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---
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: 칸토스 남여 기능성 다이어트 지압슬리퍼 5. 신여성자갈_240 온누리산업
- text: 국산 헤라칸 케나프 케냐프 발 발바닥 지압 건강 슬리퍼 실내화 연핑크(M) 예일마켓
- text: 풀리오 종아리마사지기 V3 디와이shop
- text: 풋 브러쉬 발각질 제거 마사지 큐오랩
- text: 마사지 실내 발지압매트 돌지압판 50x200CM보보보생꽃롱 도치글로벌
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: 0.9710123383380407
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:** 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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6.0 | <ul><li>'자갈 매트 지압판 조약돌 발판 지압길 지압 발매트 40X60CM 컬러풀 에이알'</li><li>'뽀송뽀송 메모리폼 발닦개매트 욕실 주방 발매트 러그 대형 면 화장실 라지 50X80_레드 시그나몰'</li><li>'굳은 살 걱정없는 특허기술 헬스핀 발 지압매트 지압판 부모님 선물 효도 헬스핀 운동 기획상품_핑크 붐코리아'</li></ul> |
| 1.0 | <ul><li>'김수자 엔젤 종아리 발마사지기 다리안마기 GKM-1004 닥터PLUS'</li><li>'듀플렉스 쎄라웨어 온열찜질 발마사지기 DP-FM700 신인선'</li><li>'7만원대 추가할인 여름휴가필수 [ ]업그레이드 3세대 스마트센서 종아리부터 허벅지까지 붓기 싹!! 무선다리마사지기 SR-S1+슬리밍삭스(옐로우) 수련닷컴'</li></ul> |
| 5.0 | <ul><li>'발각질양말 실리콘 패드 발보습 양말 발각질 케어 화이트 석진케이 주식회사'</li><li>'[BZJKWP4I_49]irbrush 뒤꿈치 패드 풋케어 발각질 3.블랙(5mm)FREE 롯데아이몰'</li><li>'일상공감 보드랍족 발보호대 1+1 뒤꿈치 발각질 보습풋패드 양말 발보호대 1+1_스킨 L 1쌍+화이트 M 1쌍 주식회사 이공구오'</li></ul> |
| 4.0 | <ul><li>'쾌발Q 60매 발냄새제거제/유해세균억제/무좀/발관리 해피MART'</li><li>'편백나무 슈즈프레쉬 신발장 옷장 탈취 발냄새제거 제습 방향효과 크레비스'</li><li>'[공식수입] 발냄새제거제 그랜즈레미디 페퍼민트향 cscosmetics'</li></ul> |
| 0.0 | <ul><li>'평발 아치 슬리퍼 발바닥 통증 완화 아치슬리퍼 사파이어 블루_290 탱큐'</li><li>'통굽 지압슬리퍼 실내화 층간소음방지 미끄럼방지 욕실화 도톨 지압 옐로우 39-40 9025 파인메탈릭'</li><li>'[낫소]낫소 지압2 슬리퍼 아이보리/230 패션플러스'</li></ul> |
| 3.0 | <ul><li>'바렌 매직 스텐 양면 프로 발각질제거기 포유어뷰티'</li><li>'바렌 패디퍼펙트 전동 발각질 제거기 퍼플에디션 발 뒤꿈치 발바닥 굳은살 제거 패디플래닝 1세트(사은품 증정)_리뷰약속 x (주)마르스랩스'</li><li>'오제끄 실크풋 버퍼 단품 인앤인마켓'</li></ul> |
| 7.0 | <ul><li>'[리퍼브]굿프렌드 밸런스휴 건식좌훈족욕기 GOOD-F5 리퍼브 건식좌훈족욕기 GOOD-F5 주식회사 굿테크'</li><li>'GSN-1610 편백나무 원적외선 건식 좌훈기+족욕기 겸용 MinSellAmount 온유어핏'</li><li>'B 굿프렌드 캐나다산 소나무 원목 스마트 건식족욕기 GOOD-F4 휴게실 가정용 마켓뷰'</li></ul> |
| 2.0 | <ul><li>'염색도구세트 셀프 키트 볼 브러쉬 가정용 헤어 브러시 빗 머리 모발 간편 07.Aeib 염색빗세트3P_본상품선택 주식회사유마켓'</li><li>'OC1242 손가락 발가락 관절보호 보습 실리콘 골무18종 통기화이트S(12425) 테익디스(TAKE THIS)'</li><li>'OC1242 손가락 발가락 보호 보습 실리콘 골무18종 실리콘골무 구멍뚫린골무 발가락 보호 골무스킨톤L(11033) 제이한 주식회사'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9710 |
## 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_lh11")
# Run inference
preds = model("풋 브러쉬 발각질 제거 마사지 큐오랩")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 9.9325 | 21 |
| 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 |
| 7.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0159 | 1 | 0.4383 | - |
| 0.7937 | 50 | 0.2003 | - |
| 1.5873 | 100 | 0.0636 | - |
| 2.3810 | 150 | 0.0158 | - |
| 3.1746 | 200 | 0.0239 | - |
| 3.9683 | 250 | 0.0153 | - |
| 4.7619 | 300 | 0.0004 | - |
| 5.5556 | 350 | 0.0023 | - |
| 6.3492 | 400 | 0.0005 | - |
| 7.1429 | 450 | 0.0002 | - |
| 7.9365 | 500 | 0.0001 | - |
| 8.7302 | 550 | 0.0001 | - |
| 9.5238 | 600 | 0.0001 | - |
| 10.3175 | 650 | 0.0001 | - |
| 11.1111 | 700 | 0.0001 | - |
| 11.9048 | 750 | 0.0001 | - |
| 12.6984 | 800 | 0.0 | - |
| 13.4921 | 850 | 0.0001 | - |
| 14.2857 | 900 | 0.0001 | - |
| 15.0794 | 950 | 0.0001 | - |
| 15.8730 | 1000 | 0.0 | - |
| 16.6667 | 1050 | 0.0001 | - |
| 17.4603 | 1100 | 0.0 | - |
| 18.2540 | 1150 | 0.0001 | - |
| 19.0476 | 1200 | 0.0001 | - |
| 19.8413 | 1250 | 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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