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: 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 |
---|---|
0 |
|
10 |
|
4 |
|
13 |
|
7 |
|
14 |
|
1 |
|
3 |
|
6 |
|
11 |
|
12 |
|
9 |
|
5 |
|
8 |
|
2 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8482 |
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_bt3_test_flat_top_cate")
# Run inference
preds = model("에스테소피 스크럽 솔트 솝 진저 1kg (#M)11st>바디케어>바디스크럽>바디스크럽 11st > 뷰티 > 바디케어 > 바디스크럽")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 11 | 21.6635 | 51 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 46 |
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 | 0.421 | - |
0.0429 | 50 | 0.4469 | - |
0.0858 | 100 | 0.4667 | - |
0.1286 | 150 | 0.4451 | - |
0.1715 | 200 | 0.4292 | - |
0.2144 | 250 | 0.4105 | - |
0.2573 | 300 | 0.4006 | - |
0.3002 | 350 | 0.3816 | - |
0.3431 | 400 | 0.3448 | - |
0.3859 | 450 | 0.3177 | - |
0.4288 | 500 | 0.2957 | - |
0.4717 | 550 | 0.2719 | - |
0.5146 | 600 | 0.2574 | - |
0.5575 | 650 | 0.2516 | - |
0.6003 | 700 | 0.2601 | - |
0.6432 | 750 | 0.2533 | - |
0.6861 | 800 | 0.2498 | - |
0.7290 | 850 | 0.2401 | - |
0.7719 | 900 | 0.2253 | - |
0.8148 | 950 | 0.2273 | - |
0.8576 | 1000 | 0.223 | - |
0.9005 | 1050 | 0.22 | - |
0.9434 | 1100 | 0.2089 | - |
0.9863 | 1150 | 0.2111 | - |
1.0292 | 1200 | 0.2048 | - |
1.0720 | 1250 | 0.2072 | - |
1.1149 | 1300 | 0.1999 | - |
1.1578 | 1350 | 0.1977 | - |
1.2007 | 1400 | 0.1938 | - |
1.2436 | 1450 | 0.1805 | - |
1.2864 | 1500 | 0.1769 | - |
1.3293 | 1550 | 0.1764 | - |
1.3722 | 1600 | 0.1716 | - |
1.4151 | 1650 | 0.1635 | - |
1.4580 | 1700 | 0.1529 | - |
1.5009 | 1750 | 0.1563 | - |
1.5437 | 1800 | 0.148 | - |
1.5866 | 1850 | 0.1465 | - |
1.6295 | 1900 | 0.1393 | - |
1.6724 | 1950 | 0.1278 | - |
1.7153 | 2000 | 0.1262 | - |
1.7581 | 2050 | 0.12 | - |
1.8010 | 2100 | 0.1123 | - |
1.8439 | 2150 | 0.1051 | - |
1.8868 | 2200 | 0.0968 | - |
1.9297 | 2250 | 0.0902 | - |
1.9726 | 2300 | 0.0843 | - |
2.0154 | 2350 | 0.0784 | - |
2.0583 | 2400 | 0.0698 | - |
2.1012 | 2450 | 0.0671 | - |
2.1441 | 2500 | 0.0605 | - |
2.1870 | 2550 | 0.0601 | - |
2.2298 | 2600 | 0.0494 | - |
2.2727 | 2650 | 0.0484 | - |
2.3156 | 2700 | 0.0442 | - |
2.3585 | 2750 | 0.0376 | - |
2.4014 | 2800 | 0.0356 | - |
2.4443 | 2850 | 0.0308 | - |
2.4871 | 2900 | 0.0313 | - |
2.5300 | 2950 | 0.0321 | - |
2.5729 | 3000 | 0.0279 | - |
2.6158 | 3050 | 0.0293 | - |
2.6587 | 3100 | 0.0304 | - |
2.7015 | 3150 | 0.0211 | - |
2.7444 | 3200 | 0.0233 | - |
2.7873 | 3250 | 0.0204 | - |
2.8302 | 3300 | 0.0177 | - |
2.8731 | 3350 | 0.0181 | - |
2.9160 | 3400 | 0.0183 | - |
2.9588 | 3450 | 0.0145 | - |
3.0017 | 3500 | 0.0163 | - |
3.0446 | 3550 | 0.0145 | - |
3.0875 | 3600 | 0.0131 | - |
3.1304 | 3650 | 0.0113 | - |
3.1732 | 3700 | 0.0136 | - |
3.2161 | 3750 | 0.012 | - |
3.2590 | 3800 | 0.0109 | - |
3.3019 | 3850 | 0.011 | - |
3.3448 | 3900 | 0.0113 | - |
3.3877 | 3950 | 0.0105 | - |
3.4305 | 4000 | 0.0095 | - |
3.4734 | 4050 | 0.008 | - |
3.5163 | 4100 | 0.0072 | - |
3.5592 | 4150 | 0.0077 | - |
3.6021 | 4200 | 0.0057 | - |
3.6449 | 4250 | 0.0056 | - |
3.6878 | 4300 | 0.0061 | - |
3.7307 | 4350 | 0.004 | - |
3.7736 | 4400 | 0.0049 | - |
3.8165 | 4450 | 0.0041 | - |
3.8593 | 4500 | 0.0028 | - |
3.9022 | 4550 | 0.002 | - |
3.9451 | 4600 | 0.0015 | - |
3.9880 | 4650 | 0.0012 | - |
4.0309 | 4700 | 0.0011 | - |
4.0738 | 4750 | 0.0021 | - |
4.1166 | 4800 | 0.0014 | - |
4.1595 | 4850 | 0.0006 | - |
4.2024 | 4900 | 0.0008 | - |
4.2453 | 4950 | 0.0006 | - |
4.2882 | 5000 | 0.0005 | - |
4.3310 | 5050 | 0.0003 | - |
4.3739 | 5100 | 0.0003 | - |
4.4168 | 5150 | 0.0002 | - |
4.4597 | 5200 | 0.0002 | - |
4.5026 | 5250 | 0.0002 | - |
4.5455 | 5300 | 0.0002 | - |
4.5883 | 5350 | 0.0002 | - |
4.6312 | 5400 | 0.0002 | - |
4.6741 | 5450 | 0.0003 | - |
4.7170 | 5500 | 0.0001 | - |
4.7599 | 5550 | 0.0001 | - |
4.8027 | 5600 | 0.0001 | - |
4.8456 | 5650 | 0.0001 | - |
4.8885 | 5700 | 0.0001 | - |
4.9314 | 5750 | 0.0001 | - |
4.9743 | 5800 | 0.0001 | - |
5.0172 | 5850 | 0.0001 | - |
5.0600 | 5900 | 0.0001 | - |
5.1029 | 5950 | 0.0001 | - |
5.1458 | 6000 | 0.0001 | - |
5.1887 | 6050 | 0.0001 | - |
5.2316 | 6100 | 0.0001 | - |
5.2744 | 6150 | 0.0001 | - |
5.3173 | 6200 | 0.0002 | - |
5.3602 | 6250 | 0.0001 | - |
5.4031 | 6300 | 0.0001 | - |
5.4460 | 6350 | 0.0001 | - |
5.4889 | 6400 | 0.0 | - |
5.5317 | 6450 | 0.0 | - |
5.5746 | 6500 | 0.0001 | - |
5.6175 | 6550 | 0.0 | - |
5.6604 | 6600 | 0.0001 | - |
5.7033 | 6650 | 0.0 | - |
5.7461 | 6700 | 0.0001 | - |
5.7890 | 6750 | 0.0 | - |
5.8319 | 6800 | 0.0 | - |
5.8748 | 6850 | 0.0001 | - |
5.9177 | 6900 | 0.0 | - |
5.9605 | 6950 | 0.0001 | - |
6.0034 | 7000 | 0.0023 | - |
6.0463 | 7050 | 0.0094 | - |
6.0892 | 7100 | 0.0089 | - |
6.1321 | 7150 | 0.0075 | - |
6.1750 | 7200 | 0.0033 | - |
6.2178 | 7250 | 0.0026 | - |
6.2607 | 7300 | 0.0023 | - |
6.3036 | 7350 | 0.0034 | - |
6.3465 | 7400 | 0.0013 | - |
6.3894 | 7450 | 0.0008 | - |
6.4322 | 7500 | 0.0004 | - |
6.4751 | 7550 | 0.0002 | - |
6.5180 | 7600 | 0.0001 | - |
6.5609 | 7650 | 0.0001 | - |
6.6038 | 7700 | 0.0001 | - |
6.6467 | 7750 | 0.0002 | - |
6.6895 | 7800 | 0.0001 | - |
6.7324 | 7850 | 0.0002 | - |
6.7753 | 7900 | 0.0001 | - |
6.8182 | 7950 | 0.0 | - |
6.8611 | 8000 | 0.0021 | - |
6.9039 | 8050 | 0.0003 | - |
6.9468 | 8100 | 0.0011 | - |
6.9897 | 8150 | 0.0013 | - |
7.0326 | 8200 | 0.0001 | - |
7.0755 | 8250 | 0.0001 | - |
7.1184 | 8300 | 0.0 | - |
7.1612 | 8350 | 0.0001 | - |
7.2041 | 8400 | 0.0002 | - |
7.2470 | 8450 | 0.0015 | - |
7.2899 | 8500 | 0.0008 | - |
7.3328 | 8550 | 0.0001 | - |
7.3756 | 8600 | 0.0 | - |
7.4185 | 8650 | 0.0 | - |
7.4614 | 8700 | 0.0 | - |
7.5043 | 8750 | 0.0 | - |
7.5472 | 8800 | 0.0001 | - |
7.5901 | 8850 | 0.0 | - |
7.6329 | 8900 | 0.0001 | - |
7.6758 | 8950 | 0.0001 | - |
7.7187 | 9000 | 0.0 | - |
7.7616 | 9050 | 0.0001 | - |
7.8045 | 9100 | 0.0003 | - |
7.8473 | 9150 | 0.0002 | - |
7.8902 | 9200 | 0.0 | - |
7.9331 | 9250 | 0.0 | - |
7.9760 | 9300 | 0.0 | - |
8.0189 | 9350 | 0.0 | - |
8.0617 | 9400 | 0.0 | - |
8.1046 | 9450 | 0.0005 | - |
8.1475 | 9500 | 0.0092 | - |
8.1904 | 9550 | 0.009 | - |
8.2333 | 9600 | 0.0042 | - |
8.2762 | 9650 | 0.0011 | - |
8.3190 | 9700 | 0.0001 | - |
8.3619 | 9750 | 0.0003 | - |
8.4048 | 9800 | 0.0001 | - |
8.4477 | 9850 | 0.0003 | - |
8.4906 | 9900 | 0.0 | - |
8.5334 | 9950 | 0.0 | - |
8.5763 | 10000 | 0.0002 | - |
8.6192 | 10050 | 0.0003 | - |
8.6621 | 10100 | 0.0 | - |
8.7050 | 10150 | 0.0 | - |
8.7479 | 10200 | 0.0 | - |
8.7907 | 10250 | 0.0 | - |
8.8336 | 10300 | 0.0 | - |
8.8765 | 10350 | 0.0 | - |
8.9194 | 10400 | 0.0 | - |
8.9623 | 10450 | 0.0 | - |
9.0051 | 10500 | 0.0 | - |
9.0480 | 10550 | 0.0 | - |
9.0909 | 10600 | 0.0 | - |
9.1338 | 10650 | 0.0 | - |
9.1767 | 10700 | 0.0 | - |
9.2196 | 10750 | 0.0 | - |
9.2624 | 10800 | 0.0 | - |
9.3053 | 10850 | 0.0018 | - |
9.3482 | 10900 | 0.0016 | - |
9.3911 | 10950 | 0.0012 | - |
9.4340 | 11000 | 0.0007 | - |
9.4768 | 11050 | 0.0075 | - |
9.5197 | 11100 | 0.0044 | - |
9.5626 | 11150 | 0.004 | - |
9.6055 | 11200 | 0.004 | - |
9.6484 | 11250 | 0.0019 | - |
9.6913 | 11300 | 0.0015 | - |
9.7341 | 11350 | 0.0017 | - |
9.7770 | 11400 | 0.0011 | - |
9.8199 | 11450 | 0.0003 | - |
9.8628 | 11500 | 0.0001 | - |
9.9057 | 11550 | 0.0001 | - |
9.9485 | 11600 | 0.0001 | - |
9.9914 | 11650 | 0.0 | - |
10.0343 | 11700 | 0.0 | - |
10.0772 | 11750 | 0.0 | - |
10.1201 | 11800 | 0.0 | - |
10.1630 | 11850 | 0.0 | - |
10.2058 | 11900 | 0.0 | - |
10.2487 | 11950 | 0.0 | - |
10.2916 | 12000 | 0.0 | - |
10.3345 | 12050 | 0.0 | - |
10.3774 | 12100 | 0.0 | - |
10.4202 | 12150 | 0.0 | - |
10.4631 | 12200 | 0.0 | - |
10.5060 | 12250 | 0.0 | - |
10.5489 | 12300 | 0.0 | - |
10.5918 | 12350 | 0.0 | - |
10.6346 | 12400 | 0.0 | - |
10.6775 | 12450 | 0.0 | - |
10.7204 | 12500 | 0.0 | - |
10.7633 | 12550 | 0.0 | - |
10.8062 | 12600 | 0.0 | - |
10.8491 | 12650 | 0.0 | - |
10.8919 | 12700 | 0.0 | - |
10.9348 | 12750 | 0.0003 | - |
10.9777 | 12800 | 0.0014 | - |
11.0206 | 12850 | 0.0004 | - |
11.0635 | 12900 | 0.0001 | - |
11.1063 | 12950 | 0.0 | - |
11.1492 | 13000 | 0.0 | - |
11.1921 | 13050 | 0.0 | - |
11.2350 | 13100 | 0.0 | - |
11.2779 | 13150 | 0.0 | - |
11.3208 | 13200 | 0.0 | - |
11.3636 | 13250 | 0.0 | - |
11.4065 | 13300 | 0.0 | - |
11.4494 | 13350 | 0.0 | - |
11.4923 | 13400 | 0.0 | - |
11.5352 | 13450 | 0.0 | - |
11.5780 | 13500 | 0.0 | - |
11.6209 | 13550 | 0.0 | - |
11.6638 | 13600 | 0.0 | - |
11.7067 | 13650 | 0.0 | - |
11.7496 | 13700 | 0.0 | - |
11.7925 | 13750 | 0.0 | - |
11.8353 | 13800 | 0.0 | - |
11.8782 | 13850 | 0.0 | - |
11.9211 | 13900 | 0.0 | - |
11.9640 | 13950 | 0.0 | - |
12.0069 | 14000 | 0.0 | - |
12.0497 | 14050 | 0.0 | - |
12.0926 | 14100 | 0.0 | - |
12.1355 | 14150 | 0.0 | - |
12.1784 | 14200 | 0.0 | - |
12.2213 | 14250 | 0.0 | - |
12.2642 | 14300 | 0.0 | - |
12.3070 | 14350 | 0.0 | - |
12.3499 | 14400 | 0.0 | - |
12.3928 | 14450 | 0.0 | - |
12.4357 | 14500 | 0.0 | - |
12.4786 | 14550 | 0.0 | - |
12.5214 | 14600 | 0.0 | - |
12.5643 | 14650 | 0.0 | - |
12.6072 | 14700 | 0.0 | - |
12.6501 | 14750 | 0.0 | - |
12.6930 | 14800 | 0.0 | - |
12.7358 | 14850 | 0.0 | - |
12.7787 | 14900 | 0.0 | - |
12.8216 | 14950 | 0.0 | - |
12.8645 | 15000 | 0.0 | - |
12.9074 | 15050 | 0.0 | - |
12.9503 | 15100 | 0.0001 | - |
12.9931 | 15150 | 0.0068 | - |
13.0360 | 15200 | 0.0085 | - |
13.0789 | 15250 | 0.007 | - |
13.1218 | 15300 | 0.0059 | - |
13.1647 | 15350 | 0.0036 | - |
13.2075 | 15400 | 0.0041 | - |
13.2504 | 15450 | 0.0054 | - |
13.2933 | 15500 | 0.007 | - |
13.3362 | 15550 | 0.0047 | - |
13.3791 | 15600 | 0.0041 | - |
13.4220 | 15650 | 0.0019 | - |
13.4648 | 15700 | 0.002 | - |
13.5077 | 15750 | 0.0004 | - |
13.5506 | 15800 | 0.0001 | - |
13.5935 | 15850 | 0.0 | - |
13.6364 | 15900 | 0.0001 | - |
13.6792 | 15950 | 0.0 | - |
13.7221 | 16000 | 0.0002 | - |
13.7650 | 16050 | 0.0 | - |
13.8079 | 16100 | 0.0 | - |
13.8508 | 16150 | 0.0 | - |
13.8937 | 16200 | 0.0 | - |
13.9365 | 16250 | 0.0 | - |
13.9794 | 16300 | 0.0 | - |
14.0223 | 16350 | 0.0 | - |
14.0652 | 16400 | 0.0 | - |
14.1081 | 16450 | 0.0 | - |
14.1509 | 16500 | 0.0 | - |
14.1938 | 16550 | 0.0 | - |
14.2367 | 16600 | 0.0 | - |
14.2796 | 16650 | 0.0 | - |
14.3225 | 16700 | 0.0 | - |
14.3654 | 16750 | 0.0 | - |
14.4082 | 16800 | 0.0 | - |
14.4511 | 16850 | 0.0 | - |
14.4940 | 16900 | 0.0 | - |
14.5369 | 16950 | 0.0 | - |
14.5798 | 17000 | 0.0 | - |
14.6226 | 17050 | 0.0 | - |
14.6655 | 17100 | 0.0 | - |
14.7084 | 17150 | 0.0 | - |
14.7513 | 17200 | 0.0 | - |
14.7942 | 17250 | 0.0 | - |
14.8370 | 17300 | 0.0 | - |
14.8799 | 17350 | 0.0 | - |
14.9228 | 17400 | 0.0 | - |
14.9657 | 17450 | 0.0 | - |
15.0086 | 17500 | 0.0 | - |
15.0515 | 17550 | 0.0 | - |
15.0943 | 17600 | 0.0 | - |
15.1372 | 17650 | 0.0 | - |
15.1801 | 17700 | 0.0 | - |
15.2230 | 17750 | 0.0 | - |
15.2659 | 17800 | 0.0 | - |
15.3087 | 17850 | 0.0 | - |
15.3516 | 17900 | 0.0 | - |
15.3945 | 17950 | 0.0 | - |
15.4374 | 18000 | 0.0 | - |
15.4803 | 18050 | 0.0 | - |
15.5232 | 18100 | 0.0 | - |
15.5660 | 18150 | 0.0 | - |
15.6089 | 18200 | 0.0004 | - |
15.6518 | 18250 | 0.002 | - |
15.6947 | 18300 | 0.0015 | - |
15.7376 | 18350 | 0.0016 | - |
15.7804 | 18400 | 0.002 | - |
15.8233 | 18450 | 0.0009 | - |
15.8662 | 18500 | 0.0007 | - |
15.9091 | 18550 | 0.0011 | - |
15.9520 | 18600 | 0.0004 | - |
15.9949 | 18650 | 0.0004 | - |
16.0377 | 18700 | 0.0001 | - |
16.0806 | 18750 | 0.0 | - |
16.1235 | 18800 | 0.0 | - |
16.1664 | 18850 | 0.0002 | - |
16.2093 | 18900 | 0.0 | - |
16.2521 | 18950 | 0.0 | - |
16.2950 | 19000 | 0.0 | - |
16.3379 | 19050 | 0.0 | - |
16.3808 | 19100 | 0.0 | - |
16.4237 | 19150 | 0.0 | - |
16.4666 | 19200 | 0.0 | - |
16.5094 | 19250 | 0.0 | - |
16.5523 | 19300 | 0.0 | - |
16.5952 | 19350 | 0.0 | - |
16.6381 | 19400 | 0.0 | - |
16.6810 | 19450 | 0.0 | - |
16.7238 | 19500 | 0.0 | - |
16.7667 | 19550 | 0.0001 | - |
16.8096 | 19600 | 0.0001 | - |
16.8525 | 19650 | 0.0007 | - |
16.8954 | 19700 | 0.0002 | - |
16.9383 | 19750 | 0.0003 | - |
16.9811 | 19800 | 0.0 | - |
17.0240 | 19850 | 0.0 | - |
17.0669 | 19900 | 0.0 | - |
17.1098 | 19950 | 0.0 | - |
17.1527 | 20000 | 0.0 | - |
17.1955 | 20050 | 0.0 | - |
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17.3242 | 20200 | 0.0 | - |
17.3671 | 20250 | 0.0 | - |
17.4099 | 20300 | 0.0 | - |
17.4528 | 20350 | 0.0 | - |
17.4957 | 20400 | 0.0 | - |
17.5386 | 20450 | 0.0 | - |
17.5815 | 20500 | 0.0 | - |
17.6244 | 20550 | 0.0 | - |
17.6672 | 20600 | 0.0 | - |
17.7101 | 20650 | 0.0 | - |
17.7530 | 20700 | 0.0 | - |
17.7959 | 20750 | 0.0 | - |
17.8388 | 20800 | 0.0 | - |
17.8816 | 20850 | 0.0 | - |
17.9245 | 20900 | 0.0 | - |
17.9674 | 20950 | 0.0 | - |
18.0103 | 21000 | 0.0 | - |
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18.8679 | 22000 | 0.0 | - |
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18.9966 | 22150 | 0.0 | - |
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19.0823 | 22250 | 0.0 | - |
19.1252 | 22300 | 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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