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---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 오너클랜 블랙헤드 클리닝 24개입 코팩 스킨애 대용량 참숯 옵션없음 모던벙커
- text: 히든올가 히아루론산 마스크 400g 옵션없음 판타시아
- text: '[셀러허브 1]프롬더스킨 글루타치온 콜라겐 팩 50g 1개 RS 기본 에스케이스토아주식회사'
- text: 볼라욘 스피넴 파우더500g(모델링 마스크)+샘플+팩도구세트 옵션없음 수애
- text: 피부관리실/피부 미용 실기 비타민 열석고 1kg 황토 석고 700g 주식회사 엔에프코리아
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: accuracy
value: 0.6112115732368897
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:** 8 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>'리쥬란 마스크팩 리쥬란힐러 마스크 2박스 총10개입 (병원용) MKSISTER'</li><li>'폴리머 수화겔 하이드로겔 마스크 팩세트 35gx4P 주식회사 에이에이앤티'</li><li>'바이오던스 바이오 콜라겐 리얼 딥 마스크 4매 콜라겐마스크팩 저분자 블망몰'</li></ul> |
| 6.0 | <ul><li>'블랙헤드 아웃 시트 35개입 3개 옵션없음 건강드림'</li><li>'CNP 차앤박 안티 포어 블렉헤드 클리어 키트 10매세트 옵션없음 구메구메'</li><li>'아이스 얼음팩 쿨링 마스크 피부 자극 붓기 열 진정 자외선 옵션없음 삼성메디원'</li></ul> |
| 5.0 | <ul><li>'이즈앤트리 머그워트 카밍 클레이 마스크 100ml 1개 옵션없음 비래유통'</li><li>'정품 정케이스 설화수 백삼팩 120ml 옵션없음 맥스베스트'</li><li>'마미케어 들깨 워시오프 팩 100g 옵션없음 주식회사 올리브인터내셔널'</li></ul> |
| 4.0 | <ul><li>'메노킨 30초 퀵 버블 마스크_브라이트 옵션없음 주식회사 포레스트에비뉴'</li><li>'아비브 라이스 프로바이오틱스 오버나이트 마스크 배리어 젤리 80ml 옵션없음 주식회사 지에스원(GS ONE CO.,LTD.)'</li><li>'[갤러리아] [GL]슈퍼 나이츠 - 클리어 수딩 마스크 점보 120ml(한화갤러리아㈜ 센터시티) 옵션없음 한화갤러리아(주)'</li></ul> |
| 7.0 | <ul><li>'끌레드벨 원킬 브이 리프팅 마스크 옵션없음 아이앤에이 (INA)'</li><li>'루미너스 쿨링팩 4개 쿨링팩 4개 히얼위아'</li><li>'메디필 콜라겐 마스크 2+1 옵션없음 성원아토'</li></ul> |
| 2.0 | <ul><li>'골프 피부진정 패치 5매 아이패치 하이드로겔 등산 옵션없음 아이템코리아주식회사(Item KOREA Inc.)'</li><li>'13 에어뮤즈 멜라이드 패치스탠다드 아이패치 야외 5매 옵션없음 네이비몰'</li><li>'퍼스트랩 프로바이오틱 마스크 시즌4 25g 1매 옵션없음 다사다 유한책임회사'</li></ul> |
| 3.0 | <ul><li>'볼라욘 스피넴 파우더 스피루리나 모델링팩 500g+팩도구+샘플 옵션없음 라플레르'</li><li>'데쌍브르 골드 필 오프 모델링팩 1kg 데쌍브르 골드 필 오프 모델링팩 1kg 마가렛스킨'</li><li>'데쌍브르 골드 모델링 마스크 1000g + 고무볼,스파츌라,앰플 골드 모델링마스크 반하다'</li></ul> |
| 0.0 | <ul><li>'IS-SA 오이 바이탈라이징 마사지크림 500g 옵션없음 우리의쇼핑'</li><li>'MSM글루코사민크림 핫 120ml 맛사지크림 뉴나노웰제약 옵션없음 운호'</li><li>'더후 공진향 인양 넥앤페이스 탄력 리페어75ml 옵션없음 씨플랩'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.6112 |
## 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_bt2_test")
# Run inference
preds = model("히든올가 히아루론산 마스크 400g 옵션없음 판타시아")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 9.7453 | 23 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 16 |
| 1.0 | 10 |
| 2.0 | 42 |
| 3.0 | 21 |
| 4.0 | 20 |
| 5.0 | 19 |
| 6.0 | 15 |
| 7.0 | 18 |
### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (50, 50)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 60
- 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0526 | 1 | 0.4875 | - |
| 2.6316 | 50 | 0.3322 | - |
| 5.2632 | 100 | 0.0442 | - |
| 7.8947 | 150 | 0.0025 | - |
| 10.5263 | 200 | 0.0002 | - |
| 13.1579 | 250 | 0.0001 | - |
| 15.7895 | 300 | 0.0001 | - |
| 18.4211 | 350 | 0.0001 | - |
| 21.0526 | 400 | 0.0001 | - |
| 23.6842 | 450 | 0.0001 | - |
| 26.3158 | 500 | 0.0001 | - |
| 28.9474 | 550 | 0.0001 | - |
| 31.5789 | 600 | 0.0001 | - |
| 34.2105 | 650 | 0.0001 | - |
| 36.8421 | 700 | 0.0001 | - |
| 39.4737 | 750 | 0.0001 | - |
| 42.1053 | 800 | 0.0001 | - |
| 44.7368 | 850 | 0.0001 | - |
| 47.3684 | 900 | 0.0001 | - |
| 50.0 | 950 | 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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