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master_cate_bc2

Text Classification
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master_cate_bc2 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '[도착보장] 엘프레리 에어윙 팬티 밤 기저귀 4팩 M사이즈 (팩당 32개입) 출산/육아 > 기저귀 > 일회용기저귀'
- text: 마미포코 물놀이팬티 4-5단계 (남녀선택) 12매 출산/육아 > 기저귀 > 수영장기저귀
- text: 플라팜 뉴코코맘 아기 천기저귀 5매 출산/육아 > 기저귀 > 천기저귀
- text: 프리미엄 친환경 아기 팬티기저귀 XL 18매 출산/육아 > 기저귀 > 일회용기저귀
- text: 팸퍼스 2025 통잠팬티 팬티형 밤기저귀 4단계 4팩+4팩(총 240매) 출산/육아 > 기저귀 > 일회용기저귀
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
---
# 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:** 4 classes
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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 |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0 | <ul><li>'아라칸 아기 물놀이 방수 기저귀 3개입 2세트 총 6매 출산/육아 > 기저귀 > 수영장기저귀'</li><li>'마미포코 물놀이팬티 4-5단계 (남녀선택) 12매 출산/육아 > 기저귀 > 수영장기저귀'</li><li>'밤보 물놀이 수영팬티 스몰 1팩(12P) 출산/육아 > 기저귀 > 수영장기저귀'</li></ul> |
| 2.0 | <ul><li>'나비잠 나비잠 울트라씬듀얼핏 팬티 6팩 출산/육아 > 기저귀 > 일회용기저귀'</li><li>'르소메 프리미엄 통잠 밤 아기 신생아 발진없는 밴드형 기저귀 2팩 출산/육아 > 기저귀 > 일회용기저귀'</li><li>'애플크럼비 [보리보리/애플크럼비]애플크럼비 NEW 오리지널 테이프 XL 6팩(108매) 출산/육아 > 기저귀 > 일회용기저귀'</li></ul> |
| 3.0 | <ul><li>'아가방 새싹오가닉 기저귀 5매 출산/육아 > 기저귀 > 천기저귀'</li><li>'베베라온 신생아 밤부 천기저귀 선물 체험 출산/육아 > 기저귀 > 천기저귀'</li><li>'투유모유 무형광 무나염 순면 국산 아기 천기저귀 2박스 구매시 파우치 증정 출산/육아 > 기저귀 > 천기저귀'</li></ul> |
| 0.0 | <ul><li>'[베이비앙] 국내산 무형광 사이즈 상관없이 벨크로 탈부착으로 사용 가능 기저귀 고정을 위한 천 기저귀밴드 출산/육아 > 기저귀 > 기저귀커버/기저귀밴드'</li><li>'처비체리 천기저귀 커버 쁘띠코숑 P tit Cochon 1개 출산/육아 > 기저귀 > 기저귀커버/기저귀밴드'</li><li>'포켓식 원사이즈 기저귀커버 3장세트(잠금장치&색상선택) 출산/육아 > 기저귀 > 기저귀커버/기저귀밴드'</li></ul> |
## 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_bc2")
# Run inference
preds = model("플라팜 뉴코코맘 아기 천기저귀 5매 출산/육아 > 기저귀 > 천기저귀")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 9 | 12.95 | 20 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 20 |
| 1.0 | 20 |
| 2.0 | 20 |
| 3.0 | 20 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0625 | 1 | 0.476 | - |
| 3.125 | 50 | 0.3608 | - |
| 6.25 | 100 | 0.0472 | - |
| 9.375 | 150 | 0.0 | - |
| 12.5 | 200 | 0.0 | - |
| 15.625 | 250 | 0.0 | - |
| 18.75 | 300 | 0.0 | - |
| 21.875 | 350 | 0.0 | - |
| 25.0 | 400 | 0.0 | - |
| 28.125 | 450 | 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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