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master_cate_el16

Text Classification
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---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: WD NEW MY PASSPORT 외장SSD 1TB 외장하드 스마트폰 아이패드 XBOX 세븐컴
- text: '2.5인치 HDD SSD 보관 케이스 USB3.0 SATA 어답터 확장 외장하드 케이스 선택1: 2.5인치 HDD SSD 하드 보관함
퀄리티어슈어런스코리아'
- text: 이지넷 NEXT-350U3 3.5 외장케이스/USB3.0 하드미포함 레알몰
- text: NEXT-644DU3 4베이 HDD SSD USB3.0 도킹스테이션 프리줌
- text: Seagate IronWolf NAS ST1000VN002 1TB AS3년/공식판매점 (주)픽셀아트 (PIXELART)
inference: true
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: metric
value: 0.7785757031717534
name: Metric
---
# 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:** 12 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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3 | <ul><li>'키오시아 EXCERIA PLUS G3 M.2 NVMe 엄지척스토어'</li><li>'[키오시아] EXCERIA G2 M.2 NVMe (500GB) 주식회사 에티버스이비티'</li><li>'ADATA Ultimate SU650 120GB 밀알시스템'</li></ul> |
| 1 | <ul><li>'시놀로지 Expansion Unit DX517 (5베이/하드미포함) 타워형 확장 유닛 DS1817+, DS1517+ (주)비엔지센터'</li><li>'[아이피타임 쇼핑몰] NAS1 dual 1베이 나스 (하드미포함) (주)에이치앤인터내셔널'</li><li>'시놀로지 정품 나스 DS223 2베이 NAS 스토리지 클라우드 서버 구축 시놀로지 NAS DS223 유심홀릭'</li></ul> |
| 0 | <ul><li>'씨게이트 바라쿠다 1TB ST1000DM010 SATA3 64M 1테라 하드 오늘 출발 주식회사 호스트시스템'</li><li>'WD BLUE (WD20EZBX) 3.5 SATA HDD (2TB/7200rpm/256MB/SMR) 아이코다(주)'</li><li>'씨게이트 IronWolf 8TB ST8000VN004 (SATA3/7200/256M) (주)조이젠'</li></ul> |
| 4 | <ul><li>'Sandisk Extreme Pro CZ880 (128GB) (주)아이티엔조이'</li><li>'Sandisk Cruzer Glide CZ600 (16GB) 컴튜브 주식회사'</li><li>'샌디스크 울트라 핏 USB 3.1 32GB Ultra Fit CZ430 초소형 주식회사 에스티원테크'</li></ul> |
| 6 | <ul><li>'NEXT-DC3011TS 1:11 HDD SSD 스마트 하드복사 삭제기 리벤플러스'</li><li>'넥시 NX-802RU31 2베이 RAID 데이터 스토리지 하드 도킹스테이션 (NX768) 대성NETWORK'</li><li>'넥시 USB3.1 C타입 2베이 DAS 데이터 스토리지 NX768 (주)팁스커뮤니케이션즈'</li></ul> |
| 11 | <ul><li>'이지넷유비쿼터스 NEXT-215U3 (하드미포함) (주)컴파크씨앤씨'</li><li>'ORICO PHP-35 보라 3.5인치 하드 보호케이스 (주)조이젠'</li><li>'[ORICO] PHP-35 3.5형 하드디스크 보관함 [블루] (주)컴퓨존'</li></ul> |
| 2 | <ul><li>'(주)근호컴 [라인업시스템]LS-EXODDC 외장ODD (주)근호컴'</li><li>'[라인업시스템] LANSTAR LS-BRODD 블루레이 외장ODD 주식회사 에티버스이비티'</li><li>'넥스트유 NEXT-200DVD-RW USB3.0 DVD-RW 드라이브 ) (주)인컴씨엔에스'</li></ul> |
| 5 | <ul><li>'(주)근호컴 [멜로디]1P 투명 연질 CD/DVD 케이스 (10장) (주)근호컴'</li><li>'HP CD-R 10P / 52X 700MB / 원통케이스 포장 제품 티앤제이 (T&J) 통상'</li><li>'엑토 CD롬컨테이너_50매입 CDC-50K /CD보관함/CD케이스/씨디보관함/씨디케이스/cd정리함 CDC-50K 아이보리 솔로몬샵'</li></ul> |
| 9 | <ul><li>'시놀로지 비드라이브 BDS70-1T BeeDrive 1TB 외장SSD 개인 백업허브 정품 솔루션 웍스(Solution Works)'</li><li>'CORSAIR EX100U Portable SSD Type C (1TB) (주)아이티엔조이'</li><li>'ASUS ROG STRIX ARION ESD-S1C M 2 NVMe SSD 외장케이스 (주)아이웍스'</li></ul> |
| 8 | <ul><li>'넥스트유 NEXT-651DCU3 도킹스테이션 2베이 (주)수빈인포텍'</li><li>'이지넷유비쿼터스 넥스트유 659CCU3 도킹 스테이션 주식회사 매커드'</li><li>'이지넷유비쿼터스 NEXT-644DU3 4베이 도킹스테이션 에이치엠에스'</li></ul> |
| 10 | <ul><li>'USB3.0 4베이 DAS 스토리지 NX770 (주)담다몰'</li><li>'[NEXI] NX-804RU30 외장 케이스 HDD SSD USB 3.0 4베이 하드 도킹스테이션 NX770 주식회사 유진정보통신'</li><li>'[NEXI] 넥시 NX-804RU30 RAID (4베이) [USB3.0] [NX770] [DAS] [하드미포함] (주)컴퓨존'</li></ul> |
| 7 | <ul><li>'USB3.0 하드 도킹스테이션 복제 복사 클론 복사기 HDD SSD 2.5인치 3.5인치 듀얼 외장하드 케이스 Q6GCLONE 퀄리티어슈런스'</li><li>'USB3.0 하드 도킹스테이션 복제 복사 클론 복사기 HDD SSD 2.5인치 3.5인치 듀얼 외장하드 케이스 28TB지원 퀄리티어슈런스'</li><li>'NEXT 652DCU3 HDD복제기능탑재/도킹스테이션/2.5인치/3.5인치/백업/클론기능 마하링크'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.7786 |
## 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_el16")
# Run inference
preds = model("이지넷 NEXT-350U3 3.5 외장케이스/USB3.0 하드미포함 레알몰")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 4 | 9.6059 | 20 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 50 |
| 5 | 50 |
| 6 | 50 |
| 7 | 3 |
| 8 | 50 |
| 9 | 50 |
| 10 | 7 |
| 11 | 50 |
### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0125 | 1 | 0.497 | - |
| 0.625 | 50 | 0.2348 | - |
| 1.25 | 100 | 0.0733 | - |
| 1.875 | 150 | 0.0254 | - |
| 2.5 | 200 | 0.0165 | - |
| 3.125 | 250 | 0.0122 | - |
| 3.75 | 300 | 0.0021 | - |
| 4.375 | 350 | 0.0024 | - |
| 5.0 | 400 | 0.001 | - |
| 5.625 | 450 | 0.0019 | - |
| 6.25 | 500 | 0.0002 | - |
| 6.875 | 550 | 0.0007 | - |
| 7.5 | 600 | 0.0009 | - |
| 8.125 | 650 | 0.0002 | - |
| 8.75 | 700 | 0.0002 | - |
| 9.375 | 750 | 0.0003 | - |
| 10.0 | 800 | 0.0002 | - |
| 10.625 | 850 | 0.0002 | - |
| 11.25 | 900 | 0.0002 | - |
| 11.875 | 950 | 0.0001 | - |
| 12.5 | 1000 | 0.0001 | - |
| 13.125 | 1050 | 0.0001 | - |
| 13.75 | 1100 | 0.0001 | - |
| 14.375 | 1150 | 0.0001 | - |
| 15.0 | 1200 | 0.0001 | - |
| 15.625 | 1250 | 0.0001 | - |
| 16.25 | 1300 | 0.0001 | - |
| 16.875 | 1350 | 0.0001 | - |
| 17.5 | 1400 | 0.0001 | - |
| 18.125 | 1450 | 0.0001 | - |
| 18.75 | 1500 | 0.0001 | - |
| 19.375 | 1550 | 0.0001 | - |
| 20.0 | 1600 | 0.0001 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.20.0
## 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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