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: >-
트위저맨 포인트 트위저 Pretty in Pink (#M)홈>화장품/미용>뷰티소품>페이스소품>기타페이스소품 Naverstore >
화장품/미용 > 뷰티소품 > 페이스소품 > 기타페이스소품
- text: >-
에스쁘아 에어 퍼프 5개입 소프트 터치 에어퍼프 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프 LotteOn >
뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬
- text: >-
더툴랩 더스타일 래쉬 - 리얼(TSL001) x 1개 리얼(TSL001) × 1개 LotteOn > 뷰티 > 뷰티기기/소품 >
아이/브로우소품 > 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리
- text: >-
미용재료/셀프파마/롯드/헤어롤/미용용품/파지/귀마개/염색볼/집게핀/샤워캡/헤어밴드 41.다용도 공병 2개
홈>펌,염색,미용소도구;홈>파마용품;(#M)홈>파마 소도구>파마용품 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 >
기타헤어소품
- text: >-
에스쁘아 비글로우 에어 퍼프 5개입(22AD) (#M)홈>화장품/미용>뷰티소품>페이스소품>기타페이스소품 Naverstore >
화장품/미용 > 뷰티소품 > 페이스소품 > 기타페이스소품
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: 0.9419292632686155
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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- 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: 8 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
7 |
|
3 |
|
6 |
|
0 |
|
5 |
|
1 |
|
2 |
|
4 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9419 |
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_item_top_bt6")
# Run inference
preds = model("에스쁘아 에어 퍼프 5개입 소프트 터치 에어퍼프 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 12 | 22.0313 | 72 |
Label | Training Sample Count |
---|---|
0 | 1 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 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 |
---|---|---|---|
0.0018 | 1 | 0.4099 | - |
0.0911 | 50 | 0.3973 | - |
0.1821 | 100 | 0.3456 | - |
0.2732 | 150 | 0.2947 | - |
0.3643 | 200 | 0.2369 | - |
0.4554 | 250 | 0.1705 | - |
0.5464 | 300 | 0.107 | - |
0.6375 | 350 | 0.0696 | - |
0.7286 | 400 | 0.0494 | - |
0.8197 | 450 | 0.0488 | - |
0.9107 | 500 | 0.0307 | - |
1.0018 | 550 | 0.0259 | - |
1.0929 | 600 | 0.0247 | - |
1.1840 | 650 | 0.022 | - |
1.2750 | 700 | 0.0215 | - |
1.3661 | 750 | 0.005 | - |
1.4572 | 800 | 0.0007 | - |
1.5483 | 850 | 0.0004 | - |
1.6393 | 900 | 0.0002 | - |
1.7304 | 950 | 0.0001 | - |
1.8215 | 1000 | 0.0001 | - |
1.9126 | 1050 | 0.0001 | - |
2.0036 | 1100 | 0.0001 | - |
2.0947 | 1150 | 0.0001 | - |
2.1858 | 1200 | 0.0001 | - |
2.2769 | 1250 | 0.0 | - |
2.3679 | 1300 | 0.0 | - |
2.4590 | 1350 | 0.0 | - |
2.5501 | 1400 | 0.0 | - |
2.6412 | 1450 | 0.0 | - |
2.7322 | 1500 | 0.0 | - |
2.8233 | 1550 | 0.0 | - |
2.9144 | 1600 | 0.0 | - |
3.0055 | 1650 | 0.0 | - |
3.0965 | 1700 | 0.0 | - |
3.1876 | 1750 | 0.0 | - |
3.2787 | 1800 | 0.0 | - |
3.3698 | 1850 | 0.0 | - |
3.4608 | 1900 | 0.0 | - |
3.5519 | 1950 | 0.0 | - |
3.6430 | 2000 | 0.0 | - |
3.7341 | 2050 | 0.0 | - |
3.8251 | 2100 | 0.0 | - |
3.9162 | 2150 | 0.0 | - |
4.0073 | 2200 | 0.0 | - |
4.0984 | 2250 | 0.0 | - |
4.1894 | 2300 | 0.0 | - |
4.2805 | 2350 | 0.0 | - |
4.3716 | 2400 | 0.0 | - |
4.4627 | 2450 | 0.0 | - |
4.5537 | 2500 | 0.0 | - |
4.6448 | 2550 | 0.0 | - |
4.7359 | 2600 | 0.0 | - |
4.8270 | 2650 | 0.0 | - |
4.9180 | 2700 | 0.0 | - |
5.0091 | 2750 | 0.0 | - |
5.1002 | 2800 | 0.0 | - |
5.1913 | 2850 | 0.0 | - |
5.2823 | 2900 | 0.0 | - |
5.3734 | 2950 | 0.0 | - |
5.4645 | 3000 | 0.0 | - |
5.5556 | 3050 | 0.0 | - |
5.6466 | 3100 | 0.0 | - |
5.7377 | 3150 | 0.0 | - |
5.8288 | 3200 | 0.0 | - |
5.9199 | 3250 | 0.0 | - |
6.0109 | 3300 | 0.0 | - |
6.1020 | 3350 | 0.0 | - |
6.1931 | 3400 | 0.0 | - |
6.2842 | 3450 | 0.0 | - |
6.3752 | 3500 | 0.0 | - |
6.4663 | 3550 | 0.0 | - |
6.5574 | 3600 | 0.0 | - |
6.6485 | 3650 | 0.0 | - |
6.7395 | 3700 | 0.0 | - |
6.8306 | 3750 | 0.0 | - |
6.9217 | 3800 | 0.0 | - |
7.0128 | 3850 | 0.0 | - |
7.1038 | 3900 | 0.0 | - |
7.1949 | 3950 | 0.0 | - |
7.2860 | 4000 | 0.0 | - |
7.3770 | 4050 | 0.0 | - |
7.4681 | 4100 | 0.0 | - |
7.5592 | 4150 | 0.0 | - |
7.6503 | 4200 | 0.0 | - |
7.7413 | 4250 | 0.0 | - |
7.8324 | 4300 | 0.0 | - |
7.9235 | 4350 | 0.0 | - |
8.0146 | 4400 | 0.0 | - |
8.1056 | 4450 | 0.0 | - |
8.1967 | 4500 | 0.0 | - |
8.2878 | 4550 | 0.0 | - |
8.3789 | 4600 | 0.0 | - |
8.4699 | 4650 | 0.0 | - |
8.5610 | 4700 | 0.0 | - |
8.6521 | 4750 | 0.0 | - |
8.7432 | 4800 | 0.0 | - |
8.8342 | 4850 | 0.0 | - |
8.9253 | 4900 | 0.0 | - |
9.0164 | 4950 | 0.0 | - |
9.1075 | 5000 | 0.0 | - |
9.1985 | 5050 | 0.0 | - |
9.2896 | 5100 | 0.0 | - |
9.3807 | 5150 | 0.0 | - |
9.4718 | 5200 | 0.0 | - |
9.5628 | 5250 | 0.0 | - |
9.6539 | 5300 | 0.0 | - |
9.7450 | 5350 | 0.0 | - |
9.8361 | 5400 | 0.0 | - |
9.9271 | 5450 | 0.0 | - |
10.0182 | 5500 | 0.0 | - |
10.1093 | 5550 | 0.0 | - |
10.2004 | 5600 | 0.0 | - |
10.2914 | 5650 | 0.0 | - |
10.3825 | 5700 | 0.0 | - |
10.4736 | 5750 | 0.0 | - |
10.5647 | 5800 | 0.0 | - |
10.6557 | 5850 | 0.0 | - |
10.7468 | 5900 | 0.0 | - |
10.8379 | 5950 | 0.0 | - |
10.9290 | 6000 | 0.0 | - |
11.0200 | 6050 | 0.0 | - |
11.1111 | 6100 | 0.0 | - |
11.2022 | 6150 | 0.0 | - |
11.2933 | 6200 | 0.0 | - |
11.3843 | 6250 | 0.0 | - |
11.4754 | 6300 | 0.0 | - |
11.5665 | 6350 | 0.0 | - |
11.6576 | 6400 | 0.0 | - |
11.7486 | 6450 | 0.0 | - |
11.8397 | 6500 | 0.0 | - |
11.9308 | 6550 | 0.0 | - |
12.0219 | 6600 | 0.0 | - |
12.1129 | 6650 | 0.0 | - |
12.2040 | 6700 | 0.0 | - |
12.2951 | 6750 | 0.0 | - |
12.3862 | 6800 | 0.0 | - |
12.4772 | 6850 | 0.0 | - |
12.5683 | 6900 | 0.0 | - |
12.6594 | 6950 | 0.0 | - |
12.7505 | 7000 | 0.0 | - |
12.8415 | 7050 | 0.0 | - |
12.9326 | 7100 | 0.0 | - |
13.0237 | 7150 | 0.0 | - |
13.1148 | 7200 | 0.0 | - |
13.2058 | 7250 | 0.0 | - |
13.2969 | 7300 | 0.0 | - |
13.3880 | 7350 | 0.0 | - |
13.4791 | 7400 | 0.0 | - |
13.5701 | 7450 | 0.0 | - |
13.6612 | 7500 | 0.0 | - |
13.7523 | 7550 | 0.0 | - |
13.8434 | 7600 | 0.0 | - |
13.9344 | 7650 | 0.0 | - |
14.0255 | 7700 | 0.0 | - |
14.1166 | 7750 | 0.0 | - |
14.2077 | 7800 | 0.0 | - |
14.2987 | 7850 | 0.0 | - |
14.3898 | 7900 | 0.0 | - |
14.4809 | 7950 | 0.0 | - |
14.5719 | 8000 | 0.0 | - |
14.6630 | 8050 | 0.0 | - |
14.7541 | 8100 | 0.0 | - |
14.8452 | 8150 | 0.0 | - |
14.9362 | 8200 | 0.0 | - |
15.0273 | 8250 | 0.0 | - |
15.1184 | 8300 | 0.0 | - |
15.2095 | 8350 | 0.0 | - |
15.3005 | 8400 | 0.0 | - |
15.3916 | 8450 | 0.0 | - |
15.4827 | 8500 | 0.0 | - |
15.5738 | 8550 | 0.012 | - |
15.6648 | 8600 | 0.0012 | - |
15.7559 | 8650 | 0.0003 | - |
15.8470 | 8700 | 0.0 | - |
15.9381 | 8750 | 0.0 | - |
16.0291 | 8800 | 0.0 | - |
16.1202 | 8850 | 0.0 | - |
16.2113 | 8900 | 0.0 | - |
16.3024 | 8950 | 0.0 | - |
16.3934 | 9000 | 0.0 | - |
16.4845 | 9050 | 0.0 | - |
16.5756 | 9100 | 0.0 | - |
16.6667 | 9150 | 0.0 | - |
16.7577 | 9200 | 0.0 | - |
16.8488 | 9250 | 0.0 | - |
16.9399 | 9300 | 0.0 | - |
17.0310 | 9350 | 0.0 | - |
17.1220 | 9400 | 0.0 | - |
17.2131 | 9450 | 0.0 | - |
17.3042 | 9500 | 0.0 | - |
17.3953 | 9550 | 0.0 | - |
17.4863 | 9600 | 0.0 | - |
17.5774 | 9650 | 0.0 | - |
17.6685 | 9700 | 0.0 | - |
17.7596 | 9750 | 0.0 | - |
17.8506 | 9800 | 0.0 | - |
17.9417 | 9850 | 0.0 | - |
18.0328 | 9900 | 0.0 | - |
18.1239 | 9950 | 0.0 | - |
18.2149 | 10000 | 0.0 | - |
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18.7614 | 10300 | 0.0 | - |
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20.0364 | 11000 | 0.0 | - |
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20.2186 | 11100 | 0.0 | - |
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25.7741 | 14150 | 0.0 | - |
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25.9563 | 14250 | 0.0 | - |
26.0474 | 14300 | 0.0 | - |
26.1384 | 14350 | 0.0 | - |
26.2295 | 14400 | 0.0 | - |
26.3206 | 14450 | 0.0 | - |
26.4117 | 14500 | 0.0 | - |
26.5027 | 14550 | 0.0 | - |
26.5938 | 14600 | 0.0 | - |
26.6849 | 14650 | 0.0 | - |
26.7760 | 14700 | 0.0 | - |
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26.9581 | 14800 | 0.0 | - |
27.0492 | 14850 | 0.0 | - |
27.1403 | 14900 | 0.0 | - |
27.2313 | 14950 | 0.0 | - |
27.3224 | 15000 | 0.0 | - |
27.4135 | 15050 | 0.0 | - |
27.5046 | 15100 | 0.0 | - |
27.5956 | 15150 | 0.0 | - |
27.6867 | 15200 | 0.0 | - |
27.7778 | 15250 | 0.0 | - |
27.8689 | 15300 | 0.0 | - |
27.9599 | 15350 | 0.0 | - |
28.0510 | 15400 | 0.0 | - |
28.1421 | 15450 | 0.0 | - |
28.2332 | 15500 | 0.0 | - |
28.3242 | 15550 | 0.0 | - |
28.4153 | 15600 | 0.0 | - |
28.5064 | 15650 | 0.0 | - |
28.5974 | 15700 | 0.0 | - |
28.6885 | 15750 | 0.0 | - |
28.7796 | 15800 | 0.0 | - |
28.8707 | 15850 | 0.0 | - |
28.9617 | 15900 | 0.0 | - |
29.0528 | 15950 | 0.0 | - |
29.1439 | 16000 | 0.0 | - |
29.2350 | 16050 | 0.0 | - |
29.3260 | 16100 | 0.0 | - |
29.4171 | 16150 | 0.0 | - |
29.5082 | 16200 | 0.0 | - |
29.5993 | 16250 | 0.0 | - |
29.6903 | 16300 | 0.0 | - |
29.7814 | 16350 | 0.0 | - |
29.8725 | 16400 | 0.0 | - |
29.9636 | 16450 | 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}
}