metadata
base_model: mini1013/master_domain
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
폴미첼 프리즈 앤 샤인 슈퍼 스프레이 250ml x 3개 단일상품 (#M)11st>헤어케어>헤어스프레이>헤어스프레이 11st > 뷰티
> 헤어케어 > 헤어스프레이
- text: >-
댕기머리 뉴골드 새치머리용 한방 칼라 크림 염색약 염색제 5호 4개 Coupang > 뷰티 > 헤어 > 염색/파마 > 염색/헤어컬러
> 한방염색제(새치);(#M)쿠팡 홈>뷰티>헤어>염색/파마>염색/헤어컬러>한방염색제(새치) Coupang > 뷰티 > 헤어 >
염색/파마 > 염색/헤어컬러 > 한방염색제(새치)
- text: >-
갸스비 스타일링 왁스 익사이팅 하드 80g MinSellAmount (#M)바디/헤어>헤어스타일링>헤어왁스 Gmarket > 뷰티 >
바디/헤어 > 헤어스타일링 > 헤어왁스
- text: >-
이지엔 쉐이킹 푸딩 헤어칼라 6.61(멋내기용-스모키 애쉬블러썸) (#M)홈>헤어케어>염색약>멋내기 염색 Naverstore >
화장품/미용 > 헤어스타일링 > 염색약
- text: >-
[웰라] 아이미 앱솔루트 셋 300ml LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 헤어스프레이 LotteOn > 뷰티
> 헤어/바디 > 헤어스타일링 > 헤어스프레이
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: accuracy
value: 0.8061538461538461
name: Accuracy
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:
- 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: mini1013/master_domain
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 7 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 |
---|---|
6 |
|
2 |
|
5 |
|
0 |
|
4 |
|
1 |
|
3 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8062 |
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_bt_top12_test")
# Run inference
preds = model("폴미첼 프리즈 앤 샤인 슈퍼 스프레이 250ml x 3개 단일상품 (#M)11st>헤어케어>헤어스프레이>헤어스프레이 11st > 뷰티 > 헤어케어 > 헤어스프레이")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 11 | 21.3486 | 60 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 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 | 0.4121 | - |
0.0914 | 50 | 0.4364 | - |
0.1828 | 100 | 0.4222 | - |
0.2742 | 150 | 0.395 | - |
0.3656 | 200 | 0.3538 | - |
0.4570 | 250 | 0.3385 | - |
0.5484 | 300 | 0.3178 | - |
0.6399 | 350 | 0.2704 | - |
0.7313 | 400 | 0.2307 | - |
0.8227 | 450 | 0.1968 | - |
0.9141 | 500 | 0.1908 | - |
1.0055 | 550 | 0.1629 | - |
1.0969 | 600 | 0.1568 | - |
1.1883 | 650 | 0.1495 | - |
1.2797 | 700 | 0.1378 | - |
1.3711 | 750 | 0.1215 | - |
1.4625 | 800 | 0.0961 | - |
1.5539 | 850 | 0.0704 | - |
1.6453 | 900 | 0.0564 | - |
1.7367 | 950 | 0.0565 | - |
1.8282 | 1000 | 0.0549 | - |
1.9196 | 1050 | 0.0489 | - |
2.0110 | 1100 | 0.0484 | - |
2.1024 | 1150 | 0.0471 | - |
2.1938 | 1200 | 0.042 | - |
2.2852 | 1250 | 0.0456 | - |
2.3766 | 1300 | 0.0329 | - |
2.4680 | 1350 | 0.0308 | - |
2.5594 | 1400 | 0.0262 | - |
2.6508 | 1450 | 0.0317 | - |
2.7422 | 1500 | 0.0267 | - |
2.8336 | 1550 | 0.0248 | - |
2.9250 | 1600 | 0.0175 | - |
3.0165 | 1650 | 0.0113 | - |
3.1079 | 1700 | 0.0111 | - |
3.1993 | 1750 | 0.0096 | - |
3.2907 | 1800 | 0.0054 | - |
3.3821 | 1850 | 0.004 | - |
3.4735 | 1900 | 0.0035 | - |
3.5649 | 1950 | 0.0015 | - |
3.6563 | 2000 | 0.0013 | - |
3.7477 | 2050 | 0.0015 | - |
3.8391 | 2100 | 0.0009 | - |
3.9305 | 2150 | 0.0009 | - |
4.0219 | 2200 | 0.0007 | - |
4.1133 | 2250 | 0.0007 | - |
4.2048 | 2300 | 0.0006 | - |
4.2962 | 2350 | 0.0011 | - |
4.3876 | 2400 | 0.0004 | - |
4.4790 | 2450 | 0.0004 | - |
4.5704 | 2500 | 0.0002 | - |
4.6618 | 2550 | 0.0001 | - |
4.7532 | 2600 | 0.0001 | - |
4.8446 | 2650 | 0.0001 | - |
4.9360 | 2700 | 0.0001 | - |
5.0274 | 2750 | 0.0001 | - |
5.1188 | 2800 | 0.0001 | - |
5.2102 | 2850 | 0.0001 | - |
5.3016 | 2900 | 0.0001 | - |
5.3931 | 2950 | 0.0001 | - |
5.4845 | 3000 | 0.0001 | - |
5.5759 | 3050 | 0.0 | - |
5.6673 | 3100 | 0.0001 | - |
5.7587 | 3150 | 0.0 | - |
5.8501 | 3200 | 0.0 | - |
5.9415 | 3250 | 0.0 | - |
6.0329 | 3300 | 0.0 | - |
6.1243 | 3350 | 0.0 | - |
6.2157 | 3400 | 0.0 | - |
6.3071 | 3450 | 0.0 | - |
6.3985 | 3500 | 0.0 | - |
6.4899 | 3550 | 0.0 | - |
6.5814 | 3600 | 0.0 | - |
6.6728 | 3650 | 0.0 | - |
6.7642 | 3700 | 0.0 | - |
6.8556 | 3750 | 0.0 | - |
6.9470 | 3800 | 0.0 | - |
7.0384 | 3850 | 0.0001 | - |
7.1298 | 3900 | 0.0 | - |
7.2212 | 3950 | 0.0 | - |
7.3126 | 4000 | 0.0 | - |
7.4040 | 4050 | 0.0 | - |
7.4954 | 4100 | 0.0 | - |
7.5868 | 4150 | 0.0 | - |
7.6782 | 4200 | 0.0 | - |
7.7697 | 4250 | 0.0 | - |
7.8611 | 4300 | 0.0 | - |
7.9525 | 4350 | 0.0 | - |
8.0439 | 4400 | 0.0 | - |
8.1353 | 4450 | 0.0 | - |
8.2267 | 4500 | 0.0 | - |
8.3181 | 4550 | 0.0 | - |
8.4095 | 4600 | 0.0002 | - |
8.5009 | 4650 | 0.0 | - |
8.5923 | 4700 | 0.0 | - |
8.6837 | 4750 | 0.0 | - |
8.7751 | 4800 | 0.0 | - |
8.8665 | 4850 | 0.0 | - |
8.9580 | 4900 | 0.0 | - |
9.0494 | 4950 | 0.0 | - |
9.1408 | 5000 | 0.0 | - |
9.2322 | 5050 | 0.0 | - |
9.3236 | 5100 | 0.0 | - |
9.4150 | 5150 | 0.0 | - |
9.5064 | 5200 | 0.0 | - |
9.5978 | 5250 | 0.0 | - |
9.6892 | 5300 | 0.0 | - |
9.7806 | 5350 | 0.0 | - |
9.8720 | 5400 | 0.0 | - |
9.9634 | 5450 | 0.0 | - |
10.0548 | 5500 | 0.0 | - |
10.1463 | 5550 | 0.0 | - |
10.2377 | 5600 | 0.0 | - |
10.3291 | 5650 | 0.0 | - |
10.4205 | 5700 | 0.0 | - |
10.5119 | 5750 | 0.0 | - |
10.6033 | 5800 | 0.0 | - |
10.6947 | 5850 | 0.0 | - |
10.7861 | 5900 | 0.0 | - |
10.8775 | 5950 | 0.0 | - |
10.9689 | 6000 | 0.0 | - |
11.0603 | 6050 | 0.0 | - |
11.1517 | 6100 | 0.0 | - |
11.2431 | 6150 | 0.0 | - |
11.3346 | 6200 | 0.0 | - |
11.4260 | 6250 | 0.0 | - |
11.5174 | 6300 | 0.0001 | - |
11.6088 | 6350 | 0.0 | - |
11.7002 | 6400 | 0.0 | - |
11.7916 | 6450 | 0.0 | - |
11.8830 | 6500 | 0.0 | - |
11.9744 | 6550 | 0.0 | - |
12.0658 | 6600 | 0.0 | - |
12.1572 | 6650 | 0.0 | - |
12.2486 | 6700 | 0.0007 | - |
12.3400 | 6750 | 0.0343 | - |
12.4314 | 6800 | 0.0285 | - |
12.5229 | 6850 | 0.0151 | - |
12.6143 | 6900 | 0.0047 | - |
12.7057 | 6950 | 0.003 | - |
12.7971 | 7000 | 0.0047 | - |
12.8885 | 7050 | 0.0027 | - |
12.9799 | 7100 | 0.0017 | - |
13.0713 | 7150 | 0.0002 | - |
13.1627 | 7200 | 0.0001 | - |
13.2541 | 7250 | 0.0004 | - |
13.3455 | 7300 | 0.0001 | - |
13.4369 | 7350 | 0.0 | - |
13.5283 | 7400 | 0.0 | - |
13.6197 | 7450 | 0.0 | - |
13.7112 | 7500 | 0.0001 | - |
13.8026 | 7550 | 0.0 | - |
13.8940 | 7600 | 0.0 | - |
13.9854 | 7650 | 0.0002 | - |
14.0768 | 7700 | 0.0002 | - |
14.1682 | 7750 | 0.0001 | - |
14.2596 | 7800 | 0.0001 | - |
14.3510 | 7850 | 0.0 | - |
14.4424 | 7900 | 0.0 | - |
14.5338 | 7950 | 0.0002 | - |
14.6252 | 8000 | 0.0 | - |
14.7166 | 8050 | 0.0 | - |
14.8080 | 8100 | 0.0 | - |
14.8995 | 8150 | 0.0 | - |
14.9909 | 8200 | 0.0 | - |
15.0823 | 8250 | 0.0 | - |
15.1737 | 8300 | 0.0 | - |
15.2651 | 8350 | 0.0 | - |
15.3565 | 8400 | 0.0 | - |
15.4479 | 8450 | 0.0 | - |
15.5393 | 8500 | 0.0 | - |
15.6307 | 8550 | 0.0 | - |
15.7221 | 8600 | 0.0 | - |
15.8135 | 8650 | 0.0 | - |
15.9049 | 8700 | 0.0 | - |
15.9963 | 8750 | 0.0001 | - |
16.0878 | 8800 | 0.0 | - |
16.1792 | 8850 | 0.0 | - |
16.2706 | 8900 | 0.0002 | - |
16.3620 | 8950 | 0.0 | - |
16.4534 | 9000 | 0.0 | - |
16.5448 | 9050 | 0.0001 | - |
16.6362 | 9100 | 0.0001 | - |
16.7276 | 9150 | 0.0001 | - |
16.8190 | 9200 | 0.0 | - |
16.9104 | 9250 | 0.0002 | - |
17.0018 | 9300 | 0.0 | - |
17.0932 | 9350 | 0.0002 | - |
17.1846 | 9400 | 0.0006 | - |
17.2761 | 9450 | 0.0002 | - |
17.3675 | 9500 | 0.0003 | - |
17.4589 | 9550 | 0.0 | - |
17.5503 | 9600 | 0.0 | - |
17.6417 | 9650 | 0.0 | - |
17.7331 | 9700 | 0.0 | - |
17.8245 | 9750 | 0.0 | - |
17.9159 | 9800 | 0.0 | - |
18.0073 | 9850 | 0.0 | - |
18.0987 | 9900 | 0.0 | - |
18.1901 | 9950 | 0.0 | - |
18.2815 | 10000 | 0.0 | - |
18.3729 | 10050 | 0.0 | - |
18.4644 | 10100 | 0.0 | - |
18.5558 | 10150 | 0.0 | - |
18.6472 | 10200 | 0.0 | - |
18.7386 | 10250 | 0.0 | - |
18.8300 | 10300 | 0.0 | - |
18.9214 | 10350 | 0.0 | - |
19.0128 | 10400 | 0.0 | - |
19.1042 | 10450 | 0.0 | - |
19.1956 | 10500 | 0.0 | - |
19.2870 | 10550 | 0.0 | - |
19.3784 | 10600 | 0.0 | - |
19.4698 | 10650 | 0.0 | - |
19.5612 | 10700 | 0.0 | - |
19.6527 | 10750 | 0.0 | - |
19.7441 | 10800 | 0.0 | - |
19.8355 | 10850 | 0.0 | - |
19.9269 | 10900 | 0.0 | - |
20.0183 | 10950 | 0.0002 | - |
20.1097 | 11000 | 0.0002 | - |
20.2011 | 11050 | 0.0 | - |
20.2925 | 11100 | 0.0 | - |
20.3839 | 11150 | 0.0 | - |
20.4753 | 11200 | 0.0 | - |
20.5667 | 11250 | 0.0 | - |
20.6581 | 11300 | 0.0 | - |
20.7495 | 11350 | 0.0 | - |
20.8410 | 11400 | 0.0 | - |
20.9324 | 11450 | 0.0 | - |
21.0238 | 11500 | 0.0 | - |
21.1152 | 11550 | 0.0 | - |
21.2066 | 11600 | 0.0 | - |
21.2980 | 11650 | 0.0 | - |
21.3894 | 11700 | 0.0 | - |
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21.5722 | 11800 | 0.0 | - |
21.6636 | 11850 | 0.0 | - |
21.7550 | 11900 | 0.0 | - |
21.8464 | 11950 | 0.0 | - |
21.9378 | 12000 | 0.0 | - |
22.0293 | 12050 | 0.0 | - |
22.1207 | 12100 | 0.0 | - |
22.2121 | 12150 | 0.0 | - |
22.3035 | 12200 | 0.0 | - |
22.3949 | 12250 | 0.0 | - |
22.4863 | 12300 | 0.0 | - |
22.5777 | 12350 | 0.0 | - |
22.6691 | 12400 | 0.0 | - |
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23.0347 | 12600 | 0.0 | - |
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23.2176 | 12700 | 0.0 | - |
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23.4004 | 12800 | 0.0 | - |
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23.7660 | 13000 | 0.0 | - |
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25.9598 | 14200 | 0.0 | - |
26.0512 | 14250 | 0.0 | - |
26.1426 | 14300 | 0.0 | - |
26.2340 | 14350 | 0.0 | - |
26.3254 | 14400 | 0.0 | - |
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26.5996 | 14550 | 0.0 | - |
26.6910 | 14600 | 0.0 | - |
26.7824 | 14650 | 0.0 | - |
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27.0567 | 14800 | 0.0 | - |
27.1481 | 14850 | 0.0 | - |
27.2395 | 14900 | 0.0 | - |
27.3309 | 14950 | 0.0 | - |
27.4223 | 15000 | 0.0 | - |
27.5137 | 15050 | 0.0 | - |
27.6051 | 15100 | 0.0 | - |
27.6965 | 15150 | 0.0 | - |
27.7879 | 15200 | 0.0 | - |
27.8793 | 15250 | 0.0 | - |
27.9707 | 15300 | 0.0 | - |
28.0622 | 15350 | 0.0 | - |
28.1536 | 15400 | 0.0 | - |
28.2450 | 15450 | 0.0 | - |
28.3364 | 15500 | 0.0 | - |
28.4278 | 15550 | 0.0 | - |
28.5192 | 15600 | 0.0 | - |
28.6106 | 15650 | 0.0 | - |
28.7020 | 15700 | 0.0 | - |
28.7934 | 15750 | 0.0 | - |
28.8848 | 15800 | 0.0 | - |
28.9762 | 15850 | 0.0 | - |
29.0676 | 15900 | 0.0 | - |
29.1590 | 15950 | 0.0 | - |
29.2505 | 16000 | 0.0 | - |
29.3419 | 16050 | 0.0 | - |
29.4333 | 16100 | 0.0 | - |
29.5247 | 16150 | 0.0 | - |
29.6161 | 16200 | 0.0 | - |
29.7075 | 16250 | 0.0 | - |
29.7989 | 16300 | 0.0 | - |
29.8903 | 16350 | 0.0 | - |
29.9817 | 16400 | 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}
}