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mini1013
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master_cate_el1

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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master_cate_el1 / README.md
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metadata
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: 넥스트 USB 3.0 2포트 PCI Express 카드 (NEXT-212U3) YNMI-NK0431  윤 미디어
  - text: 앱코 NCORE G30 트루포스 (블랙) 미들타워 컴퓨터 케이스  오케이 바이오
  - text: >-
      APC SMC1500I-2U Smart UPS 900W/1500VA 무정전 전원공급장치 교체배터리 전원백업장치 (DHCNC) 
      주식회사 대현씨앤씨
  - text: 이지넷 카드리더기 NEXT-8603TCU3 블랙 [KF]  주식회사 케이에프컴퍼니
  - text: >-
      다크플래쉬 darkFlash DS900 ARGB 강화유리 컴퓨터 PC 케이스 (블랙)  주식회사 아크런 (Akrun Co.,
      Ltd.)
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: 0.9098343017000216
            name: Metric

SetFit with mini1013/master_domain

This is a SetFit model that can be used for Text Classification. This SetFit model uses mini1013/master_domain as the Sentence Transformer embedding model. A LogisticRegression 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 with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
9
  • 'APC BK500EI UPS배터리 무정전전원장치 300W 500VA 다피(dappy)'
  • '리안리 SP750 80PLUS GOLD (WHITE) 주식회사 브라보세컨즈'
  • 'APC Smart UPS C 2000VA Tower 무정전전원장치 - smc2000ic 주식회사 파인인프라'
2
  • '3RSYS R200 RGB (블랙) 미들타워 컴온씨앤씨(주)'
  • 'DAVEN AQUA (블랙) 주식회사 꿈누리'
  • 'w 대원TMT DW-H1200 허브랙 (H1200×D800×W600/25U/회색) (착불배송) (주)원영씨앤씨'
0
  • '인텔 코어i7-13세대 13700K 랩터레이크 정품 에어캡배송 (주)신우밀루유떼'
  • 'AMD 라이젠5-4세대 5600X (버미어)벌크포장 AS 3년 태성에프앤비(주)'
  • '[INTEL] 코어10세대 i7-10700 벌크 병행 쿨러미포함 (코멧레이크) (주)컴퓨존'
4
  • 'SAPPHIRE 라데온 RX 7900 GRE PURE D6 16GB 주식회사 꿈누리'
  • 'ASRock 라데온 RX 7900 XTX Phantom Gaming OC D6 24GB 대원씨티에스 주식회사 에스씨엠인포텍'
  • '[HY] INNO3D 지포스 GT1030 D5 2GB LP 무소음 (주)제이케이존'
8
  • '잘만 ZM-STC10 (2g) 주식회사 피씨사자'
  • '3RSYS APB BAR 35 (주)컴퓨존'
  • 'LP30 ARGB PSU 커버 화이트 주식회사보성닷컴'
6
  • 'NEXTU NEXT-206NEC EX 에스앤와이'
  • 'LANstar PCI-E 내부 SATA3 4포트 카드/LS-PCIE-4SATA/PC 내부에 SATA3 4포트 생성/발열 방지용 방열판/LP 브라켓 포함 디피시스템'
  • 'NEXTU NEXT-405NEC LP 에스앤와이'
3
  • 'V-Color BLACK DDR5-5200 CL42 STANDARD 벌크 (8GB) (주)가이드컴'
  • 'TEAMGROUP T-Force DDR5 6000 CL38 Delta RGB 화이트 패키지 32GB(16Gx2) (주)서린씨앤아이'
  • 'ADATA DDR5-5600 CL46 (16GB)/정품판매점/하이닉스A다이/언락/평생 제한 보증/R 주식회사 에이알씨앤아이'
5
  • 'ASRock H510M-HDV/M.2 SE 에즈윈 주식회사디케이'
  • 'DK ASRock B760M PG Riptide D5 에즈윈 주식회사디케이'
  • '[ ] GIGABYTE B650 AORUS ELITE AX ICE 제이씨현 뉴비시스템즈'
7
  • '아틱 P14 PWM PST 블랙 VALUE 5팩 (주)서린씨앤아이'
  • '앱코 타이폰 120X5 CPU 쿨러 알루미늄 방열판 주식회사 지디스엠알오'
  • 'Thermalright Peerless Assassin 120 SE 서린 태성에프앤비(주)'
1
  • '엠비에프 CAT.7 SFTP 금도금 UTP 3중 쉴드 패치코드 기가비트 랜케이블 0.5M (MBF-U705G) 주식회사 아크런 (Akrun Co., Ltd.)'
  • 'MBF-C5E305R 305M 레드 BOX CAT.5E UTP 랜케이블 컴샷정보'
  • '엠비에프 CAT.5e UTP 제작형 랜케이블 박스 MBF-C5E305Y 옐로우 305m (주)아토닉스'

Evaluation

Metrics

Label Metric
all 0.9098

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_el1")
# Run inference
preds = model("앱코 NCORE G30 트루포스 (블랙) 미들타워 컴퓨터 케이스  오케이 바이오")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 4 9.206 18
Label Training Sample Count
0 50
1 50
2 50
3 50
4 50
5 50
6 50
7 50
8 50
9 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
0.0127 1 0.4969 -
0.6329 50 0.2753 -
1.2658 100 0.0677 -
1.8987 150 0.014 -
2.5316 200 0.0023 -
3.1646 250 0.0001 -
3.7975 300 0.0001 -
4.4304 350 0.0001 -
5.0633 400 0.0001 -
5.6962 450 0.0 -
6.3291 500 0.0001 -
6.9620 550 0.0001 -
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 -

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 

@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}
}