스랑 곤드레감자밥 쉽게만들기6인분  강원팜'</li><li>'마이셰프 즉석밥 일상정원 명란 솥밥 (냉동), 233g, 1개  하누코지'</li><li>'여름철 보양식 전복죽 200g 1팩  더블제이doubleJ'</li></ul>                                                                            |
| 7.0   | <ul><li>'우정옥 여주 한우 특곰탕 1kg(2인분) 한우사골곰탕 도가니탕 1000g(약 2인분) 주식회사 우정옥'</li><li>'25년 전통 수복 얼큰 감자탕 [기본팩] 캠핑요리 밀키트 우거지 리얼감자탕 알뜰팩(라면사리X / 야채X) 수복얼큰감자탕'</li><li>'인천 정통 맛집 장금수 스페셜 부대전골 부대찌개 2-3인분 술안주 캠핑 집들이 밀키트  더렌'</li></ul>     |
| 2.0   | <ul><li>'1분완성 개별포장 매콤 알싸 비빔 막국수 막국수 1팩 (주)데이지웰푸드'</li><li>'동원 면발의신 얼큰칼국수 268g 엄마손맛 육수 쉬운요리 감칠맛 자취 풍미 레시피 소스  인영'</li><li>'샐러드미인 쉐프엠 미트파스타 230g  주식회사 엠디에스코리아'</li></ul>                                                     |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9173 |

## 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_fd8")
# Run inference
preds = model("[CJ](신세계 의정부점) 비비고 누룽지닭다리삼계탕 550g  주식회사 에스에스지닷컴")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.3575 | 20  |

| 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    | 0.4347        | -               |
| 0.7937  | 50   | 0.2865        | -               |
| 1.5873  | 100  | 0.0903        | -               |
| 2.3810  | 150  | 0.0636        | -               |
| 3.1746  | 200  | 0.0401        | -               |
| 3.9683  | 250  | 0.003         | -               |
| 4.7619  | 300  | 0.0016        | -               |
| 5.5556  | 350  | 0.0017        | -               |
| 6.3492  | 400  | 0.0025        | -               |
| 7.1429  | 450  | 0.0007        | -               |
| 7.9365  | 500  | 0.0001        | -               |
| 8.7302  | 550  | 0.0001        | -               |
| 9.5238  | 600  | 0.0002        | -               |
| 10.3175 | 650  | 0.0001        | -               |
| 11.1111 | 700  | 0.0008        | -               |
| 11.9048 | 750  | 0.0001        | -               |
| 12.6984 | 800  | 0.0001        | -               |
| 13.4921 | 850  | 0.0           | -               |
| 14.2857 | 900  | 0.0001        | -               |
| 15.0794 | 950  | 0.0           | -               |
| 15.8730 | 1000 | 0.0           | -               |
| 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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