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master_cate_sl12

Text Classification
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master_cate_sl12 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 배드민턴 스윙연습 연습기 연습 훈련 트레이닝 손목 효과 스포츠/레저>배드민턴>연습용품
- text: 요넥스 나노지 배드민턴스트링 NBG 98-2 200M 스포츠/레저>배드민턴>스트링
- text: 동호회 배트민턴채 관리 교체용 롤스트링 배드민턴스트링 스포츠/레저>배드민턴>스트링
- text: 배드민턴연습기 스윙 셀프 훈련 서브 트레이닝 혼자 레슨 스포츠/레저>배드민턴>연습용품
- text: 키모니 납테이프 알파 플러스 라켓 밸런스 테이프 KBN261 스포츠/레저>배드민턴>기타배드민턴용품
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:** 10 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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 8.0 | <ul><li>'토알슨 배드민턴 롤 스트링 거트 200m BL-6700 스포츠/레저>배드민턴>스트링'</li><li>'배드민턴줄교체 테니스 리폼 조정 스트링 기계 텐션 스포츠/레저>배드민턴>스트링'</li><li>'테니스 거트 라켓줄 라켓 스트링 1 25 원형 네츄럴 스포츠/레저>배드민턴>스트링'</li></ul> |
| 6.0 | <ul><li>'요넥스 이클립션Z 미드 남녀공용 배드민턴화 스포츠/레저>배드민턴>배드민턴화'</li><li>'미즈노 남여 배드민턴화 웨이브 스텔스 네오 에너지 스포츠/레저>배드민턴>배드민턴화'</li><li>'미즈노 배드민턴화 체대입시화 사이클론 스피드 3 BM10314574 스포츠/레저>배드민턴>배드민턴화'</li></ul> |
| 4.0 | <ul><li>'어태커 W-FORCE 배드민턴라켓 스포츠/레저>배드민턴>배드민턴라켓'</li><li>'미즈노 알티우스 01 필 배드민턴라켓 스포츠/레저>배드민턴>배드민턴라켓'</li><li>'TAAN 미라지 100 배드민턴라켓 스포츠/레저>배드민턴>배드민턴라켓'</li></ul> |
| 2.0 | <ul><li>'요넥스 테니스가방 베드민턴 백팩 라켓백 BAG2328-007 스포츠/레저>배드민턴>배드민턴가방'</li><li>'2023 요넥스 배드민턴 테니스 백팩 가방 BA02312EX 스포츠/레저>배드민턴>배드민턴가방'</li><li>'테크니스트 배드민턴 미니파우치 가방 TBS-32 TBS-33 스포츠/레저>배드민턴>배드민턴가방'</li></ul> |
| 0.0 | <ul><li>'배드민턴 그립 밴드 라켓 손잡이 오버 테이프 스포츠/레저>배드민턴>그립'</li><li>'키모니 타올그립 1롤 10m 6컬러 KGT116 스포츠/레저>배드민턴>그립'</li><li>'배드민턴 골프 심 라켓 그립 테이프 테니스 손잡이 스포츠/레저>배드민턴>그립'</li></ul> |
| 1.0 | <ul><li>'낫소 배드민턴라켓 템테이션 카본 레저용 라켓세트 학교체육 스포츠/레저>배드민턴>기타배드민턴용품'</li><li>'익스트리모 LiNing 배드민턴 라켓 스트링 미포함 AYPM438-4 스포츠/레저>배드민턴>기타배드민턴용품'</li><li>'트라이온 스트링머신 X-700 스포츠/레저>배드민턴>기타배드민턴용품'</li></ul> |
| 7.0 | <ul><li>'셔틀콕 1박스 25타 배드민턴셔틀콕 KK7000 스포츠/레저>배드민턴>셔틀콕'</li><li>'요넥스 배드민턴 셔틀콕 FEATHER 12개입 AS-10EX 스포츠/레저>배드민턴>셔틀콕'</li><li>'경원 셔틀콕 배드민턴공 스포츠/레저>배드민턴>셔틀콕'</li></ul> |
| 3.0 | <ul><li>'니스포 멀티 지주네트 5m 세트 스포츠/레저>배드민턴>배드민턴네트'</li><li>'런웨이브 배드민턴 네트 LW-0187 스포츠/레저>배드민턴>배드민턴네트'</li><li>'이고진 배드민턴 네트 스포츠/레저>배드민턴>배드민턴네트'</li></ul> |
| 9.0 | <ul><li>'배드민턴 연습기 트레이닝 성인 초등학생 리턴콕 실내 3 5m 기본형 공1 야광1 스포츠/레저>배드민턴>연습용품'</li><li>'배드민턴 스윙 연습기 셀프 트레이닝 훈련 스매싱 스포츠/레저>배드민턴>연습용품'</li><li>'배드민턴 셀프 트레이닝 스파링 연습기 스매싱 서브 스포츠/레저>배드민턴>연습용품'</li></ul> |
| 5.0 | <ul><li>'패기앤코 남성 티셔츠 DT-118 스포츠/레저>배드민턴>배드민턴의류'</li><li>'요넥스 아노락 바람막이 긴팔 티셔츠 BE 231JJ003U 스포츠/레저>배드민턴>배드민턴의류'</li><li>'요넥스 여성 바람막이 긴팔티셔츠 반바지 233JJ002U 231PH002FNV 스포츠/레저>배드민턴>배드민턴의류'</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_sl12")
# Run inference
preds = model("요넥스 나노지 배드민턴스트링 NBG 98-2 200M 스포츠/레저>배드민턴>스트링")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 2 | 8.0186 | 22 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 16 |
| 4.0 | 70 |
| 5.0 | 70 |
| 6.0 | 70 |
| 7.0 | 70 |
| 8.0 | 70 |
| 9.0 | 69 |
### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0079 | 1 | 0.475 | - |
| 0.3968 | 50 | 0.4972 | - |
| 0.7937 | 100 | 0.2864 | - |
| 1.1905 | 150 | 0.1285 | - |
| 1.5873 | 200 | 0.0559 | - |
| 1.9841 | 250 | 0.0233 | - |
| 2.3810 | 300 | 0.007 | - |
| 2.7778 | 350 | 0.0026 | - |
| 3.1746 | 400 | 0.0006 | - |
| 3.5714 | 450 | 0.0004 | - |
| 3.9683 | 500 | 0.0002 | - |
| 4.3651 | 550 | 0.0001 | - |
| 4.7619 | 600 | 0.0001 | - |
| 5.1587 | 650 | 0.0001 | - |
| 5.5556 | 700 | 0.0001 | - |
| 5.9524 | 750 | 0.0001 | - |
| 6.3492 | 800 | 0.0001 | - |
| 6.7460 | 850 | 0.0002 | - |
| 7.1429 | 900 | 0.0001 | - |
| 7.5397 | 950 | 0.0001 | - |
| 7.9365 | 1000 | 0.0 | - |
| 8.3333 | 1050 | 0.0 | - |
| 8.7302 | 1100 | 0.0 | - |
| 9.1270 | 1150 | 0.0 | - |
| 9.5238 | 1200 | 0.0 | - |
| 9.9206 | 1250 | 0.0 | - |
| 10.3175 | 1300 | 0.0 | - |
| 10.7143 | 1350 | 0.0 | - |
| 11.1111 | 1400 | 0.0 | - |
| 11.5079 | 1450 | 0.0 | - |
| 11.9048 | 1500 | 0.0 | - |
| 12.3016 | 1550 | 0.0 | - |
| 12.6984 | 1600 | 0.0 | - |
| 13.0952 | 1650 | 0.0 | - |
| 13.4921 | 1700 | 0.0 | - |
| 13.8889 | 1750 | 0.0 | - |
| 14.2857 | 1800 | 0.0 | - |
| 14.6825 | 1850 | 0.0 | - |
| 15.0794 | 1900 | 0.0 | - |
| 15.4762 | 1950 | 0.0 | - |
| 15.8730 | 2000 | 0.0 | - |
| 16.2698 | 2050 | 0.0 | - |
| 16.6667 | 2100 | 0.0 | - |
| 17.0635 | 2150 | 0.0 | - |
| 17.4603 | 2200 | 0.0 | - |
| 17.8571 | 2250 | 0.0 | - |
| 18.2540 | 2300 | 0.0 | - |
| 18.6508 | 2350 | 0.0 | - |
| 19.0476 | 2400 | 0.0 | - |
| 19.4444 | 2450 | 0.0 | - |
| 19.8413 | 2500 | 0.0 | - |
| 20.2381 | 2550 | 0.0 | - |
| 20.6349 | 2600 | 0.0 | - |
| 21.0317 | 2650 | 0.0 | - |
| 21.4286 | 2700 | 0.0 | - |
| 21.8254 | 2750 | 0.0 | - |
| 22.2222 | 2800 | 0.0 | - |
| 22.6190 | 2850 | 0.0 | - |
| 23.0159 | 2900 | 0.0 | - |
| 23.4127 | 2950 | 0.0 | - |
| 23.8095 | 3000 | 0.0 | - |
| 24.2063 | 3050 | 0.0 | - |
| 24.6032 | 3100 | 0.0 | - |
| 25.0 | 3150 | 0.0 | - |
| 25.3968 | 3200 | 0.0 | - |
| 25.7937 | 3250 | 0.0 | - |
| 26.1905 | 3300 | 0.0 | - |
| 26.5873 | 3350 | 0.0 | - |
| 26.9841 | 3400 | 0.0 | - |
| 27.3810 | 3450 | 0.0 | - |
| 27.7778 | 3500 | 0.0 | - |
| 28.1746 | 3550 | 0.0 | - |
| 28.5714 | 3600 | 0.0 | - |
| 28.9683 | 3650 | 0.0 | - |
| 29.3651 | 3700 | 0.0 | - |
| 29.7619 | 3750 | 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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