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mini1013
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master_cate_top_bt13_9

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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master_cate_top_bt13_9 / README.md
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metadata
base_model: klue/roberta-base
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      밀크바오밥 오리지널 샴푸 베이비파우더 1L 09_트리트먼트 화이트머스크 1000ml (#M)화장품/미용>헤어케어>샴푸 AD >
      Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 약산성샴푸
  - text: >-
      무코타염색제 7박스+3박스+정품 트리트먼트 50g 1.카키브라운 (#M)바디/헤어>바디케어>바디케어세트 Gmarket > 뷰티 >
      바디/헤어 > 바디케어 > 바디케어세트
  - text: >-
      1+1세트~(컨센+릴렉스마스크100ml) 에스테티카 데미지 케어 컨센트레이트 120ml (열활성 열보호 에센스) 정품 +
      릴렉스마스크100ml 1개 (#M)쿠팡 홈>싱글라이프>샤워/세안>헤어에센스 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 >
      헤어에센스
  - text: >-
      헤드스파7 트리트먼트 더 프리미엄 210ml + 210ml MinSellAmount (#M)바디/헤어>헤어케어>헤어트리트먼트
      Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 헤어트리트먼트
  - text: >-
      헤어플러스 실크 코팅 트리트먼트 50ml 4개 실크 코팅 트리트먼트 50ml 4개 위메프 > 생활·주방·반려동물 > 바디/헤어 >
      샴푸/린스/헤어케어 > 트리트먼트;위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어;위메프 > 뷰티 > 바디/헤어
      > 샴푸/린스/헤어케어 > 샴푸/린스;(#M)위메프 > 생활·주방용품 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트 위메프 >
      뷰티 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트
inference: true
model-index:
  - name: SetFit with klue/roberta-base
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.8206115779645191
            name: Accuracy

SetFit with klue/roberta-base

This is a SetFit model that can be used for Text Classification. This SetFit model uses klue/roberta-base 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 Type: SetFit
  • Sentence Transformer body: klue/roberta-base
  • Classification head: a LogisticRegression instance
  • Maximum Sequence Length: 512 tokens
  • Number of Classes: 2 classes

Model Sources

Model Labels

Label Examples
1
  • '로레알파리 토탈리페어5 트리트먼트 헤어팩 170ml × 1개 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩'
  • '아모스 녹차실감 인텐시브 팩 250ml 녹차실감 인텐시브팩250g 홈>전체상품;(#M)홈>녹차실감 Naverstore > 화장품/미용 > 헤어케어 > 헤어팩'
  • '프리미엄 헤어클리닉 헤어팩 258ml 베이비파우더 LotteOn > 뷰티 > 헤어케어 > 헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩'
0
  • '퓨어시카 트리트먼트 베이비파우더향 1000ml 1개 MinSellAmount 스마일배송 홈>뷰티>바디케어>바디워시;스마일배송 홈>;(#M)스마일배송 홈>뷰티>헤어케어/스타일링>트리트먼트/팩 Gmarket > 뷰티 > 바디/헤어 > 바디케어 > 바디클렌저'
  • '1+1 살림백서 탈모 샴푸 엑티브B7 맥주효모 앤 비오틴 1000ml 남자 여자 바이오틴 4)오푼티아 트리트먼트 유칼립투스 1L (#M)화장품/미용>헤어케어>탈모케어 AD > Naverstore > 화장품/미용 > 가을뷰티 > 각질관리템 > 탈모샴푸'
  • '1+1 살림백서 오푼티아 퍼퓸 샴푸 500ml 약산성 비듬 지성 두피 볼륨 유칼립투스향 13.유칼립투스 트리트먼트 1+1 500ml (#M)화장품/미용>헤어케어>샴푸 AD > Naverstore > 화장품/미용 > 머스크 > 샴푸'

Evaluation

Metrics

Label Accuracy
all 0.8206

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_top_bt13_9")
# Run inference
preds = model("무코타염색제 7박스+3박스+정품 트리트먼트 50g 1.카키브라운 (#M)바디/헤어>바디케어>바디케어세트 Gmarket > 뷰티 > 바디/헤어 > 바디케어 > 바디케어세트")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 14 23.76 98
Label Training Sample Count
0 50
1 50

Training Hyperparameters

  • batch_size: (64, 64)
  • num_epochs: (30, 30)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 100
  • 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
0.0064 1 0.4326 -
0.3185 50 0.3579 -
0.6369 100 0.2616 -
0.9554 150 0.0326 -
1.2739 200 0.0 -
1.5924 250 0.0 -
1.9108 300 0.0 -
2.2293 350 0.0 -
2.5478 400 0.0 -
2.8662 450 0.0 -
3.1847 500 0.0 -
3.5032 550 0.0 -
3.8217 600 0.0 -
4.1401 650 0.0 -
4.4586 700 0.0 -
4.7771 750 0.0 -
5.0955 800 0.0 -
5.4140 850 0.0 -
5.7325 900 0.0 -
6.0510 950 0.0 -
6.3694 1000 0.0 -
6.6879 1050 0.0 -
7.0064 1100 0.0 -
7.3248 1150 0.0 -
7.6433 1200 0.0 -
7.9618 1250 0.0 -
8.2803 1300 0.0 -
8.5987 1350 0.0 -
8.9172 1400 0.0 -
9.2357 1450 0.0 -
9.5541 1500 0.0 -
9.8726 1550 0.0 -
10.1911 1600 0.0 -
10.5096 1650 0.0 -
10.8280 1700 0.0 -
11.1465 1750 0.0 -
11.4650 1800 0.0 -
11.7834 1850 0.0 -
12.1019 1900 0.0 -
12.4204 1950 0.0 -
12.7389 2000 0.0 -
13.0573 2050 0.0 -
13.3758 2100 0.0 -
13.6943 2150 0.0 -
14.0127 2200 0.0 -
14.3312 2250 0.0 -
14.6497 2300 0.0 -
14.9682 2350 0.0 -
15.2866 2400 0.0 -
15.6051 2450 0.0 -
15.9236 2500 0.0 -
16.2420 2550 0.0 -
16.5605 2600 0.0 -
16.8790 2650 0.0 -
17.1975 2700 0.0 -
17.5159 2750 0.0 -
17.8344 2800 0.0 -
18.1529 2850 0.0 -
18.4713 2900 0.0 -
18.7898 2950 0.0 -
19.1083 3000 0.0 -
19.4268 3050 0.0 -
19.7452 3100 0.0 -
20.0637 3150 0.0 -
20.3822 3200 0.0 -
20.7006 3250 0.0 -
21.0191 3300 0.0 -
21.3376 3350 0.0 -
21.6561 3400 0.0 -
21.9745 3450 0.0 -
22.2930 3500 0.0 -
22.6115 3550 0.0 -
22.9299 3600 0.0 -
23.2484 3650 0.0 -
23.5669 3700 0.0 -
23.8854 3750 0.0 -
24.2038 3800 0.0 -
24.5223 3850 0.0 -
24.8408 3900 0.0 -
25.1592 3950 0.0 -
25.4777 4000 0.0 -
25.7962 4050 0.0 -
26.1146 4100 0.0 -
26.4331 4150 0.0 -
26.7516 4200 0.0 -
27.0701 4250 0.0 -
27.3885 4300 0.0 -
27.7070 4350 0.0 -
28.0255 4400 0.0 -
28.3439 4450 0.0 -
28.6624 4500 0.0 -
28.9809 4550 0.0 -
29.2994 4600 0.0 -
29.6178 4650 0.0 -
29.9363 4700 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 

@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}
}