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mini1013
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master_cate_fd9

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
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master_cate_fd9
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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: '[본죽]5첩반상 5종(진미채+멸치+연근+콩자반+깻잎) 5팩+5팩 외 밑반찬 5종 5팩+5팩 메가글로벌001'
- text: 싸고 맛있고 영양까지 풍부한 110가지 우리집반찬/우리홈메이드푸드 도토리묵/양념 홈메이드 푸드
- text: 샘표 쓱쓱싹싹밥도둑 반찬 9봉 골라담기 / 장조림 오징어채볶음 멸치볶음 2. 고추장 멸치볶음 3봉_4. 쇠고기 장조림 3봉_6. 돼지고기
    장조림 3봉 샘표식품 주식회사
- text: 본죽 쇠고기 장조림 170g x 4  마이엘(Maiel)
- text: 국산 고추장멸치볶음 500g 조림 반찬 국산 오복채 1kg 사계절반찬
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: 0.9101876675603218
      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>'본죽 미니장조림 2박스 70gx5개입x2  셜크'</li><li>'[본죽]쇠고기 장조림 300g (냉장 소고기 반찬 점심 저녁 도시락 어린이 아기반찬)  순수본 주식회사'</li><li>'본죽 쇠고기 장조림 170g x 4 5. 비비고 육개장 500g x 5개 감성주머니'</li></ul>                               |
| 1.0   | <ul><li>'일가집 일미 쫄깃 치자 단무지 1kg 두부 날치알 피클 일가집 일미 고추지 1kg 고추절임 고추장아찌 머치바잉'</li><li>'일가집 일미 쫄깃 치자 단무지 1kg 두부 날치알 피클 일가집 일미 깐마늘 1kg 양파 다진마늘 청양 머치바잉'</li><li>'참 맛좋은 하진 반달 단무지 2.5kg  농업회사법인 봉농주식회사'</li></ul>  |
| 5.0   | <ul><li>'진 명이나물(실속형) 10kg 대용량 업소용 식당 반찬 장아찌 05 유림 명이나물 10kg (유) 협동맛사랑식품'</li><li>'단풍콩잎 500g 양념 장아찌 국내제조 콩잎김치 삭힌 국산 갈치속젓 500g 사계절반찬'</li><li>'군산 울외장아찌 2kg 나라즈케 나라스케 술지게미 2.무 장아찌 2kg 주식회사 백년부엌'</li></ul> |
| 2.0   | <ul><li>'마늘쫑무침 4kg 대용량 식당 업소용 반찬 무침 장아찌  (유) 협동맛사랑식품'</li><li>'[서울,성남 ] 푸릇푸릇 시금치무침 300g [암사 우리집반찬]  주식회사 프레시멘토'</li><li>'[주문폭주] 농가살리기 30년 전통 통영할매 원조 생굴무침 330g 생굴무침 330g 1통 주식회사 청년농부들'</li></ul>         |
| 8.0   | <ul><li>'일본식 반찬대용 츠쿠다니 김조림 180g  서울타임즈'</li><li>'오뚜기 고등어갈치조림양념120g  제이디(JD)'</li><li>'청우식품 이음식 스지사태조림 200g  푸드뱅크(주)'</li></ul>                                                                            |
| 4.0   | <ul><li>'[종가집]종가집 오징어채볶음 60g  에스케이스토아주식회사'</li><li>'[반찬가게 찬장]신선한재료 당일제조 배송 고사리볶음 가정식 반찬 집밥 나물/무침/볶음 배달 밑반찬_건파래무침 주식회사 찬장에프에스대전'</li><li>'청정원 종가집 견과류 멸치볶음 60G  조은마켓'</li></ul>                            |
| 7.0   | <ul><li>'종가집 옛맛 무말랭이 1kg x 2개  더빈(THE BIN)'</li><li>'반찬단지 마늘쫑무침 1kg 아삭 마늘장아찌 반찬거리  와이엘플래닛'</li><li>'가을무를 말려 쫄깃하고 달큰한 국산 무말랭이 1kg 1. 국산 무말랭이 1kg 주식회사 태극인 농업회사법인'</li></ul>                                |
| 0.0   | <ul><li>'씨제이 비비고 오징어채 볶음 55g 아이스박스 포장 (주)씨티케이이비전코리아'</li><li>'매운 고추부각 튀각 30g 6봉 티각태각 속초 명품 특산물 김부각30g 6봉 엠앤엠컴퍼니'</li><li>'대구 반고개 무침회 똘똘이식당 납작만두 오징어 회무침 캠핑 밀키트 무침회세트(중)_보통맛 대구 똘똘이 무침회'</li></ul>         |
| 3.0   | <ul><li>'미자언니네 밑반찬 하얀콩강정 120g 1팩 미자언니네  하얀콩강정 에센셜키친'</li><li>'[메인반찬 국 찌개 김치 세트] 건강한 반찬 이기는면역찬 메인반찬_계란말이 이기는면역찬(서초점)'</li><li>'[본죽] 밑반찬 5종 세트(진미채볶음 멸치볶음 깻잎무침 무말랭이 궁채절임)  메가글로벌001'</li></ul>              |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9102 |

## 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_fd9")
# Run inference
preds = model("본죽 쇠고기 장조림 170g x 4  마이엘(Maiel)")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 10.1981 | 21  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 42                    |
| 2.0   | 22                    |
| 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0154  | 1    | 0.4845        | -               |
| 0.7692  | 50   | 0.2975        | -               |
| 1.5385  | 100  | 0.0992        | -               |
| 2.3077  | 150  | 0.0418        | -               |
| 3.0769  | 200  | 0.0246        | -               |
| 3.8462  | 250  | 0.0358        | -               |
| 4.6154  | 300  | 0.0185        | -               |
| 5.3846  | 350  | 0.0123        | -               |
| 6.1538  | 400  | 0.0121        | -               |
| 6.9231  | 450  | 0.0008        | -               |
| 7.6923  | 500  | 0.0003        | -               |
| 8.4615  | 550  | 0.0002        | -               |
| 9.2308  | 600  | 0.0001        | -               |
| 10.0    | 650  | 0.0001        | -               |
| 10.7692 | 700  | 0.0001        | -               |
| 11.5385 | 750  | 0.0002        | -               |
| 12.3077 | 800  | 0.0001        | -               |
| 13.0769 | 850  | 0.0001        | -               |
| 13.8462 | 900  | 0.0001        | -               |
| 14.6154 | 950  | 0.0001        | -               |
| 15.3846 | 1000 | 0.0001        | -               |
| 16.1538 | 1050 | 0.0001        | -               |
| 16.9231 | 1100 | 0.0001        | -               |
| 17.6923 | 1150 | 0.0001        | -               |
| 18.4615 | 1200 | 0.0001        | -               |
| 19.2308 | 1250 | 0.0001        | -               |
| 20.0    | 1300 | 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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