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master_cate_fi4

Text Classification
setfit
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sentence-transformers
roberta
generated_from_setfit_trainer
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master_cate_fi4 / README.md
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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 2장 지퍼형 항균베개솜 4060 애프터식스 가구/인테리어>솜류>베개솜/속통>일반베개솜
- text: 홈즈리빙 알러지케어 순면 시그니처 경추베개 가구/인테리어>솜류>베개솜/속통>마이크로화이바베개솜
- text: 그레이 바닥요매트 요솜 싱글1인용 요커버 J리빙 가구/인테리어>솜류>요솜/매트솜>견면요솜
- text: 솔로젠 가드풀 바이오 문손잡이 커버 소형 2매입 자전거 도어락 TgQ 가구/인테리어>솜류>요솜/매트솜>견면요솜
- text: 겨울용 알러지케어 블랙파이핑 헝가리 구스 이불 솜털80 - 퀸 가구/인테리어>솜류>이불솜>거위털/오리털이불솜
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:** 5 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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4.0 | <ul><li>'토게 속성 인형 이누마키 솜인형 솜뭉치 가구/인테리어>솜류>쿠션솜'</li><li>'모던하우스 호텔 다운필 쿠션솜 50x50 FP4119002 가구/인테리어>솜류>쿠션솜'</li><li>'텐바이텐 푹신한 국산 쿠션솜 지퍼형 빵빵한 구름솜 50x50 가구/인테리어>솜류>쿠션솜'</li></ul> |
| 2.0 | <ul><li>'목화 솜 요 솜이불 겨울 패드 토퍼 이불 바닥 목화솜 가구/인테리어>솜류>요솜/매트솜>목화요솜'</li><li>'이브자리 뉴 레이언 요솜 S D Q K 가구/인테리어>솜류>요솜/매트솜>견면요솜'</li><li>'생일 축하 케이크 토퍼 글리터 발레 걸 댄스 발레리나 여아용 파티 장식 댄서 토퍼 골든 132066 가구/인테리어>솜류>요솜/매트솜>견면요솜'</li></ul> |
| 3.0 | <ul><li>'폭스베딩 사계절용 모달 헝가리 구스다운 이불 솜털93프로 - 킹600g 가구/인테리어>솜류>이불솜>거위털/오리털이불솜'</li><li>'슈프렐 95도 사계절 이불솜 가구/인테리어>솜류>이불솜>일반이불솜'</li><li>'북유럽풍 램스울 양모 겨울이불 순면 이불세트 침구 극세사 두꺼운 가구/인테리어>솜류>이불솜>양모이불솜'</li></ul> |
| 0.0 | <ul><li>'베이직 방석솜 가구/인테리어>솜류>방석솜'</li><li>'코지톡 사용감의 원형 솜방석 4개 가구/인테리어>솜류>방석솜'</li><li>'포근한 하라홈 국내산 구름 새솜 방석솜 50x50 가구/인테리어>솜류>방석솜'</li></ul> |
| 1.0 | <ul><li>'힐튼 호텔 퀼팅베개 계절베개 가구/인테리어>솜류>베개솜/속통>거위털/오리털베개솜'</li><li>'바운티풀 호텔베개 폴란드 구스다운 90 수피마면 삼중구조 구스베개 600g 가구/인테리어>솜류>베개솜/속통>거위털/오리털베개솜'</li><li>'폭스베딩 프라우덴 헝가리산 구스 베개솜 솜털90 60수 베개커버선물 EH2TXX00106 가구/인테리어>솜류>베개솜/속통>거위털/오리털베개솜'</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_fi4")
# Run inference
preds = model("2장 지퍼형 항균베개솜 4060 애프터식스 가구/인테리어>솜류>베개솜/속통>일반베개솜")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 2 | 8.6171 | 19 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
| 4.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0145 | 1 | 0.4828 | - |
| 0.7246 | 50 | 0.4997 | - |
| 1.4493 | 100 | 0.2078 | - |
| 2.1739 | 150 | 0.0067 | - |
| 2.8986 | 200 | 0.0001 | - |
| 3.6232 | 250 | 0.0 | - |
| 4.3478 | 300 | 0.0 | - |
| 5.0725 | 350 | 0.0 | - |
| 5.7971 | 400 | 0.0 | - |
| 6.5217 | 450 | 0.0 | - |
| 7.2464 | 500 | 0.0 | - |
| 7.9710 | 550 | 0.0 | - |
| 8.6957 | 600 | 0.0 | - |
| 9.4203 | 650 | 0.0 | - |
| 10.1449 | 700 | 0.0 | - |
| 10.8696 | 750 | 0.0 | - |
| 11.5942 | 800 | 0.0 | - |
| 12.3188 | 850 | 0.0 | - |
| 13.0435 | 900 | 0.0 | - |
| 13.7681 | 950 | 0.0 | - |
| 14.4928 | 1000 | 0.0 | - |
| 15.2174 | 1050 | 0.0 | - |
| 15.9420 | 1100 | 0.0 | - |
| 16.6667 | 1150 | 0.0 | - |
| 17.3913 | 1200 | 0.0 | - |
| 18.1159 | 1250 | 0.0 | - |
| 18.8406 | 1300 | 0.0 | - |
| 19.5652 | 1350 | 0.0 | - |
| 20.2899 | 1400 | 0.0 | - |
| 21.0145 | 1450 | 0.0 | - |
| 21.7391 | 1500 | 0.0 | - |
| 22.4638 | 1550 | 0.0 | - |
| 23.1884 | 1600 | 0.0 | - |
| 23.9130 | 1650 | 0.0 | - |
| 24.6377 | 1700 | 0.0 | - |
| 25.3623 | 1750 | 0.0 | - |
| 26.0870 | 1800 | 0.0 | - |
| 26.8116 | 1850 | 0.0 | - |
| 27.5362 | 1900 | 0.0 | - |
| 28.2609 | 1950 | 0.0 | - |
| 28.9855 | 2000 | 0.0 | - |
| 29.7101 | 2050 | 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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