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: 코스트코 수지스 그릴드 닭가슴살 1.8kg 수비드 페퍼콘 허브 그릴드 닭가슴살 1.8kg (스테디) 리반태닝
- text: 에쓰푸드 전지베이컨(1.9mm 슬라이스) 500g(기름기가 적고 담백한 베이컨)  금정푸드
- text: 849967 동원 퀴진 통등심 돈까스 480g 3봉 외 4종 1)돈까스(통등심) 480g 1)돈까스(통등심) 480g_4)생선커틀렛
    400g_4)생선커틀렛 400g 시드웰쓰파트너스
- text: 돼지 뒷다리살 수육용 제육볶음고기 찌개용 ★핫딜대전★ 한돈 뒷다리살 1kg_보쌈용덩어리 주식회사 삼형제월드
- text: 송이 불닭발 280gX10팩/국내산, 원앙, 닭발, 매운  (주)천지농산
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: 0.6435236614085759
      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                                                                                                                                                                                                             |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7.0   | <ul><li>'남도전통 우리맛 토종순대 천연돈장 1kg 4인분 우리맛 토종순대 1kg+1kg (2개) 주식회사 금호비앤디'</li><li>'코스트코 커클랜드 시그니춰 크럼블스 베이컨 567g  최고의수준'</li><li>'하림 아이로운 닭가슴살 팝콘치킨500g 1봉+1봉  팔레스티'</li></ul>                                            |
| 2.0   | <ul><li>'청정원 안주야 매운곱창볶음 160g 4개  (주) 이카루스'</li><li>'삼치기 쫄여먹는 쫄갈비 300g 1-2인분 물갈비 캠핑요리 음식 밀키트 고기 양념돼지갈비 쫄여먹는 쫄갈비 300g(1~2인분) 삼치기'</li><li>'파티큐 귀족 통돼지바베큐 (5-10인분) 만화고기 캠핑음식 집들이 출장 부천종합버스터미널_1/6상체 주식회사 파티큐'</li></ul> |
| 6.0   | <ul><li>'송화단(화풍60g x10) 8개 식자재 업소용 대용량  일흥상회'</li><li>'오리로스500gx4팩 고추오리불고기500gx1팩 선물용  마이다스'</li><li>'춘천달갈비 국내산 즉석조리식품 안동 순살 찜닭 1kg / 3-4인분  주식회사 에프앤에프커머스'</li></ul>                                                |
| 0.0   | <ul><li>'Espuna 스페인 전통 하몽 초리초슬라이스100g1개jamon  밀도상점'</li><li>'목우촌 버터구이 치킨 봉 500gX2개  팔레스티몰'</li><li>'우리맛 모듬국밥 머리고기+내장 2인분 (440g) 모듬국밥 4pack (800g) 주식회사 금호비앤디'</li></ul>                                              |
| 5.0   | <ul><li>'[호주산] 양등뼈 1kg  cj거성푸드'</li><li>'양의나라 유기농 양고기 양갈비 양꼬치 프렌치렉 숄더랙 캠핑 냉장 냉동  양의 나라'</li><li>'하이마블 프렌치랙 프랜치랙 양갈비 양고기 450g 램 미니 토마호크 프렌치랙  450g (냉동) 주식회사 하이마블'</li></ul>                                          |
| 1.0   | <ul><li>'하림 치킨너겟(Ⅱ) 1kg 텐더스틱 1kg 주식회사 미담'</li><li>'이종하작가 비법매실먹은 춘천닭갈비 올인원세트 3인분 (닭갈비 + 야채+떡+치즈 포함) 통다리살 간장바베큐 4개(1kg) 춘천맛식품'</li><li>'국물닭발 700g 2팩 튤립 숯불 오돌뼈 술안주 혼술 야식 국내산 매운맛 제육볶음 오돌뼈 250g 2팩 주식회사 바르'</li></ul>   |
| 3.0   | <ul><li>'미트홀 부채살 찹스테이크 부채 큐브 스테이크 1kg(200gX5팩) 짜파구리  미트홀'</li><li>'[도착보장] 올반 소불고기 전골세트 (소불고기 4팩 + 전골육수 2팩) 저녁 국 탕 찌개 반찬 간편식 밀키트 소불고기 4팩+전골육수2팩 (주)신세계푸드'</li><li>'에스푸드 바싹 불고기 1kg  주식회사 클릭몰'</li></ul>               |
| 4.0   | <ul><li>'흥생농장 반숙란40구 촉촉한 부드러운 반숙계란  흥생농장'</li><li>'에그트리 특란 90구 HACCP농장직송 날계란  에그트리농장'</li><li>'중국 염장 오리알 야단 372g 유황 찐오리알 6개입  오너트리'</li></ul>                                                                        |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.6435 |

## 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_fd20")
# Run inference
preds = model("송이 불닭발 280gX10팩/국내산, 원앙, 닭발, 매운  (주)천지농산")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 10.0318 | 24  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 19                    |
| 5.0   | 27                    |
| 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0182  | 1    | 0.4004        | -               |
| 0.9091  | 50   | 0.238         | -               |
| 1.8182  | 100  | 0.1002        | -               |
| 2.7273  | 150  | 0.0799        | -               |
| 3.6364  | 200  | 0.063         | -               |
| 4.5455  | 250  | 0.0301        | -               |
| 5.4545  | 300  | 0.0261        | -               |
| 6.3636  | 350  | 0.0128        | -               |
| 7.2727  | 400  | 0.0054        | -               |
| 8.1818  | 450  | 0.008         | -               |
| 9.0909  | 500  | 0.004         | -               |
| 10.0    | 550  | 0.0001        | -               |
| 10.9091 | 600  | 0.002         | -               |
| 11.8182 | 650  | 0.002         | -               |
| 12.7273 | 700  | 0.0058        | -               |
| 13.6364 | 750  | 0.0039        | -               |
| 14.5455 | 800  | 0.0016        | -               |
| 15.4545 | 850  | 0.0001        | -               |
| 16.3636 | 900  | 0.0001        | -               |
| 17.2727 | 950  | 0.0001        | -               |
| 18.1818 | 1000 | 0.0001        | -               |
| 19.0909 | 1050 | 0.0           | -               |
| 20.0    | 1100 | 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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