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master_cate_bc8

Text Classification
setfit
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sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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master_cate_bc8 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 디스커버리익스페디션키즈 디스커버리 키즈 로고 래쉬가드 출산/육아 > 수영복/용품 > 남아수영복
- text: 뉴발란스키즈 뉴발란스 키즈 Beach Lounge 래쉬가드 유니 2in1 수영복 NK9RE2110U 출산/육아 > 수영복/용품 >
남아수영복
- text: 뷰 아동 수경 일반렌즈 일본 V424J LV 출산/육아 > 수영복/용품 > 수경/수모/귀마개
- text: 벤디스 아동 남아 여아 래쉬가드 세트 유아 수영복 P208 28.에스닉후드 비치 가디건 S207_오렌지_공용_7호 출산/육아 > 수영복/용품
> 남아수영복
- text: 비치는 반투명 수영장 방수 비치백 목욕가방 3종세트 상품선택_핑크세트 출산/육아 > 수영복/용품 > 수영가방/비치백
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:** 4 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 |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0 | <ul><li>'플랩캡 모자 자외선차단 수영모자 유아 아동 공용 UV모자 C_핑크_M 출산/육아 > 수영복/용품 > 수경/수모/귀마개'</li><li>'아기물안경 유아물안경 성인용 CA01 화이트 출산/육아 > 수영복/용품 > 수경/수모/귀마개'</li><li>'UV 플랩캡 아기 유아 아동 수영모자 버킷햇 해변 워터파크 자외선차단 UV플랩캡_그레이_S(3세이하) 출산/육아 > 수영복/용품 > 수경/수모/귀마개'</li></ul> |
| 2.0 | <ul><li>'어린이 수영가방 유아 비치백 여아 유치원 캐치티니핑 C.수영모자_47_로이도이 소프트_화이트/56 (770297) 출산/육아 > 수영복/용품 > 수영가방/비치백'</li><li>'어린이수영가방 수영장가방 비치백 유아 아동 유치원 09.엘오엘_LOL 레트로 비치 핸드백(핑크) 출산/육아 > 수영복/용품 > 수영가방/비치백'</li><li>'물빠지는 방수 메쉬 목욕가방 스파백_33.NCCSB11_블랙 출산/육아 > 수영복/용품 > 수영가방/비치백'</li></ul> |
| 0.0 | <ul><li>'레노마 아레나 슬라임 아동 4부 남아동수영복 A3BB1BF02 출산/육아 > 수영복/용품 > 남아수영복'</li><li>'스플래쉬어바웃 사계절 키즈래쉬가드 쇼티 웨트슈트 남아래쉬가드 남아수영복 키즈수영복 터그보츠_XXL(8-10세) 출산/육아 > 수영복/용품 > 남아수영복'</li><li>'디스커버리익스페디션키즈 키즈 로고 래쉬가드 L MINT 출산/육아 > 수영복/용품 > 남아수영복'</li></ul> |
| 3.0 | <ul><li>'아레나 초등여아 실내수영복 초등학생 키즈 주니어 4부 5부 반신 생존수영A3FG1GL22 핑크_70 출산/육아 > 수영복/용품 > 여아수영복'</li><li>'뿔공룡 유아 래쉬가드(90-120) 204119 피치90 출산/육아 > 수영복/용품 > 여아수영복'</li><li>'블루독 하트전판레쉬가드세트 24940 621 52 출산/육아 > 수영복/용품 > 여아수영복'</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_bc8")
# Run inference
preds = model("뷰 아동 수경 일반렌즈 일본 V424J LV 출산/육아 > 수영복/용품 > 수경/수모/귀마개")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 7 | 13.5607 | 24 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0182 | 1 | 0.4886 | - |
| 0.9091 | 50 | 0.4981 | - |
| 1.8182 | 100 | 0.3363 | - |
| 2.7273 | 150 | 0.0279 | - |
| 3.6364 | 200 | 0.0001 | - |
| 4.5455 | 250 | 0.0 | - |
| 5.4545 | 300 | 0.0 | - |
| 6.3636 | 350 | 0.0 | - |
| 7.2727 | 400 | 0.0 | - |
| 8.1818 | 450 | 0.0 | - |
| 9.0909 | 500 | 0.0 | - |
| 10.0 | 550 | 0.0 | - |
| 10.9091 | 600 | 0.0 | - |
| 11.8182 | 650 | 0.0 | - |
| 12.7273 | 700 | 0.0 | - |
| 13.6364 | 750 | 0.0 | - |
| 14.5455 | 800 | 0.0 | - |
| 15.4545 | 850 | 0.0 | - |
| 16.3636 | 900 | 0.0 | - |
| 17.2727 | 950 | 0.0 | - |
| 18.1818 | 1000 | 0.0 | - |
| 19.0909 | 1050 | 0.0 | - |
| 20.0 | 1100 | 0.0 | - |
| 20.9091 | 1150 | 0.0 | - |
| 21.8182 | 1200 | 0.0 | - |
| 22.7273 | 1250 | 0.0 | - |
| 23.6364 | 1300 | 0.0 | - |
| 24.5455 | 1350 | 0.0 | - |
| 25.4545 | 1400 | 0.0 | - |
| 26.3636 | 1450 | 0.0 | - |
| 27.2727 | 1500 | 0.0 | - |
| 28.1818 | 1550 | 0.0 | - |
| 29.0909 | 1600 | 0.0 | - |
| 30.0 | 1650 | 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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