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master_cate_bc5

Text Classification
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sentence-transformers
roberta
generated_from_setfit_trainer
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master_cate_bc5 / README.md
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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 커버형 약통 물티슈박스 2개 위생소품 신생아 오염방지 보관함 출산/육아 > 물티슈 > 물티슈워머/물티슈캡
- text: 유한 세정 살균 일회용 물 티슈 3종 상품선택_시트러스불렌드 출산/육아 > 물티슈 > 휴대용
- text: '[비타토 탈부착 물티슈캡] 미니형 물티슈케이스 8컬러 택1 미니 체리핑크 출산/육아 > 물티슈 > 물티슈워머/물티슈캡'
- text: 네이쳐러브메레 건티슈 15매 출산/육아 > 물티슈 > 리필형
- text: 하트민 업소용물티슈 100T 600매 1매포장물티슈 두꺼운 업소 1회용 식당물티슈 지퍼백포장물티슈_70T 600매 출산/육아 > 물티슈
> 코인티슈/업소용
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: 0.9978378378378379
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:** 7 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6.0 | <ul><li>'BROWN 프리미엄 옐로우 물티슈 휴대 캡형 20매 출산/육아 > 물티슈 > 휴대용'</li><li>'쪼꼬미 휴대용 물티슈 30팩 미니형 소형 미니 여행용 쪼꼬미물티슈_90팩 출산/육아 > 물티슈 > 휴대용'</li><li>'자연속에 업소용 물티슈 64g(그린) x1 박스(400개) 1매물 음식점 물수건 휴대용 출산/육아 > 물티슈 > 휴대용'</li></ul> |
| 4.0 | <ul><li>'미니무지A 600매 개별포장 대용량 배달용 식당용 업소용 판촉 1회용 일회용 물티슈 출산/육아 > 물티슈 > 코인티슈/업소용'</li><li>'베지터블 압축 코인 티슈 300 코스트코 압축티슈 동전티슈 출산/육아 > 물티슈 > 코인티슈/업소용'</li><li>'하트민 130T 520매 일회용 식당물티슈 업소용 물수건 두툼한 식당용 고급 1회용물티슈 낱개 80g 800매 출산/육아 > 물티슈 > 코인티슈/업소용'</li></ul> |
| 3.0 | <ul><li>'달곰이 노블레스 아기물티슈 크로스엠보싱 72매x10팩 캡형 72매 10팩 캡형 출산/육아 > 물티슈 > 캡형'</li><li>'지크린텍 미엘 클래식 물티슈 캡형 100매 x 10매 캡형 100매 20매 출산/육아 > 물티슈 > 캡형'</li><li>'리꼬베이비 안전한 신생아 유아 대용량 캡형 10팩 20팩 도톰한 두꺼운 아기물티슈 모음전 11.시그니처 70매 10팩 캡 65g 출산/육아 > 물티슈 > 캡형'</li></ul> |
| 1.0 | <ul><li>'설랩수 온천 건티슈 전용 멸균온천수 떼르말 스프링 리필 스틱20ml 출산/육아 > 물티슈 > 리필형'</li><li>'우리집 건티슈 대용량 2.5kg 1500매 대량구매 두툼한원단 플레인 엠보싱 선택1.소프트 건티슈 2.5kg 플레인_1~2박스구매시 1박스가격 출산/육아 > 물티슈 > 리필형'</li><li>'베베솜 무표백 건티슈 순면 신생아건티슈 아기물티슈 리필형 퓨어_10매x15팩(150매) 출산/육아 > 물티슈 > 리필형'</li></ul> |
| 0.0 | <ul><li>'보람씨앤에치 붕어빵 패밀리 비데 물티슈 캡형 50매 10팩 출산/육아 > 물티슈 > 기능성물티슈 > 비데용'</li><li>'유한킴벌리 크리넥스 마이비데 클린케어 물티슈 캡형 46매 x 10팩 + 휴대용 10매 x 3팩 03.밸런스캡40매x5팩+밸런스휴대10매x8팩 출산/육아 > 물티슈 > 기능성물티슈 > 비데용'</li><li>'깨끗한나라 클린 손소독티슈 휴대용 10매 5팩 출산/육아 > 물티슈 > 기능성물티슈 > 손소독용'</li></ul> |
| 5.0 | <ul><li>'오리지널 플레인 휴대용 리필형 30매[12팩] 출산/육아 > 물티슈 > 혼합세트'</li><li>'신상품 물티슈 오리지널 휴대용 30매 출산/육아 > 물티슈 > 혼합세트'</li><li>'(물티슈 100매 모음) 그린터치 더플로라 하늘선물 캡형 리필 (최소 구매 10개) 하늘선물 물티슈 캡형 출산/육아 > 물티슈 > 혼합세트'</li></ul> |
| 2.0 | <ul><li>'아기 물티슈워머 물티슈보온 USB 네모 따뜻한 아기 물티슈워머 물티슈보온 USB 출산/육아 > 물티슈 > 물티슈워머/물티슈캡'</li><li>'베베숲 시그니처 위드블루 캡형 아기물티슈 저자극 70매 20개 70매 20개 출산/육아 > 물티슈 > 물티슈워머/물티슈캡'</li><li>'앙블랑 아기물티슈 민트 캡형 72매 x 10팩 출산/육아 > 물티슈 > 물티슈워머/물티슈캡'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9978 |
## 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_bc5")
# Run inference
preds = model("네이쳐러브메레 건티슈 15매 출산/육아 > 물티슈 > 리필형")
```
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 7 | 15.1388 | 30 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
| 4.0 | 70 |
| 5.0 | 70 |
| 6.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0104 | 1 | 0.4945 | - |
| 0.5208 | 50 | 0.4632 | - |
| 1.0417 | 100 | 0.2302 | - |
| 1.5625 | 150 | 0.0215 | - |
| 2.0833 | 200 | 0.0003 | - |
| 2.6042 | 250 | 0.0001 | - |
| 3.125 | 300 | 0.0001 | - |
| 3.6458 | 350 | 0.0 | - |
| 4.1667 | 400 | 0.0 | - |
| 4.6875 | 450 | 0.0 | - |
| 5.2083 | 500 | 0.0 | - |
| 5.7292 | 550 | 0.0 | - |
| 6.25 | 600 | 0.0 | - |
| 6.7708 | 650 | 0.0 | - |
| 7.2917 | 700 | 0.0 | - |
| 7.8125 | 750 | 0.0 | - |
| 8.3333 | 800 | 0.0 | - |
| 8.8542 | 850 | 0.0 | - |
| 9.375 | 900 | 0.0 | - |
| 9.8958 | 950 | 0.0 | - |
| 10.4167 | 1000 | 0.0 | - |
| 10.9375 | 1050 | 0.0 | - |
| 11.4583 | 1100 | 0.0 | - |
| 11.9792 | 1150 | 0.0 | - |
| 12.5 | 1200 | 0.0 | - |
| 13.0208 | 1250 | 0.0 | - |
| 13.5417 | 1300 | 0.0 | - |
| 14.0625 | 1350 | 0.0 | - |
| 14.5833 | 1400 | 0.0 | - |
| 15.1042 | 1450 | 0.0 | - |
| 15.625 | 1500 | 0.0 | - |
| 16.1458 | 1550 | 0.0 | - |
| 16.6667 | 1600 | 0.0 | - |
| 17.1875 | 1650 | 0.0 | - |
| 17.7083 | 1700 | 0.0 | - |
| 18.2292 | 1750 | 0.0 | - |
| 18.75 | 1800 | 0.0 | - |
| 19.2708 | 1850 | 0.0 | - |
| 19.7917 | 1900 | 0.0 | - |
| 20.3125 | 1950 | 0.0 | - |
| 20.8333 | 2000 | 0.0 | - |
| 21.3542 | 2050 | 0.0 | - |
| 21.875 | 2100 | 0.0 | - |
| 22.3958 | 2150 | 0.0 | - |
| 22.9167 | 2200 | 0.0 | - |
| 23.4375 | 2250 | 0.0 | - |
| 23.9583 | 2300 | 0.0 | - |
| 24.4792 | 2350 | 0.0 | - |
| 25.0 | 2400 | 0.0 | - |
| 25.5208 | 2450 | 0.0 | - |
| 26.0417 | 2500 | 0.0 | - |
| 26.5625 | 2550 | 0.0 | - |
| 27.0833 | 2600 | 0.0 | - |
| 27.6042 | 2650 | 0.0 | - |
| 28.125 | 2700 | 0.0 | - |
| 28.6458 | 2750 | 0.0 | - |
| 29.1667 | 2800 | 0.0 | - |
| 29.6875 | 2850 | 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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