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master_cate_bc22

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master_cate_bc22 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 남자 후리스 점퍼 자켓 패딩 가을 겨울 남학생 고등학생 중학생 빅사이즈 3XL E_XXL 출산/육아 > 유아발육용품 > 점퍼루
- text: 부드러운 양말 스타 유아 워커 편안한 국민 미끄럼 신발 아기 부티 보행기 첫 따뜻한 방지 16=B315white_13-18달 출산/육아
> 유아발육용품 > 보행기
- text: 이븐플로 엑서쏘서 트리플펀 아마존 점프앤런 9종 택1 출산선물 조카선물 이븐플로 엑서쏘서_트리플펀 아마존 출산/육아 > 유아발육용품
> 쏘서
- text: 신생아 바운서 유아 용품 흔들 요람 침대 스마트 출산 카키 접이식 표준 엘리트 버전 출산/육아 > 유아발육용품 > 바운서/흔들침대
- text: 아기보행기 O자형 전복방지 카트탑승가능 보행기 4.하늘색 유아교육경음악발매트 출산/육아 > 유아발육용품 > 보행기
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 -->
<!-- - **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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0 | <ul><li>'동물 자동차 당김 로프, 조기 교육 선물, 드래그 차량 딸랑이 158447 B2QD2SS901998-6 158447 B2QD2SS901998-4 출산/육아 > 유아발육용품 > 보행기'</li><li>'[대여] [보행기] NEW 뉴 스텝360 다기능 아기보행기 대여 렌탈 ①스텝360_3개월대여 출산/육아 > 유아발육용품 > 보행기'</li><li>'휴대용 접이식 신생아 걸음마 보행기 Egobaby 360 베이비 캐리어 다기능 통기성 유아 백팩 어린이 캐리지 아기 슬링 랩 멜빵 옴니 28 breeze black print 출산/육아 > 유아발육용품 > 보행기'</li></ul> |
| 2.0 | <ul><li>'이븐플로 엑서쏘서 트리플펀 아마존 점프앤런 9종 택1 출산선물 조카선물 이븐플로 엑서쏘서_사파리 친구들 점프&런 출산/육아 > 유아발육용품 > 쏘서'</li><li>'젤리캣 동물 인형 딸랑이 버니 퍼피 양 코끼리 유니콘 5종 유니콘 출산/육아 > 유아발육용품 > 쏘서'</li><li>'[대여][+10일연장] 바로가능 새상품입고 피아노 어라운드 위고 베이비아인슈타인/위고대여 중고판매상품_피아노어라운드위고 출산/육아 > 유아발육용품 > 쏘서'</li></ul> |
| 0.0 | <ul><li>'2021 new 스마트 흔들바운서 진동요람 (B타입)그린 출산/육아 > 유아발육용품 > 바운서/흔들침대'</li><li>'[개봉/미개봉] 베이비뵨 바운서 대여 렌탈 메쉬 저지 코튼 아기 신생아 컨디션A 05. 소프트코튼저지(개봉)_2개월+10일_랜덤발송 출산/육아 > 유아발육용품 > 바운서/흔들침대'</li><li>'[대여][+7일추가/왕복 ] 최신형 크래들스윙대여 피셔프라이스 [베이비노리터] 신형 버튼식 A급+이너시트(당일출고)_블루_한달대여+7일서비스 출산/육아 > 유아발육용품 > 바운서/흔들침대'</li></ul> |
| 3.0 | <ul><li>'여자 어린이 겨울 패딩 후드 잠바 다운 재킷 6. 2중후드 패딩 퍼플화이트_140cm 출산/육아 > 유아발육용품 > 점퍼루'</li><li>'[대여][미개봉새상품대여] 졸리점퍼 오리지널스탠드 슈퍼스탠드 점퍼루 쏘서 슈퍼 스탠드[+7일 추가]_2022입고 한달대여[+7일 추가] 출산/육아 > 유아발육용품 > 점퍼루'</li><li>'경량 패딩점퍼 오버핏 중년 여성롱패딩 빅사이즈 구스다운 퀄팅 롱패딩 블랙_XL 출산/육아 > 유아발육용품 > 점퍼루'</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_bc22")
# Run inference
preds = model("아기보행기 O자형 전복방지 카트탑승가능 보행기 4.하늘색 유아교육경음악발매트 출산/육아 > 유아발육용품 > 보행기")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 8 | 15.8643 | 35 |
| 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 | 0.489 | - |
| 0.9091 | 50 | 0.4696 | - |
| 1.8182 | 100 | 0.1613 | - |
| 2.7273 | 150 | 0.0002 | - |
| 3.6364 | 200 | 0.0 | - |
| 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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