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master_cate_sl14

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master_cate_sl14 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 피라미드 Pyramid Path 디럭스 더블 롤러와 오버사이즈 액세서리 포켓 볼링 백 로열 스포츠/레저>볼링>볼링가방
- text: 볼링 파우치 싱글볼용 백 공 휴대용 스포츠/레저>볼링>볼링가방
- text: 900글로벌 T N T 볼링공 12-16파운드 스포츠/레저>볼링>볼링공
- text: KR 스트라이크포스 스타 청록 오른손 여성 볼링화 스포츠/레저>볼링>볼링화
- text: 해머 공인구 햄머 바이브 볼링공 15파운드 소프트볼 시소백 스포츠/레저>볼링>볼링공
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 -->
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### 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>'매드볼링 72매 볼링공클리너티슈 스포츠/레저>볼링>볼링용품'</li><li>'BEL 볼링 기름제거 걸레 천 볼링공 광 마찰 스포츠/레저>볼링>볼링용품'</li><li>'면 로프 크로켓 위켓 5pcs 소프트 게임 스틱 7 08x5 9 플레이어 세트 가족 게임용 스포츠/레저>볼링>볼링용품'</li></ul> |
| 0.0 | <ul><li>'와이드앵글 CO 미니 캐주얼 볼링백 WWU23B09K7 LE1214748392 스포츠/레저>볼링>볼링가방'</li><li>'FOTTSFOTTS 볼링백 미니 - BOWLING BAG MINI 219966 스포츠/레저>볼링>볼링가방'</li><li>'대륙 스파이크 풀셋 가방 스포츠/레저>볼링>볼링가방'</li></ul> |
| 5.0 | <ul><li>'볼링 아대 핸드 손목 가드 스포츠/레저>볼링>아대'</li><li>'선브릿지 메카텍터 MECHATECTER 볼링 아대 왼손 MD-4DX 스포츠/레저>볼링>아대'</li><li>'1쌍 프로볼링장갑 통기성장갑 스포츠장갑 스포츠/레저>볼링>아대'</li></ul> |
| 4.0 | <ul><li>'해머 디젤 왼손 볼링화 남성용 - 9 5 스포츠/레저>볼링>볼링화'</li><li>'Dexter 볼링 슈즈 스포츠/레저>볼링>볼링화'</li><li>'ACCOREN 볼링화 커버 1피스 - 볼링화용 조절 가능한 볼링화 슬라이더 - 프리미엄 볼링 액세서리 - 일관된 스포츠/레저>볼링>볼링화'</li></ul> |
| 3.0 | <ul><li>'어썸 향균쿨론 탄탄스판티 단체 볼링 티셔츠 5장이상 2L AS20200402 스포츠/레저>볼링>볼링의류'</li><li>'SAVALINO 남성용 볼링 폴로 셔츠 소재 땀 흡수 빠른 건조 사이즈 5X-Large 스포츠/레저>볼링>볼링의류'</li><li>'오프화이트 홀리데이 볼링 패턴 반팔셔츠 OMGG004C99FAB001 1000 스포츠/레저>볼링>볼링의류'</li></ul> |
| 1.0 | <ul><li>'해머 Hammer Widow Legend Bowling Ball 13lbs 155828 스포츠/레저>볼링>볼링공'</li><li>'로우 해머 볼링 공 블루실버화이트 12 스포츠/레저>볼링>볼링공'</li><li>'단체활동 10000 플렛볼 파워 플레시 스포츠/레저>볼링>볼링공'</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_sl14")
# Run inference
preds = model("볼링 파우치 싱글볼용 백 공 휴대용 스포츠/레저>볼링>볼링가방")
```
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## Bias, Risks and Limitations
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 8.8452 | 20 |
| 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 | 0.4925 | - |
| 0.6024 | 50 | 0.4964 | - |
| 1.2048 | 100 | 0.3374 | - |
| 1.8072 | 150 | 0.0388 | - |
| 2.4096 | 200 | 0.0003 | - |
| 3.0120 | 250 | 0.0001 | - |
| 3.6145 | 300 | 0.0001 | - |
| 4.2169 | 350 | 0.0001 | - |
| 4.8193 | 400 | 0.0 | - |
| 5.4217 | 450 | 0.0 | - |
| 6.0241 | 500 | 0.0001 | - |
| 6.6265 | 550 | 0.0001 | - |
| 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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