README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 매일유업 앱솔루트 센서티브 1단계 900g x 1개 [음료] 차음료_비락식혜175ml30캔 출산/육아 > 분유 > 국내분유
- text: Hipp 힙 콤비오틱 유기농 1단계 800g [육아] 분유_Hipp 힙 콤비오틱 유기농 3단계 800g 출산/육아 > 분유 > 수입분유
- text: 남양유업 아이엠마더 액상 3단계 240ml x96개  출산/육아 > 분유 > 국내분유
- text: 일동후디스 프리미엄 산양분유 3단계 800g x 1개 [육아] 분유_파스퇴르 무항생제 위드맘 3단계 750g 출산/육아 > 분유 >
    국내분유
- text: 일동후디스 프리미엄 산양분유 1단계 800g x 1개 [음료] 탄산음료_웰치스제로오렌지355ml24캔 출산/육아 > 분유 > 국내분유
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
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: accuracy
      value: 1.0
      name: Accuracy
---

# 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:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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>'셀렉스 매일 마시는 프로틴 12l 160ml × 48개 출산/육아 > 분유 > 특수분유'</li><li>'일동후디스 초유밀플러스2단계 1캔(1gx90포))  출산/육아 > 분유 > 특수분유'</li><li>'gvp 스마트폰 카드포켓 스마트링블랙  출산/육아 > 분유 > 특수분유'</li></ul>                                                                                                                                                      |
| 0.0   | <ul><li>'매일유업 앱솔루트 명작 2FL 액상 2단계 240ml 24개 x2개  출산/육아 > 분유 > 국내분유'</li><li>'매일유업 앱솔루트 센서티브 1단계 900g x 1개 [라면] 봉지라면_얼큰한 너구리 120g 20개 출산/육아 > 분유 > 국내분유'</li><li>'매일유업 앱솔루트 센서티브 1단계 900g x 1개 [음료] 우유두유_삼육검은콩앤칼슘파우치190ml40팩 출산/육아 > 분유 > 국내분유'</li></ul>                                                                              |
| 1.0   | <ul><li>'힙 압타밀 HA 뢰벤짠 밀라산 홀레 퇴퍼 베바 세레락 프레 2단계 콤비오틱 무전분 산양 [퇴퍼] Töpfer_퇴퍼 락타나 600g (최대8통)_[1통] xPRE Topfer 출산/육아 > 분유 > 수입분유'</li><li>'뉴트리시아 압타밀 프로누트라 어드밴스 2단계 800g [음료] 탄산음료_데미소다피치250ml30캔 출산/육아 > 분유 > 수입분유'</li><li>'퇴퍼 홀레 뢰벤짠 힙 노발락 압타밀 무전분 AR 킨더밀쉬 압타밀 오가닉(New)_오가닉 2 800g 1통_◆dm4056631003169_1◆ 출산/육아 > 분유 > 수입분유'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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_bc6")
# Run inference
preds = model("남양유업 아이엠마더 액상 3단계 240ml x96개  출산/육아 > 분유 > 국내분유")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 7   | 14.9429 | 30  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 70                    |

### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch   | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0238  | 1    | 0.4943        | -               |
| 1.1905  | 50   | 0.4806        | -               |
| 2.3810  | 100  | 0.1671        | -               |
| 3.5714  | 150  | 0.0003        | -               |
| 4.7619  | 200  | 0.0           | -               |
| 5.9524  | 250  | 0.0           | -               |
| 7.1429  | 300  | 0.0           | -               |
| 8.3333  | 350  | 0.0           | -               |
| 9.5238  | 400  | 0.0           | -               |
| 10.7143 | 450  | 0.0           | -               |
| 11.9048 | 500  | 0.0           | -               |
| 13.0952 | 550  | 0.0           | -               |
| 14.2857 | 600  | 0.0           | -               |
| 15.4762 | 650  | 0.0           | -               |
| 16.6667 | 700  | 0.0           | -               |
| 17.8571 | 750  | 0.0           | -               |
| 19.0476 | 800  | 0.0           | -               |
| 20.2381 | 850  | 0.0           | -               |
| 21.4286 | 900  | 0.0           | -               |
| 22.6190 | 950  | 0.0           | -               |
| 23.8095 | 1000 | 0.0           | -               |
| 25.0    | 1050 | 0.0           | -               |
| 26.1905 | 1100 | 0.0           | -               |
| 27.3810 | 1150 | 0.0           | -               |
| 28.5714 | 1200 | 0.0           | -               |
| 29.7619 | 1250 | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## 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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