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: 15 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 |
---|---|
8 |
|
10 |
|
11 |
|
2 |
|
0 |
|
4 |
|
14 |
|
3 |
|
13 |
|
12 |
|
9 |
|
6 |
|
1 |
|
5 |
|
7 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9225 |
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_bt4")
# Run inference
preds = model("더마비 우레아 9.8 풋 크림 80ml (#M)홈>전체상품 Naverstore > 화장품/미용 > 바디케어 > 풋케어")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 10 | 22.6203 | 90 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 40 |
8 | 50 |
9 | 50 |
10 | 50 |
11 | 50 |
12 | 50 |
13 | 50 |
14 | 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 |
---|---|---|---|
0.0009 | 1 | 0.3534 | - |
0.0432 | 50 | 0.4083 | - |
0.0864 | 100 | 0.3856 | - |
0.1296 | 150 | 0.3604 | - |
0.1729 | 200 | 0.3354 | - |
0.2161 | 250 | 0.2934 | - |
0.2593 | 300 | 0.2689 | - |
0.3025 | 350 | 0.2501 | - |
0.3457 | 400 | 0.2434 | - |
0.3889 | 450 | 0.216 | - |
0.4322 | 500 | 0.1721 | - |
0.4754 | 550 | 0.1269 | - |
0.5186 | 600 | 0.1067 | - |
0.5618 | 650 | 0.0844 | - |
0.6050 | 700 | 0.073 | - |
0.6482 | 750 | 0.0687 | - |
0.6914 | 800 | 0.0611 | - |
0.7347 | 850 | 0.0577 | - |
0.7779 | 900 | 0.0463 | - |
0.8211 | 950 | 0.0431 | - |
0.8643 | 1000 | 0.0373 | - |
0.9075 | 1050 | 0.0302 | - |
0.9507 | 1100 | 0.035 | - |
0.9939 | 1150 | 0.0311 | - |
1.0372 | 1200 | 0.0239 | - |
1.0804 | 1250 | 0.0204 | - |
1.1236 | 1300 | 0.0154 | - |
1.1668 | 1350 | 0.0088 | - |
1.2100 | 1400 | 0.0065 | - |
1.2532 | 1450 | 0.0064 | - |
1.2965 | 1500 | 0.0064 | - |
1.3397 | 1550 | 0.0055 | - |
1.3829 | 1600 | 0.0037 | - |
1.4261 | 1650 | 0.004 | - |
1.4693 | 1700 | 0.0042 | - |
1.5125 | 1750 | 0.0041 | - |
1.5557 | 1800 | 0.0056 | - |
1.5990 | 1850 | 0.0049 | - |
1.6422 | 1900 | 0.0056 | - |
1.6854 | 1950 | 0.0037 | - |
1.7286 | 2000 | 0.0048 | - |
1.7718 | 2050 | 0.005 | - |
1.8150 | 2100 | 0.0036 | - |
1.8583 | 2150 | 0.004 | - |
1.9015 | 2200 | 0.0046 | - |
1.9447 | 2250 | 0.005 | - |
1.9879 | 2300 | 0.0042 | - |
2.0311 | 2350 | 0.0036 | - |
2.0743 | 2400 | 0.0051 | - |
2.1175 | 2450 | 0.0029 | - |
2.1608 | 2500 | 0.0037 | - |
2.2040 | 2550 | 0.0072 | - |
2.2472 | 2600 | 0.0088 | - |
2.2904 | 2650 | 0.0048 | - |
2.3336 | 2700 | 0.0031 | - |
2.3768 | 2750 | 0.0035 | - |
2.4201 | 2800 | 0.0045 | - |
2.4633 | 2850 | 0.005 | - |
2.5065 | 2900 | 0.004 | - |
2.5497 | 2950 | 0.0037 | - |
2.5929 | 3000 | 0.0047 | - |
2.6361 | 3050 | 0.0047 | - |
2.6793 | 3100 | 0.0033 | - |
2.7226 | 3150 | 0.0011 | - |
2.7658 | 3200 | 0.0001 | - |
2.8090 | 3250 | 0.0001 | - |
2.8522 | 3300 | 0.0001 | - |
2.8954 | 3350 | 0.0001 | - |
2.9386 | 3400 | 0.0001 | - |
2.9818 | 3450 | 0.0001 | - |
3.0251 | 3500 | 0.0001 | - |
3.0683 | 3550 | 0.0 | - |
3.1115 | 3600 | 0.0 | - |
3.1547 | 3650 | 0.0 | - |
3.1979 | 3700 | 0.0 | - |
3.2411 | 3750 | 0.0 | - |
3.2844 | 3800 | 0.0 | - |
3.3276 | 3850 | 0.0 | - |
3.3708 | 3900 | 0.0 | - |
3.4140 | 3950 | 0.0 | - |
3.4572 | 4000 | 0.0 | - |
3.5004 | 4050 | 0.0 | - |
3.5436 | 4100 | 0.0 | - |
3.5869 | 4150 | 0.0 | - |
3.6301 | 4200 | 0.0 | - |
3.6733 | 4250 | 0.0 | - |
3.7165 | 4300 | 0.0 | - |
3.7597 | 4350 | 0.0 | - |
3.8029 | 4400 | 0.0 | - |
3.8462 | 4450 | 0.0 | - |
3.8894 | 4500 | 0.0 | - |
3.9326 | 4550 | 0.0 | - |
3.9758 | 4600 | 0.0 | - |
4.0190 | 4650 | 0.0 | - |
4.0622 | 4700 | 0.0 | - |
4.1054 | 4750 | 0.0 | - |
4.1487 | 4800 | 0.0 | - |
4.1919 | 4850 | 0.0 | - |
4.2351 | 4900 | 0.0 | - |
4.2783 | 4950 | 0.0 | - |
4.3215 | 5000 | 0.0 | - |
4.3647 | 5050 | 0.0 | - |
4.4080 | 5100 | 0.0 | - |
4.4512 | 5150 | 0.0 | - |
4.4944 | 5200 | 0.0 | - |
4.5376 | 5250 | 0.0 | - |
4.5808 | 5300 | 0.0 | - |
4.6240 | 5350 | 0.0 | - |
4.6672 | 5400 | 0.0 | - |
4.7105 | 5450 | 0.0 | - |
4.7537 | 5500 | 0.0 | - |
4.7969 | 5550 | 0.0 | - |
4.8401 | 5600 | 0.0 | - |
4.8833 | 5650 | 0.0 | - |
4.9265 | 5700 | 0.0 | - |
4.9697 | 5750 | 0.0 | - |
5.0130 | 5800 | 0.0 | - |
5.0562 | 5850 | 0.0 | - |
5.0994 | 5900 | 0.0 | - |
5.1426 | 5950 | 0.0 | - |
5.1858 | 6000 | 0.0 | - |
5.2290 | 6050 | 0.0 | - |
5.2723 | 6100 | 0.0 | - |
5.3155 | 6150 | 0.0 | - |
5.3587 | 6200 | 0.0 | - |
5.4019 | 6250 | 0.0 | - |
5.4451 | 6300 | 0.0 | - |
5.4883 | 6350 | 0.0 | - |
5.5315 | 6400 | 0.0 | - |
5.5748 | 6450 | 0.0 | - |
5.6180 | 6500 | 0.0 | - |
5.6612 | 6550 | 0.0 | - |
5.7044 | 6600 | 0.0 | - |
5.7476 | 6650 | 0.0 | - |
5.7908 | 6700 | 0.0 | - |
5.8341 | 6750 | 0.0 | - |
5.8773 | 6800 | 0.0 | - |
5.9205 | 6850 | 0.0 | - |
5.9637 | 6900 | 0.0 | - |
6.0069 | 6950 | 0.0 | - |
6.0501 | 7000 | 0.0 | - |
6.0933 | 7050 | 0.0 | - |
6.1366 | 7100 | 0.0 | - |
6.1798 | 7150 | 0.0 | - |
6.2230 | 7200 | 0.0 | - |
6.2662 | 7250 | 0.0 | - |
6.3094 | 7300 | 0.0 | - |
6.3526 | 7350 | 0.0 | - |
6.3959 | 7400 | 0.0 | - |
6.4391 | 7450 | 0.0 | - |
6.4823 | 7500 | 0.0052 | - |
6.5255 | 7550 | 0.0309 | - |
6.5687 | 7600 | 0.0065 | - |
6.6119 | 7650 | 0.0026 | - |
6.6551 | 7700 | 0.0007 | - |
6.6984 | 7750 | 0.0004 | - |
6.7416 | 7800 | 0.0001 | - |
6.7848 | 7850 | 0.0 | - |
6.8280 | 7900 | 0.0 | - |
6.8712 | 7950 | 0.0001 | - |
6.9144 | 8000 | 0.0 | - |
6.9576 | 8050 | 0.0 | - |
7.0009 | 8100 | 0.0 | - |
7.0441 | 8150 | 0.0 | - |
7.0873 | 8200 | 0.0 | - |
7.1305 | 8250 | 0.0 | - |
7.1737 | 8300 | 0.0 | - |
7.2169 | 8350 | 0.0 | - |
7.2602 | 8400 | 0.0 | - |
7.3034 | 8450 | 0.0 | - |
7.3466 | 8500 | 0.0 | - |
7.3898 | 8550 | 0.0 | - |
7.4330 | 8600 | 0.0 | - |
7.4762 | 8650 | 0.0 | - |
7.5194 | 8700 | 0.0 | - |
7.5627 | 8750 | 0.0 | - |
7.6059 | 8800 | 0.0 | - |
7.6491 | 8850 | 0.0 | - |
7.6923 | 8900 | 0.0 | - |
7.7355 | 8950 | 0.0 | - |
7.7787 | 9000 | 0.0 | - |
7.8220 | 9050 | 0.0 | - |
7.8652 | 9100 | 0.0 | - |
7.9084 | 9150 | 0.0 | - |
7.9516 | 9200 | 0.0 | - |
7.9948 | 9250 | 0.0 | - |
8.0380 | 9300 | 0.0 | - |
8.0812 | 9350 | 0.0 | - |
8.1245 | 9400 | 0.0 | - |
8.1677 | 9450 | 0.0 | - |
8.2109 | 9500 | 0.0 | - |
8.2541 | 9550 | 0.0 | - |
8.2973 | 9600 | 0.0 | - |
8.3405 | 9650 | 0.0 | - |
8.3838 | 9700 | 0.0 | - |
8.4270 | 9750 | 0.0 | - |
8.4702 | 9800 | 0.0 | - |
8.5134 | 9850 | 0.0 | - |
8.5566 | 9900 | 0.0 | - |
8.5998 | 9950 | 0.0 | - |
8.6430 | 10000 | 0.0 | - |
8.6863 | 10050 | 0.0 | - |
8.7295 | 10100 | 0.0 | - |
8.7727 | 10150 | 0.0 | - |
8.8159 | 10200 | 0.0 | - |
8.8591 | 10250 | 0.0 | - |
8.9023 | 10300 | 0.0 | - |
8.9455 | 10350 | 0.0 | - |
8.9888 | 10400 | 0.0 | - |
9.0320 | 10450 | 0.0 | - |
9.0752 | 10500 | 0.0 | - |
9.1184 | 10550 | 0.0 | - |
9.1616 | 10600 | 0.0 | - |
9.2048 | 10650 | 0.0 | - |
9.2481 | 10700 | 0.0 | - |
9.2913 | 10750 | 0.0 | - |
9.3345 | 10800 | 0.0 | - |
9.3777 | 10850 | 0.0 | - |
9.4209 | 10900 | 0.0 | - |
9.4641 | 10950 | 0.0 | - |
9.5073 | 11000 | 0.0 | - |
9.5506 | 11050 | 0.0 | - |
9.5938 | 11100 | 0.0 | - |
9.6370 | 11150 | 0.0 | - |
9.6802 | 11200 | 0.0 | - |
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9.8099 | 11350 | 0.0 | - |
9.8531 | 11400 | 0.0 | - |
9.8963 | 11450 | 0.0 | - |
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10.0259 | 11600 | 0.0 | - |
10.0691 | 11650 | 0.0 | - |
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10.1988 | 11800 | 0.0 | - |
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10.3717 | 12000 | 0.0 | - |
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10.4581 | 12100 | 0.0 | - |
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10.5445 | 12200 | 0.0 | - |
10.5877 | 12250 | 0.0 | - |
10.6309 | 12300 | 0.0 | - |
10.6742 | 12350 | 0.0 | - |
10.7174 | 12400 | 0.0 | - |
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10.8038 | 12500 | 0.0 | - |
10.8470 | 12550 | 0.0 | - |
10.8902 | 12600 | 0.0 | - |
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10.9767 | 12700 | 0.0 | - |
11.0199 | 12750 | 0.0 | - |
11.0631 | 12800 | 0.0 | - |
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11.2360 | 13000 | 0.0 | - |
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12.0138 | 13900 | 0.0 | - |
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12.1003 | 14000 | 0.0 | - |
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12.2299 | 14150 | 0.0 | - |
12.2731 | 14200 | 0.0 | - |
12.3163 | 14250 | 0.0 | - |
12.3596 | 14300 | 0.0 | - |
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13.8721 | 16050 | 0.0 | - |
13.9153 | 16100 | 0.0 | - |
13.9585 | 16150 | 0.0 | - |
14.0017 | 16200 | 0.0 | - |
14.0449 | 16250 | 0.0 | - |
14.0882 | 16300 | 0.0 | - |
14.1314 | 16350 | 0.0 | - |
14.1746 | 16400 | 0.0 | - |
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14.2610 | 16500 | 0.0 | - |
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14.3475 | 16600 | 0.0 | - |
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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}
}
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