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master_cate_bc1

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master_cate_bc1 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 손가락 빠는 아기 치발기 손빠는 아이 못빨게 교정기 빨기 방지 신생아 엄지 백일 100일 손가락 빨기 방지 엄지 장갑 (L) 출산/육아
> 구강청결용품 > 손가락빨기방지용품
- text: 치아모형 간단한 헤드 모델 치과 시뮬레이터 팬텀 헤드, 의사 교육용, 트레이닝 기구 출산/육아 > 구강청결용품 > 기타구강청결용품
- text: 이천사 OT704 SSSS 7형0.4mm 치실치간칫솔 교정치간칫솔 유아 어린이 65- CT22- 3단계 이중라운드-12개 출산/육아
> 구강청결용품 > 기타구강청결용품
- text: 제니튼 닥터제니 어린이치약 4개 미니4개 초등학생치약 유아 베이비 [1450 고불소]_1450 고불소(라즈베리)4개+미니치약4개 출산/육아
> 구강청결용품 > 유아치약
- text: 이천사 OT110 S 1형1.0mm 치실치간칫솔 교정치간칫솔 유아 어린이 71- PT31- 청소년이중슬림모-12개 출산/육아 > 구강청결용품
> 기타구강청결용품
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:** 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>'가그린 어린이 가글 딸기 3개 구강청결제 출산/육아 > 구강청결용품 > 유아구강세정제'</li><li>'메가텐 키즈 소닉 럭스 360 전동칫솔 리필모 4P 미디엄(만4세-만12세) 출산/육아 > 구강청결용품 > 유아구강세정제'</li><li>'2080 kids 어린이가글250ml/ 저불소/ 바나나맛/딸기맛 구강청결제 작은사이즈 키즈 상품선택_바나나맛250ml 출산/육아 > 구강청결용품 > 유아구강세정제'</li></ul> |
| 0.0 | <ul><li>'코클리어 시원한호흡 코밴드 코패치 10일사용(10매1박스) 출산/육아 > 구강청결용품 > 기타구강청결용품'</li><li>'플랙커스치실 일회용치실 어린이 유아 아기 치실 4팩 1. 플랙커스 키즈치실 30p 4팩 출산/육아 > 구강청결용품 > 기타구강청결용품'</li><li>'닥터코링 출산/육아 > 구강청결용품 > 기타구강청결용품'</li></ul> |
| 5.0 | <ul><li>'켄트 초등학생 키즈 칫솔 어린이칫솔 4개입 켄트 오리지널 6개 출산/육아 > 구강청결용품 > 유아칫솔'</li><li>'메가텐 럭스360 어린이 유아 칫솔 6개입 기능성 (1 2 3 단계) 독일도스 유기농 치약(무불소)_메가텐7P_1단계 출산/육아 > 구강청결용품 > 유아칫솔'</li><li>'키즈텐 5형제 어린이 칫솔 3단계 5개입 출산/육아 > 구강청결용품 > 유아칫솔'</li></ul> |
| 4.0 | <ul><li>'키즈세이프 충치집중케어 치약 60g x 10개/ 고불소 치약, 충치 6.키즈세이프 유아칫솔 2단계 x 16개 출산/육아 > 구강청결용품 > 유아치약'</li><li>'벨레다 어린이 치약 50ml 2p+손가락칫솔 1p 충치 예방 안전 치약 벨레다 치약 2개 + 유아칫솔 1개 출산/육아 > 구강청결용품 > 유아치약'</li><li>'페리오키즈 스텝2 핑크퐁 치약 청포도향 75g 무불소 80G 출산/육아 > 구강청결용품 > 유아치약'</li></ul> |
| 3.0 | <ul><li>'[1+1] 121도씨 비앤비 리꼬 구강티슈 (오가닉 순면 무첨가물) [1+1] 121도씨 구강티슈 50매 x2 출산/육아 > 구강청결용품 > 유아구강청결티슈'</li><li>'메디안 골드 와이드프로 칫솔 4개입 잇몸케어 미세모 치석케어 이중미세모 치솔 메디안 골드 와이드프로 칫솔4개입(미세모) 출산/육아 > 구강청결용품 > 유아구강청결티슈'</li><li>'비앤비 구강청결티슈 30매 X 3개/4개/5개/2개 비앤비 구강청결티슈30매X3 출산/육아 > 구강청결용품 > 유아구강청결티슈'</li></ul> |
| 1.0 | <ul><li>'썸프렌즈 (엄지용) A set 동물 친구들_Large 출산/육아 > 구강청결용품 > 손가락빨기방지용품'</li><li>'아가프라/닥터핑거 1+1 (엄지,검지,중지)l/손가락빨기교정 양손겸용 닥터핑거(중지/검지용)-화이트_닥터핑거(중지/검지용)-퍼플 출산/육아 > 구강청결용품 > 손가락빨기방지용품'</li><li>'스몰 투명색상 닥터썸 출산/육아 > 구강청결용품 > 손가락빨기방지용품'</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_bc1")
# Run inference
preds = model("치아모형 간단한 헤드 모델 치과 시뮬레이터 팬텀 헤드, 의사 교육용, 트레이닝 기구 출산/육아 > 구강청결용품 > 기타구강청결용품")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 7 | 14.6262 | 32 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
| 4.0 | 70 |
| 5.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0120 | 1 | 0.4929 | - |
| 0.6024 | 50 | 0.4852 | - |
| 1.2048 | 100 | 0.3091 | - |
| 1.8072 | 150 | 0.0608 | - |
| 2.4096 | 200 | 0.0005 | - |
| 3.0120 | 250 | 0.0001 | - |
| 3.6145 | 300 | 0.0001 | - |
| 4.2169 | 350 | 0.0 | - |
| 4.8193 | 400 | 0.0 | - |
| 5.4217 | 450 | 0.0 | - |
| 6.0241 | 500 | 0.0 | - |
| 6.6265 | 550 | 0.0 | - |
| 7.2289 | 600 | 0.0 | - |
| 7.8313 | 650 | 0.0 | - |
| 8.4337 | 700 | 0.0 | - |
| 9.0361 | 750 | 0.0 | - |
| 9.6386 | 800 | 0.0 | - |
| 10.2410 | 850 | 0.0 | - |
| 10.8434 | 900 | 0.0 | - |
| 11.4458 | 950 | 0.0 | - |
| 12.0482 | 1000 | 0.0 | - |
| 12.6506 | 1050 | 0.0 | - |
| 13.2530 | 1100 | 0.0 | - |
| 13.8554 | 1150 | 0.0 | - |
| 14.4578 | 1200 | 0.0 | - |
| 15.0602 | 1250 | 0.0 | - |
| 15.6627 | 1300 | 0.0 | - |
| 16.2651 | 1350 | 0.0 | - |
| 16.8675 | 1400 | 0.0 | - |
| 17.4699 | 1450 | 0.0 | - |
| 18.0723 | 1500 | 0.0 | - |
| 18.6747 | 1550 | 0.0 | - |
| 19.2771 | 1600 | 0.0 | - |
| 19.8795 | 1650 | 0.0 | - |
| 20.4819 | 1700 | 0.0 | - |
| 21.0843 | 1750 | 0.0 | - |
| 21.6867 | 1800 | 0.0 | - |
| 22.2892 | 1850 | 0.0 | - |
| 22.8916 | 1900 | 0.0 | - |
| 23.4940 | 1950 | 0.0 | - |
| 24.0964 | 2000 | 0.0 | - |
| 24.6988 | 2050 | 0.0 | - |
| 25.3012 | 2100 | 0.0 | - |
| 25.9036 | 2150 | 0.0 | - |
| 26.5060 | 2200 | 0.0 | - |
| 27.1084 | 2250 | 0.0 | - |
| 27.7108 | 2300 | 0.0 | - |
| 28.3133 | 2350 | 0.0 | - |
| 28.9157 | 2400 | 0.0 | - |
| 29.5181 | 2450 | 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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