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: >-
백화점정품 샤넬 루쥬 알뤼르 잉크 6ml 140-AMOUREUX_. (#M)쿠팡 홈>뷰티>메이크업>립 메이크업>립스틱 Coupang
> 뷰티 > 메이크업 > 립 메이크업 > 립스틱
- text: >-
더페이스샵 모노큐브 아이섀도우 2g 매트_앙 버터 (#M)화장품/향수>색조메이크업>아이섀도 Gmarket > 뷰티 > 화장품/향수 >
색조메이크업 > 아이섀도
- text: >-
3CE BLUR WATER TINT 블러 워터 틴트 FRE_SEPIA LOREAL > LotteOn > 입생로랑 > Generic >
틴트 LotteOn > 뷰티 > 메이크업 > 립메이크업 > 립틴트
- text: >-
3CE 페이스 블러쉬 ROSE BEIGE 홈>전체상품;(#M)홈>FACE>치크 Naverstore > 화장품/미용 > 색조메이크업 >
블러셔
- text: >-
섀도 듀오 4g 6호 라이커블 LotteOn > 뷰티 > 색조메이크업 > 아이메이크업 LotteOn > 뷰티 > 메이크업 >
아이메이크업 > 아이섀도우
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: 0.48148148148148145
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: 11 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 |
---|---|
0 |
|
4 |
|
8 |
|
9 |
|
6 |
|
1 |
|
3 |
|
7 |
|
10 |
|
2 |
|
5 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.4815 |
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_bt6_test_flat_top_cate")
# Run inference
preds = model("3CE 페이스 블러쉬 ROSE BEIGE 홈>전체상품;(#M)홈>FACE>치크 Naverstore > 화장품/미용 > 색조메이크업 > 블러셔")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 10 | 24.2404 | 79 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 50 |
8 | 50 |
9 | 50 |
10 | 49 |
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 |
---|---|---|---|
0.0012 | 1 | 0.4756 | - |
0.0583 | 50 | 0.447 | - |
0.1166 | 100 | 0.4629 | - |
0.1748 | 150 | 0.4327 | - |
0.2331 | 200 | 0.4182 | - |
0.2914 | 250 | 0.3863 | - |
0.3497 | 300 | 0.3492 | - |
0.4079 | 350 | 0.3277 | - |
0.4662 | 400 | 0.2987 | - |
0.5245 | 450 | 0.2729 | - |
0.5828 | 500 | 0.2637 | - |
0.6410 | 550 | 0.2554 | - |
0.6993 | 600 | 0.252 | - |
0.7576 | 650 | 0.2419 | - |
0.8159 | 700 | 0.2382 | - |
0.8741 | 750 | 0.239 | - |
0.9324 | 800 | 0.2294 | - |
0.9907 | 850 | 0.2274 | - |
1.0490 | 900 | 0.2237 | - |
1.1072 | 950 | 0.2241 | - |
1.1655 | 1000 | 0.2196 | - |
1.2238 | 1050 | 0.2164 | - |
1.2821 | 1100 | 0.2119 | - |
1.3403 | 1150 | 0.2048 | - |
1.3986 | 1200 | 0.2007 | - |
1.4569 | 1250 | 0.1969 | - |
1.5152 | 1300 | 0.1898 | - |
1.5734 | 1350 | 0.1857 | - |
1.6317 | 1400 | 0.1753 | - |
1.6900 | 1450 | 0.1703 | - |
1.7483 | 1500 | 0.1552 | - |
1.8065 | 1550 | 0.1481 | - |
1.8648 | 1600 | 0.1341 | - |
1.9231 | 1650 | 0.1254 | - |
1.9814 | 1700 | 0.1077 | - |
2.0396 | 1750 | 0.0895 | - |
2.0979 | 1800 | 0.0806 | - |
2.1562 | 1850 | 0.0674 | - |
2.2145 | 1900 | 0.0618 | - |
2.2727 | 1950 | 0.056 | - |
2.3310 | 2000 | 0.0549 | - |
2.3893 | 2050 | 0.0492 | - |
2.4476 | 2100 | 0.0438 | - |
2.5058 | 2150 | 0.0394 | - |
2.5641 | 2200 | 0.0395 | - |
2.6224 | 2250 | 0.0358 | - |
2.6807 | 2300 | 0.0373 | - |
2.7389 | 2350 | 0.0303 | - |
2.7972 | 2400 | 0.0321 | - |
2.8555 | 2450 | 0.0267 | - |
2.9138 | 2500 | 0.029 | - |
2.9720 | 2550 | 0.0314 | - |
3.0303 | 2600 | 0.031 | - |
3.0886 | 2650 | 0.019 | - |
3.1469 | 2700 | 0.02 | - |
3.2051 | 2750 | 0.0223 | - |
3.2634 | 2800 | 0.0206 | - |
3.3217 | 2850 | 0.0173 | - |
3.3800 | 2900 | 0.016 | - |
3.4382 | 2950 | 0.0181 | - |
3.4965 | 3000 | 0.0102 | - |
3.5548 | 3050 | 0.0078 | - |
3.6131 | 3100 | 0.0107 | - |
3.6713 | 3150 | 0.0094 | - |
3.7296 | 3200 | 0.0089 | - |
3.7879 | 3250 | 0.0097 | - |
3.8462 | 3300 | 0.0094 | - |
3.9044 | 3350 | 0.0111 | - |
3.9627 | 3400 | 0.0102 | - |
4.0210 | 3450 | 0.0091 | - |
4.0793 | 3500 | 0.0082 | - |
4.1375 | 3550 | 0.0048 | - |
4.1958 | 3600 | 0.0022 | - |
4.2541 | 3650 | 0.0007 | - |
4.3124 | 3700 | 0.0007 | - |
4.3706 | 3750 | 0.0012 | - |
4.4289 | 3800 | 0.0009 | - |
4.4872 | 3850 | 0.0006 | - |
4.5455 | 3900 | 0.0002 | - |
4.6037 | 3950 | 0.0002 | - |
4.6620 | 4000 | 0.0002 | - |
4.7203 | 4050 | 0.0002 | - |
4.7786 | 4100 | 0.0002 | - |
4.8368 | 4150 | 0.0001 | - |
4.8951 | 4200 | 0.0001 | - |
4.9534 | 4250 | 0.0001 | - |
5.0117 | 4300 | 0.0001 | - |
5.0699 | 4350 | 0.0001 | - |
5.1282 | 4400 | 0.0001 | - |
5.1865 | 4450 | 0.0001 | - |
5.2448 | 4500 | 0.0001 | - |
5.3030 | 4550 | 0.001 | - |
5.3613 | 4600 | 0.0003 | - |
5.4196 | 4650 | 0.0005 | - |
5.4779 | 4700 | 0.0014 | - |
5.5361 | 4750 | 0.0005 | - |
5.5944 | 4800 | 0.0016 | - |
5.6527 | 4850 | 0.0007 | - |
5.7110 | 4900 | 0.0003 | - |
5.7692 | 4950 | 0.0001 | - |
5.8275 | 5000 | 0.0005 | - |
5.8858 | 5050 | 0.0004 | - |
5.9441 | 5100 | 0.0003 | - |
6.0023 | 5150 | 0.0011 | - |
6.0606 | 5200 | 0.0001 | - |
6.1189 | 5250 | 0.0001 | - |
6.1772 | 5300 | 0.0001 | - |
6.2354 | 5350 | 0.0001 | - |
6.2937 | 5400 | 0.0002 | - |
6.3520 | 5450 | 0.0004 | - |
6.4103 | 5500 | 0.0009 | - |
6.4685 | 5550 | 0.0002 | - |
6.5268 | 5600 | 0.0001 | - |
6.5851 | 5650 | 0.0 | - |
6.6434 | 5700 | 0.0 | - |
6.7016 | 5750 | 0.0 | - |
6.7599 | 5800 | 0.0 | - |
6.8182 | 5850 | 0.0001 | - |
6.8765 | 5900 | 0.0006 | - |
6.9347 | 5950 | 0.0008 | - |
6.9930 | 6000 | 0.0013 | - |
7.0513 | 6050 | 0.0015 | - |
7.1096 | 6100 | 0.0007 | - |
7.1678 | 6150 | 0.003 | - |
7.2261 | 6200 | 0.0031 | - |
7.2844 | 6250 | 0.0013 | - |
7.3427 | 6300 | 0.0019 | - |
7.4009 | 6350 | 0.0025 | - |
7.4592 | 6400 | 0.0009 | - |
7.5175 | 6450 | 0.0008 | - |
7.5758 | 6500 | 0.0001 | - |
7.6340 | 6550 | 0.0001 | - |
7.6923 | 6600 | 0.0001 | - |
7.7506 | 6650 | 0.0 | - |
7.8089 | 6700 | 0.0 | - |
7.8671 | 6750 | 0.0 | - |
7.9254 | 6800 | 0.0 | - |
7.9837 | 6850 | 0.0 | - |
8.0420 | 6900 | 0.0 | - |
8.1002 | 6950 | 0.0 | - |
8.1585 | 7000 | 0.0 | - |
8.2168 | 7050 | 0.0 | - |
8.2751 | 7100 | 0.0 | - |
8.3333 | 7150 | 0.0 | - |
8.3916 | 7200 | 0.0 | - |
8.4499 | 7250 | 0.0 | - |
8.5082 | 7300 | 0.0 | - |
8.5664 | 7350 | 0.0 | - |
8.6247 | 7400 | 0.0 | - |
8.6830 | 7450 | 0.0 | - |
8.7413 | 7500 | 0.0 | - |
8.7995 | 7550 | 0.0 | - |
8.8578 | 7600 | 0.0 | - |
8.9161 | 7650 | 0.0 | - |
8.9744 | 7700 | 0.0 | - |
9.0326 | 7750 | 0.0 | - |
9.0909 | 7800 | 0.0 | - |
9.1492 | 7850 | 0.0 | - |
9.2075 | 7900 | 0.0 | - |
9.2657 | 7950 | 0.0 | - |
9.3240 | 8000 | 0.0 | - |
9.3823 | 8050 | 0.0 | - |
9.4406 | 8100 | 0.0 | - |
9.4988 | 8150 | 0.0 | - |
9.5571 | 8200 | 0.0 | - |
9.6154 | 8250 | 0.0 | - |
9.6737 | 8300 | 0.0 | - |
9.7319 | 8350 | 0.0 | - |
9.7902 | 8400 | 0.0 | - |
9.8485 | 8450 | 0.0 | - |
9.9068 | 8500 | 0.0 | - |
9.9650 | 8550 | 0.0 | - |
10.0233 | 8600 | 0.0 | - |
10.0816 | 8650 | 0.0001 | - |
10.1399 | 8700 | 0.0036 | - |
10.1981 | 8750 | 0.0148 | - |
10.2564 | 8800 | 0.0142 | - |
10.3147 | 8850 | 0.0132 | - |
10.3730 | 8900 | 0.0116 | - |
10.4312 | 8950 | 0.0041 | - |
10.4895 | 9000 | 0.0005 | - |
10.5478 | 9050 | 0.0001 | - |
10.6061 | 9100 | 0.0003 | - |
10.6643 | 9150 | 0.0003 | - |
10.7226 | 9200 | 0.0002 | - |
10.7809 | 9250 | 0.0001 | - |
10.8392 | 9300 | 0.0003 | - |
10.8974 | 9350 | 0.0 | - |
10.9557 | 9400 | 0.0 | - |
11.0140 | 9450 | 0.0001 | - |
11.0723 | 9500 | 0.0 | - |
11.1305 | 9550 | 0.0 | - |
11.1888 | 9600 | 0.0 | - |
11.2471 | 9650 | 0.0 | - |
11.3054 | 9700 | 0.0 | - |
11.3636 | 9750 | 0.0 | - |
11.4219 | 9800 | 0.0 | - |
11.4802 | 9850 | 0.0007 | - |
11.5385 | 9900 | 0.0001 | - |
11.5967 | 9950 | 0.0002 | - |
11.6550 | 10000 | 0.0014 | - |
11.7133 | 10050 | 0.0006 | - |
11.7716 | 10100 | 0.0003 | - |
11.8298 | 10150 | 0.0003 | - |
11.8881 | 10200 | 0.0 | - |
11.9464 | 10250 | 0.0 | - |
12.0047 | 10300 | 0.0 | - |
12.0629 | 10350 | 0.0 | - |
12.1212 | 10400 | 0.0 | - |
12.1795 | 10450 | 0.0 | - |
12.2378 | 10500 | 0.002 | - |
12.2960 | 10550 | 0.0005 | - |
12.3543 | 10600 | 0.0002 | - |
12.4126 | 10650 | 0.0 | - |
12.4709 | 10700 | 0.0 | - |
12.5291 | 10750 | 0.0 | - |
12.5874 | 10800 | 0.0 | - |
12.6457 | 10850 | 0.0002 | - |
12.7040 | 10900 | 0.0 | - |
12.7622 | 10950 | 0.0 | - |
12.8205 | 11000 | 0.0 | - |
12.8788 | 11050 | 0.0 | - |
12.9371 | 11100 | 0.0 | - |
12.9953 | 11150 | 0.0 | - |
13.0536 | 11200 | 0.0001 | - |
13.1119 | 11250 | 0.0 | - |
13.1702 | 11300 | 0.0005 | - |
13.2284 | 11350 | 0.0008 | - |
13.2867 | 11400 | 0.0002 | - |
13.3450 | 11450 | 0.0005 | - |
13.4033 | 11500 | 0.0001 | - |
13.4615 | 11550 | 0.0 | - |
13.5198 | 11600 | 0.0 | - |
13.5781 | 11650 | 0.0 | - |
13.6364 | 11700 | 0.0 | - |
13.6946 | 11750 | 0.0 | - |
13.7529 | 11800 | 0.0 | - |
13.8112 | 11850 | 0.0 | - |
13.8695 | 11900 | 0.0 | - |
13.9277 | 11950 | 0.0 | - |
13.9860 | 12000 | 0.0002 | - |
14.0443 | 12050 | 0.0009 | - |
14.1026 | 12100 | 0.0 | - |
14.1608 | 12150 | 0.0 | - |
14.2191 | 12200 | 0.0 | - |
14.2774 | 12250 | 0.0 | - |
14.3357 | 12300 | 0.0 | - |
14.3939 | 12350 | 0.0 | - |
14.4522 | 12400 | 0.0 | - |
14.5105 | 12450 | 0.0 | - |
14.5688 | 12500 | 0.0 | - |
14.6270 | 12550 | 0.0 | - |
14.6853 | 12600 | 0.0 | - |
14.7436 | 12650 | 0.0 | - |
14.8019 | 12700 | 0.0 | - |
14.8601 | 12750 | 0.0 | - |
14.9184 | 12800 | 0.0 | - |
14.9767 | 12850 | 0.0 | - |
15.0350 | 12900 | 0.0 | - |
15.0932 | 12950 | 0.0 | - |
15.1515 | 13000 | 0.0 | - |
15.2098 | 13050 | 0.0 | - |
15.2681 | 13100 | 0.0 | - |
15.3263 | 13150 | 0.0 | - |
15.3846 | 13200 | 0.0 | - |
15.4429 | 13250 | 0.0 | - |
15.5012 | 13300 | 0.0 | - |
15.5594 | 13350 | 0.0 | - |
15.6177 | 13400 | 0.0 | - |
15.6760 | 13450 | 0.0 | - |
15.7343 | 13500 | 0.0 | - |
15.7925 | 13550 | 0.0 | - |
15.8508 | 13600 | 0.0 | - |
15.9091 | 13650 | 0.0 | - |
15.9674 | 13700 | 0.0 | - |
16.0256 | 13750 | 0.0 | - |
16.0839 | 13800 | 0.0 | - |
16.1422 | 13850 | 0.0 | - |
16.2005 | 13900 | 0.0 | - |
16.2587 | 13950 | 0.0 | - |
16.3170 | 14000 | 0.0 | - |
16.3753 | 14050 | 0.0 | - |
16.4336 | 14100 | 0.0 | - |
16.4918 | 14150 | 0.0 | - |
16.5501 | 14200 | 0.0 | - |
16.6084 | 14250 | 0.0 | - |
16.6667 | 14300 | 0.0 | - |
16.7249 | 14350 | 0.0 | - |
16.7832 | 14400 | 0.0 | - |
16.8415 | 14450 | 0.0 | - |
16.8998 | 14500 | 0.0 | - |
16.9580 | 14550 | 0.0 | - |
17.0163 | 14600 | 0.0 | - |
17.0746 | 14650 | 0.0 | - |
17.1329 | 14700 | 0.0 | - |
17.1911 | 14750 | 0.0 | - |
17.2494 | 14800 | 0.0 | - |
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17.3660 | 14900 | 0.0 | - |
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17.4825 | 15000 | 0.0 | - |
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17.6573 | 15150 | 0.0 | - |
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17.7739 | 15250 | 0.0 | - |
17.8322 | 15300 | 0.0 | - |
17.8904 | 15350 | 0.0 | - |
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18.0070 | 15450 | 0.0 | - |
18.0653 | 15500 | 0.0 | - |
18.1235 | 15550 | 0.0 | - |
18.1818 | 15600 | 0.0 | - |
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18.2984 | 15700 | 0.0 | - |
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18.4149 | 15800 | 0.0 | - |
18.4732 | 15850 | 0.0 | - |
18.5315 | 15900 | 0.0 | - |
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18.6480 | 16000 | 0.0 | - |
18.7063 | 16050 | 0.0 | - |
18.7646 | 16100 | 0.0 | - |
18.8228 | 16150 | 0.0 | - |
18.8811 | 16200 | 0.0 | - |
18.9394 | 16250 | 0.0 | - |
18.9977 | 16300 | 0.0 | - |
19.0559 | 16350 | 0.0 | - |
19.1142 | 16400 | 0.0 | - |
19.1725 | 16450 | 0.0 | - |
19.2308 | 16500 | 0.0 | - |
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19.3473 | 16600 | 0.0 | - |
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21.6200 | 18550 | 0.0 | - |
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23.5431 | 20200 | 0.0 | - |
23.6014 | 20250 | 0.0 | - |
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29.8951 | 25650 | 0.0 | - |
29.9534 | 25700 | 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}
}