---
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: 맑은농산 리얼넛츠 베리앤요거트 하루건강견과 20g x 25개입  비트리
- text: 23년 햅쌀 골든퀸3호 수향미 특등급 10kg / 순차출고  상상리허설
- text: 산과들에 원데이오리지널 20g x 50개입 선물세트 동의 제이엠세일즈
- text: 구운아몬드 1kg 견과류  에이케이에스앤디 (주) AK인터넷쇼핑몰
- text: 필리핀 세부 건망고 80g 10개-쫀득한망고 말린망고 말린과일  대신유통
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: 0.894413407821229
      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:** 7 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>'[채울농산] 국산 장수상황버섯(baumii 최상품) 1개월분 (100g) 1개월분 채울농산'</li><li>'명이나물 2kg 산마늘잎 생명이나물 산나물 생채 명이장아찌 강원도 산마늘 명이 장아찌 2kg 토종농장'</li><li>'풀무원 한끼연두부 오리엔탈유자 (118gX2EA)  (주)풀무원'</li></ul>                                                |
| 2.0   | <ul><li>'커클랜드 건 블루베리 567g 몸에 좋은 건과일 샐러드나 베이킹에 활용 코스트코  마인드 트레이드(mind trade)'</li><li>'웰프레쉬 냉동 블루베리 미국산 1kg  배동바지몰'</li><li>'커클랜드 냉동 블루베리 2.27kg 코스트코 아이스박스 요거트 과일 베리  라미의잡화점'</li></ul>                                                |
| 5.0   | <ul><li>'2022년산 국산 서리태 2kg 검은콩 속청 전남 구례산 볶은 서리태가루 1kg 농업회사법인(주)한결유통'</li><li>'국산 서리태 2kg 검은콩 속청 전남 구례산 국산 서리태(특A) 1kg 농업회사법인(주)한결유통'</li><li>'잔다리마을 특허받은 공법으로 로스팅한 검은콩 서리태 볶음콩 250g / 영양 간식  주식회사 패스트뷰'</li></ul>                      |
| 0.0   | <ul><li>'Sol Simple 태양열 건조 망고 6온스(1팩)_파인애플 시이부동'</li><li>'[푸드] KUNNA 쿤나 건망고 75g 3개 부담없이 젤리 망고 마른 과일 태국 간식 사무실 탕비실 건조과일 말린 망고  에스디지컴퍼니'</li><li>'너츠브라더 촉촉한 건망고 200g 건망고 1kg (주)조하'</li></ul>                                            |
| 4.0   | <ul><li>'[카무트] 고대곡물 카무트 쌀 밀 500g  이푸른(주)'</li><li>'23년 국산 현미 쌀눈 2kg  주식회사 건강중심'</li><li>'[예약구매 할인] 저당 파로 800g 이탈리아 고대곡물 바비조아 저당밥 시리즈 특허공법 저항성전분  주식회사 바비조아'</li></ul>                                                                  |
| 1.0   | <ul><li>'맛있는家 너트리 캘리포니아 생아몬드 500g x 2개  (주)씨제이이엔엠'</li><li>'길림양행 탐스팜 쿠키앤크림 아몬드 190g  바이트리스'</li><li>'머거본 커피땅콩 130g 6개/ 견과류 마른안주 주전부리  보마스'</li></ul>                                                                                   |
| 3.0   | <ul><li>'웰루츠 A등급 냉동 블루베리 1kg 냉동과일 웰루츠 냉동 키위 다이스(중국) 1kg 웰루츠'</li><li>'뉴뜨레 냉동 블루베리 홀 1kg+1kg 무가당 세척블루베리 과일 모음 다이스 퓨레 뉴뜨레 냉동 그린키위 1kg x 2봉 주식회사 보금푸드'</li><li>'코스트코 커클랜드 냉동 블루베리 2.27kg / 아이스박스 포장발송 아이스팩 + 드라이아이스 발송 남들과 다르게'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8944 |

## 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_fd5")
# Run inference
preds = model("구운아몬드 1kg 견과류  에이케이에스앤디 (주) AK인터넷쇼핑몰")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 10.0886 | 25  |

| 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                    |

### 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    | 0.4119        | -               |
| 0.9091  | 50   | 0.2564        | -               |
| 1.8182  | 100  | 0.0407        | -               |
| 2.7273  | 150  | 0.0157        | -               |
| 3.6364  | 200  | 0.014         | -               |
| 4.5455  | 250  | 0.0           | -               |
| 5.4545  | 300  | 0.0           | -               |
| 6.3636  | 350  | 0.0           | -               |
| 7.2727  | 400  | 0.0           | -               |
| 8.1818  | 450  | 0.0001        | -               |
| 9.0909  | 500  | 0.0           | -               |
| 10.0    | 550  | 0.0           | -               |
| 10.9091 | 600  | 0.0           | -               |
| 11.8182 | 650  | 0.0           | -               |
| 12.7273 | 700  | 0.0           | -               |
| 13.6364 | 750  | 0.0           | -               |
| 14.5455 | 800  | 0.0           | -               |
| 15.4545 | 850  | 0.0           | -               |
| 16.3636 | 900  | 0.0           | -               |
| 17.2727 | 950  | 0.0           | -               |
| 18.1818 | 1000 | 0.0           | -               |
| 19.0909 | 1050 | 0.0           | -               |
| 20.0    | 1100 | 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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