---
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: '[바다원] 깨끗한 돌김자반볶음 오리지널 40g x 5봉  (주)씨제이이엔엠'
- text: 쭈꾸미사령부 매운맛 300g 3개 불타는 매운맛 원츄쟈챠
- text: 냉동 새우 튀김 300g 6미 10미 대용량 업소용 빵가루 왕새우튀김 코코넛쉬림프 360g (30미) 주식회사 더꽃게
- text: 잇투헤븐 팔당 불 오징어 매운 오징어 볶음 400g 쭈꾸미도사 쭈꾸미볶음 01.팔당불오징어400g 1팩 (주)잇투헤븐
- text: CJ 명가김 파래김 4g 16입  트릴리어네어스
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: 0.8689361702127659
      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:** 6 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                                                                                                                                                                                                              |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2.0   | <ul><li>'훈제연어(통) 약1.1kg 냉동연어 필렛 슬라이스 칠레산 HACCP 국내가공 화이트베어 화이트베어 훈제연어슬라이스 ±1.3kg 주식회사 셀피'</li><li>'안동간고등어 80g 10팩(5마리) 동의합니다_80g 10팩(5마리) 델리아마켓'</li><li>'제주 국내산 손질 고등어 2KG 한팩150g이상 11-12팩 3KG(16-19팩) 효명가'</li></ul> |
| 1.0   | <ul><li>'동원F&B 양반 김치맛 김부각 50g 1개 동원F&B 양반 김치맛 김부각 50g 1개 다팔아스토어'</li><li>'오뚜기 옛날 자른미역 50G  대성상사'</li><li>'환길산업 섬마을 해초샐러드 냉동 해초무침 2kg  제루통상'</li></ul>                                                                 |
| 0.0   | <ul><li>'Fish Tree 국물용멸치 1.3kg  케이원'</li><li>'Fish Tree 국물용 볶음용 멸치 1.3kg 1kg 뼈건강 깊은맛 육수 대멸치 좋은식감 국물용 멸치 1.3kg 유라너스'</li><li>'Fish Tree 국물용 멸치 1.3kg  이숍'</li></ul>                                                    |
| 3.0   | <ul><li>'랭킹수산 장어구이 혼합 140gx20팩(데리야끼10매콤10) -인증  제이원무역'</li><li>'올반 대왕 오징어튀김 400g  나라유통'</li><li>'바다愛한끼 이원일 연평도 꽃게 해물탕 760g 소스포함 2팩  (주)티알엔'</li></ul>                                                                 |
| 5.0   | <ul><li>'날치알 동림 담홍 레드 800G [800G][동림]날치알(골드)(팩) 주식회사 명품씨푸드'</li><li>'날치알 동림 담홍 레드 800G [800G][동림]날치알(레드)(팩) 주식회사 명품씨푸드'</li><li>'날치알 동림 담홍 레드 800G [800gG[코아]날치알[골드] 주식회사 명품씨푸드'</li></ul>                            |
| 4.0   | <ul><li>'명인오가네 연어장 250g  명인오가네몰'</li><li>'[나브연] 수제 간장 연어장 750g 덜짜게 주희종'</li><li>'[나브연] 수제 간장 연어장 500g 보통 주희종'</li></ul>                                                                                               |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8689 |

## 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_fd11")
# Run inference
preds = model("CJ 명가김 파래김 4g 16입  트릴리어네어스")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.1164 | 23  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 25                    |

### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0233  | 1    | 0.4609        | -               |
| 1.1628  | 50   | 0.2116        | -               |
| 2.3256  | 100  | 0.0876        | -               |
| 3.4884  | 150  | 0.0442        | -               |
| 4.6512  | 200  | 0.0254        | -               |
| 5.8140  | 250  | 0.0133        | -               |
| 6.9767  | 300  | 0.0252        | -               |
| 8.1395  | 350  | 0.0176        | -               |
| 9.3023  | 400  | 0.0116        | -               |
| 10.4651 | 450  | 0.004         | -               |
| 11.6279 | 500  | 0.0231        | -               |
| 12.7907 | 550  | 0.0023        | -               |
| 13.9535 | 600  | 0.0017        | -               |
| 15.1163 | 650  | 0.0002        | -               |
| 16.2791 | 700  | 0.0001        | -               |
| 17.4419 | 750  | 0.0001        | -               |
| 18.6047 | 800  | 0.0001        | -               |
| 19.7674 | 850  | 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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