setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:mini1013/master_domain base_model:finetune:mini1013/master_domain model-index region:us
---
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: 남성 가죽장갑 GOT379X 스몰사이즈/닥스(장갑) 블랙 롯데쇼핑(주)
- text: 남성 가죽장갑 GPS742X 블랙 롯데백화점1관
- text: 방한 손가락 벙어리 장갑 기모 스마트폰 키보드 1.블랙 건강드림
- text: 데일리 털장갑 겨울 휴대폰 터치 남성 인기 신상장갑 캐주얼장갑 남자손장갑 방한장갑 직장인장갑 블랙 지플레이스
- text: 은창)크리스마스 러블리 벙어리 니트 장갑 털장갑 루돌프 그레이 비니벨라
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: 0.8876621100595864
      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:** 3 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                                                                                                                                                                                                  |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0   | <ul><li>'경량 방수토시 팔토시 작업 위생 안전 경량방수토시(블랙계열) 바움하우스'</li><li>'사러왕 운전 팔토시 레이스 여성 쉬폰 골프토시 워머 암 핸드 롱장갑 4 레이스 여성팔토시 살구색 언벤샵'</li><li>'여성운전 팔토시 화이트 태양금'</li></ul>                                                |
| 2.0   | <ul><li>'[갤러리아] 닥스 DCGV3F287 [남녀공용] 베이지 캐시미어 니트 장갑(타임월드)  한화갤러리아(주)'</li><li>'[갤러리아] 루이까또즈 방울방울 니트워머 GGILW30005 GGILW30005 베이지 한화갤러리아(주)'</li><li>'(신세계김해점)질스튜어트 여성 가죽장갑 GBS740X 블랙(01) 신세계백화점'</li></ul> |
| 0.0   | <ul><li>'남성 가죽 콤비장갑 GPD293H/닥스(장갑) 블랙 롯데쇼핑(주)'</li><li>'(10%+10%쿠폰) 시즌오프 잡화 / 장갑 목도리 스타킹 양말 방한용품 1_15.윈터 마스크캡_1+1 스킨라이즈'</li><li>'[갤러리아] [닥스] 남성 가죽 장갑 (D) GPS332H(타임월드) 진브라운91 한화갤러리아(주)'</li></ul>      |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8877 |

## 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_ac12")
# Run inference
preds = model("남성 가죽장갑 GPS742X 블랙 롯데백화점1관")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 10.5733 | 24  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0417  | 1    | 0.4357        | -               |
| 2.0833  | 50   | 0.1092        | -               |
| 4.1667  | 100  | 0.006         | -               |
| 6.25    | 150  | 0.0002        | -               |
| 8.3333  | 200  | 0.0002        | -               |
| 10.4167 | 250  | 0.0001        | -               |
| 12.5    | 300  | 0.0001        | -               |
| 14.5833 | 350  | 0.0001        | -               |
| 16.6667 | 400  | 0.0001        | -               |
| 18.75   | 450  | 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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