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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 오리엔트카페트 북유럽 극세사 거실 대형 특 빈티지 바닥패드 물세탁 소형 러그원룸작은 여름 가구/인테리어>카페트/러그>왕골자리
- text: 쇼파마작자리 3인 가구/인테리어>카페트/러그>왕골자리
- text: 리브맘 달콤 쿨매트 미니싱글 가구/인테리어>카페트/러그>쿨매트
- text: VIP 데일리 이지케어 생활방수 러그 카페트 가구/인테리어>카페트/러그>왕골자리
- text: 나르샤매트 TPU 발편한 주방매트 일반형 가구/인테리어>카페트/러그>발매트
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:** 6 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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2.0 | <ul><li>'1 1 데이지 규조토 4세대 발매트 구조토 규토 빨아쓰는 캠핑 발닦개 욕실 주방 가구/인테리어>카페트/러그>발매트'</li><li>'욕실 CAMPING 러그 발매트 현관 HAPPY MWA238F8 가구/인테리어>카페트/러그>발매트'</li><li>'감성 인테리어 캠프파이어 발매트 욕실 주방 화장실 현관 베란다 러그 물세탁 국산 가구/인테리어>카페트/러그>발매트'</li></ul> |
| 3.0 | <ul><li>'알티피아 피크닉 스트라이프 돗자리 소풍 야외 캠핑돗자리 WC7D33D 가구/인테리어>카페트/러그>왕골자리'</li><li>'마전동상회 극세사 드로잉 논슬립 아트카페트 논슬립 거실러그 가구/인테리어>카페트/러그>왕골자리'</li><li>'사계절 카페트 짜임 면러그 60x130- 가구/인테리어>카페트/러그>왕골자리'</li></ul> |
| 1.0 | <ul><li>'썸머 트로피컬 원형 러그 가구/인테리어>카페트/러그>러그'</li><li>'데이드리머 문 스트라이프 먼지없는 거실러그 가구/인테리어>카페트/러그>러그'</li><li>'더프리그 먼지없는 워셔블 도트 땡땡이 극세사 거실카페트 사각 원형 맞춤 거실 러그 가구/인테리어>카페트/러그>러그'</li></ul> |
| 5.0 | <ul><li>'UNKNOWN 여름 이불 침대 쿨 냉감 매트 패드 시트 깔판 캠핑 가구/인테리어>카페트/러그>쿨매트'</li><li>'귀여운 라텍스 쿨매트 침대 여름 매트 토퍼 쿨 패드 쿨링 냉감 냉 베개 돌 J 가구/인테리어>카페트/러그>쿨매트'</li><li>'코스트코쿨매트 쿨 냉 여름 침대 라텍스 패드 쿨커버 원룸 매트 1 5x2 0m N 가구/인테리어>카페트/러그>쿨매트'</li></ul> |
| 0.0 | <ul><li>'한빛카페트 마리나 대나무 여름카페트 대자리 가구/인테리어>카페트/러그>대자리'</li><li>'샤인 늘품 프리미엄 17mm 죽편 대자리 가구/인테리어>카페트/러그>대자리'</li><li>'리앤데코 탄화보더 마작자리 천연 여름 대나무 돗자리 가구/인테리어>카페트/러그>대자리'</li></ul> |
| 4.0 | <ul><li>'한일카페트 150만 네오왈츠 페르시안 거실 카페트 가구/인테리어>카페트/러그>카페트>면/극세사카페트'</li><li>'스칸디앤홈 에코퍼 클라우드 27mm 장모 러그 워셔블 카페트 원형 가구/인테리어>카페트/러그>카페트>면/극세사카페트'</li><li>'마마그리드 포근포근 마요 극세사 러그 가구/인테리어>카페트/러그>카페트>면/극세사카페트'</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_fi14")
# Run inference
preds = model("쇼파마작자리 3인 가구/인테리어>카페트/러그>왕골자리")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 7.8109 | 18 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 52 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
| 4.0 | 70 |
| 5.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0127 | 1 | 0.5081 | - |
| 0.6329 | 50 | 0.4966 | - |
| 1.2658 | 100 | 0.4935 | - |
| 1.8987 | 150 | 0.2567 | - |
| 2.5316 | 200 | 0.0017 | - |
| 3.1646 | 250 | 0.0 | - |
| 3.7975 | 300 | 0.0 | - |
| 4.4304 | 350 | 0.0 | - |
| 5.0633 | 400 | 0.0 | - |
| 5.6962 | 450 | 0.0 | - |
| 6.3291 | 500 | 0.0 | - |
| 6.9620 | 550 | 0.0 | - |
| 7.5949 | 600 | 0.0 | - |
| 8.2278 | 650 | 0.0 | - |
| 8.8608 | 700 | 0.0 | - |
| 9.4937 | 750 | 0.0 | - |
| 10.1266 | 800 | 0.0 | - |
| 10.7595 | 850 | 0.0 | - |
| 11.3924 | 900 | 0.0 | - |
| 12.0253 | 950 | 0.0 | - |
| 12.6582 | 1000 | 0.0 | - |
| 13.2911 | 1050 | 0.0 | - |
| 13.9241 | 1100 | 0.0 | - |
| 14.5570 | 1150 | 0.0 | - |
| 15.1899 | 1200 | 0.0 | - |
| 15.8228 | 1250 | 0.0 | - |
| 16.4557 | 1300 | 0.0 | - |
| 17.0886 | 1350 | 0.0 | - |
| 17.7215 | 1400 | 0.0 | - |
| 18.3544 | 1450 | 0.0 | - |
| 18.9873 | 1500 | 0.0 | - |
| 19.6203 | 1550 | 0.0 | - |
| 20.2532 | 1600 | 0.0 | - |
| 20.8861 | 1650 | 0.0 | - |
| 21.5190 | 1700 | 0.0 | - |
| 22.1519 | 1750 | 0.0 | - |
| 22.7848 | 1800 | 0.0 | - |
| 23.4177 | 1850 | 0.0 | - |
| 24.0506 | 1900 | 0.0 | - |
| 24.6835 | 1950 | 0.0 | - |
| 25.3165 | 2000 | 0.0 | - |
| 25.9494 | 2050 | 0.0 | - |
| 26.5823 | 2100 | 0.0 | - |
| 27.2152 | 2150 | 0.0 | - |
| 27.8481 | 2200 | 0.0 | - |
| 28.4810 | 2250 | 0.0 | - |
| 29.1139 | 2300 | 0.0 | - |
| 29.7468 | 2350 | 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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