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: 101 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 |
---|---|
9.0 |
|
13.0 |
|
16.0 |
|
65.0 |
|
54.0 |
|
59.0 |
|
24.0 |
|
88.0 |
|
87.0 |
|
30.0 |
|
38.0 |
|
80.0 |
|
25.0 |
|
7.0 |
|
98.0 |
|
11.0 |
|
10.0 |
|
58.0 |
|
34.0 |
|
99.0 |
|
56.0 |
|
39.0 |
|
91.0 |
|
96.0 |
|
69.0 |
|
68.0 |
|
75.0 |
|
100.0 |
|
20.0 |
|
47.0 |
|
79.0 |
|
37.0 |
|
66.0 |
|
0.0 |
|
46.0 |
|
74.0 |
|
4.0 |
|
86.0 |
|
73.0 |
|
85.0 |
|
55.0 |
|
8.0 |
|
31.0 |
|
45.0 |
|
48.0 |
|
49.0 |
|
2.0 |
|
60.0 |
|
29.0 |
|
61.0 |
|
62.0 |
|
81.0 |
|
78.0 |
|
23.0 |
|
89.0 |
|
35.0 |
|
93.0 |
|
43.0 |
|
51.0 |
|
28.0 |
|
95.0 |
|
71.0 |
|
76.0 |
|
90.0 |
|
27.0 |
|
42.0 |
|
14.0 |
|
6.0 |
|
77.0 |
|
17.0 |
|
70.0 |
|
57.0 |
|
92.0 |
|
40.0 |
|
36.0 |
|
63.0 |
|
52.0 |
|
97.0 |
|
32.0 |
|
41.0 |
|
21.0 |
|
1.0 |
|
84.0 |
|
67.0 |
|
26.0 |
|
5.0 |
|
33.0 |
|
18.0 |
|
72.0 |
|
15.0 |
|
12.0 |
|
83.0 |
|
22.0 |
|
44.0 |
|
64.0 |
|
53.0 |
|
3.0 |
|
19.0 |
|
82.0 |
|
94.0 |
|
50.0 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9025 |
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_top_fd1")
# Run inference
preds = model("에이치엘사이언스 닥터 슈퍼칸 30캡슐 3박스 MinSellAmount (#M)건강식품>영양제>밀크씨슬 Gmarket > 식품 > 건강식품 > 영양제 > 밀크씨슬")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 6 | 21.7359 | 60 |
Label | Training Sample Count |
---|---|
0.0 | 50 |
1.0 | 50 |
2.0 | 50 |
3.0 | 2 |
4.0 | 50 |
5.0 | 50 |
6.0 | 50 |
7.0 | 50 |
8.0 | 50 |
9.0 | 50 |
10.0 | 50 |
11.0 | 50 |
12.0 | 50 |
13.0 | 50 |
14.0 | 50 |
15.0 | 31 |
16.0 | 50 |
17.0 | 27 |
18.0 | 50 |
19.0 | 3 |
20.0 | 50 |
21.0 | 15 |
22.0 | 26 |
23.0 | 50 |
24.0 | 49 |
25.0 | 50 |
26.0 | 50 |
27.0 | 50 |
28.0 | 50 |
29.0 | 50 |
30.0 | 50 |
31.0 | 50 |
32.0 | 50 |
33.0 | 39 |
34.0 | 50 |
35.0 | 50 |
36.0 | 50 |
37.0 | 50 |
38.0 | 50 |
39.0 | 50 |
40.0 | 50 |
41.0 | 50 |
42.0 | 50 |
43.0 | 50 |
44.0 | 10 |
45.0 | 50 |
46.0 | 50 |
47.0 | 50 |
48.0 | 50 |
49.0 | 50 |
50.0 | 1 |
51.0 | 50 |
52.0 | 11 |
53.0 | 12 |
54.0 | 26 |
55.0 | 50 |
56.0 | 50 |
57.0 | 50 |
58.0 | 50 |
59.0 | 50 |
60.0 | 50 |
61.0 | 50 |
62.0 | 44 |
63.0 | 50 |
64.0 | 16 |
65.0 | 50 |
66.0 | 50 |
67.0 | 50 |
68.0 | 50 |
69.0 | 50 |
70.0 | 50 |
71.0 | 4 |
72.0 | 50 |
73.0 | 50 |
74.0 | 50 |
75.0 | 50 |
76.0 | 50 |
77.0 | 50 |
78.0 | 50 |
79.0 | 47 |
80.0 | 24 |
81.0 | 50 |
82.0 | 1 |
83.0 | 50 |
84.0 | 50 |
85.0 | 50 |
86.0 | 50 |
87.0 | 50 |
88.0 | 50 |
89.0 | 50 |
90.0 | 50 |
91.0 | 50 |
92.0 | 50 |
93.0 | 13 |
94.0 | 2 |
95.0 | 50 |
96.0 | 50 |
97.0 | 50 |
98.0 | 36 |
99.0 | 50 |
100.0 | 50 |
Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 30
- 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 |
---|---|---|---|
0.0005 | 1 | 0.5992 | - |
0.0243 | 50 | 0.4884 | - |
0.0486 | 100 | 0.4851 | - |
0.0729 | 150 | 0.481 | - |
0.0972 | 200 | 0.3988 | - |
0.1215 | 250 | 0.2751 | - |
0.1458 | 300 | 0.2462 | - |
0.1701 | 350 | 0.2105 | - |
0.1944 | 400 | 0.186 | - |
0.2187 | 450 | 0.171 | - |
0.2430 | 500 | 0.1598 | - |
0.2672 | 550 | 0.1399 | - |
0.2915 | 600 | 0.1307 | - |
0.3158 | 650 | 0.1118 | - |
0.3401 | 700 | 0.1066 | - |
0.3644 | 750 | 0.0928 | - |
0.3887 | 800 | 0.0829 | - |
0.4130 | 850 | 0.0774 | - |
0.4373 | 900 | 0.0736 | - |
0.4616 | 950 | 0.0645 | - |
0.4859 | 1000 | 0.0605 | - |
0.5102 | 1050 | 0.0552 | - |
0.5345 | 1100 | 0.0518 | - |
0.5588 | 1150 | 0.0488 | - |
0.5831 | 1200 | 0.0449 | - |
0.6074 | 1250 | 0.0419 | - |
0.6317 | 1300 | 0.0407 | - |
0.6560 | 1350 | 0.0367 | - |
0.6803 | 1400 | 0.0356 | - |
0.7046 | 1450 | 0.0353 | - |
0.7289 | 1500 | 0.0331 | - |
0.7532 | 1550 | 0.0299 | - |
0.7775 | 1600 | 0.0273 | - |
0.8017 | 1650 | 0.0275 | - |
0.8260 | 1700 | 0.028 | - |
0.8503 | 1750 | 0.0262 | - |
0.8746 | 1800 | 0.0243 | - |
0.8989 | 1850 | 0.0235 | - |
0.9232 | 1900 | 0.0217 | - |
0.9475 | 1950 | 0.0207 | - |
0.9718 | 2000 | 0.0223 | - |
0.9961 | 2050 | 0.0208 | - |
1.0204 | 2100 | 0.0181 | - |
1.0447 | 2150 | 0.0186 | - |
1.0690 | 2200 | 0.0197 | - |
1.0933 | 2250 | 0.0154 | - |
1.1176 | 2300 | 0.0157 | - |
1.1419 | 2350 | 0.017 | - |
1.1662 | 2400 | 0.0159 | - |
1.1905 | 2450 | 0.0151 | - |
1.2148 | 2500 | 0.0152 | - |
1.2391 | 2550 | 0.0157 | - |
1.2634 | 2600 | 0.0136 | - |
1.2877 | 2650 | 0.0136 | - |
1.3120 | 2700 | 0.0142 | - |
1.3362 | 2750 | 0.0131 | - |
1.3605 | 2800 | 0.0118 | - |
1.3848 | 2850 | 0.0133 | - |
1.4091 | 2900 | 0.01 | - |
1.4334 | 2950 | 0.0108 | - |
1.4577 | 3000 | 0.013 | - |
1.4820 | 3050 | 0.0106 | - |
1.5063 | 3100 | 0.0107 | - |
1.5306 | 3150 | 0.0126 | - |
1.5549 | 3200 | 0.0103 | - |
1.5792 | 3250 | 0.0092 | - |
1.6035 | 3300 | 0.0112 | - |
1.6278 | 3350 | 0.009 | - |
1.6521 | 3400 | 0.009 | - |
1.6764 | 3450 | 0.0086 | - |
1.7007 | 3500 | 0.0075 | - |
1.7250 | 3550 | 0.0089 | - |
1.7493 | 3600 | 0.0099 | - |
1.7736 | 3650 | 0.0085 | - |
1.7979 | 3700 | 0.0089 | - |
1.8222 | 3750 | 0.009 | - |
1.8465 | 3800 | 0.008 | - |
1.8707 | 3850 | 0.009 | - |
1.8950 | 3900 | 0.0083 | - |
1.9193 | 3950 | 0.0087 | - |
1.9436 | 4000 | 0.0098 | - |
1.9679 | 4050 | 0.0099 | - |
1.9922 | 4100 | 0.0098 | - |
2.0165 | 4150 | 0.0075 | - |
2.0408 | 4200 | 0.0067 | - |
2.0651 | 4250 | 0.0078 | - |
2.0894 | 4300 | 0.0084 | - |
2.1137 | 4350 | 0.0079 | - |
2.1380 | 4400 | 0.0054 | - |
2.1623 | 4450 | 0.005 | - |
2.1866 | 4500 | 0.0047 | - |
2.2109 | 4550 | 0.0062 | - |
2.2352 | 4600 | 0.0063 | - |
2.2595 | 4650 | 0.0066 | - |
2.2838 | 4700 | 0.0066 | - |
2.3081 | 4750 | 0.0059 | - |
2.3324 | 4800 | 0.005 | - |
2.3567 | 4850 | 0.0049 | - |
2.3810 | 4900 | 0.0044 | - |
2.4052 | 4950 | 0.0037 | - |
2.4295 | 5000 | 0.0046 | - |
2.4538 | 5050 | 0.007 | - |
2.4781 | 5100 | 0.006 | - |
2.5024 | 5150 | 0.005 | - |
2.5267 | 5200 | 0.0058 | - |
2.5510 | 5250 | 0.0054 | - |
2.5753 | 5300 | 0.0067 | - |
2.5996 | 5350 | 0.0059 | - |
2.6239 | 5400 | 0.0081 | - |
2.6482 | 5450 | 0.0068 | - |
2.6725 | 5500 | 0.006 | - |
2.6968 | 5550 | 0.0062 | - |
2.7211 | 5600 | 0.0059 | - |
2.7454 | 5650 | 0.0061 | - |
2.7697 | 5700 | 0.0045 | - |
2.7940 | 5750 | 0.0051 | - |
2.8183 | 5800 | 0.0048 | - |
2.8426 | 5850 | 0.0052 | - |
2.8669 | 5900 | 0.0053 | - |
2.8912 | 5950 | 0.0061 | - |
2.9155 | 6000 | 0.0058 | - |
2.9397 | 6050 | 0.0051 | - |
2.9640 | 6100 | 0.0054 | - |
2.9883 | 6150 | 0.0053 | - |
3.0126 | 6200 | 0.0045 | - |
3.0369 | 6250 | 0.0048 | - |
3.0612 | 6300 | 0.003 | - |
3.0855 | 6350 | 0.0033 | - |
3.1098 | 6400 | 0.0038 | - |
3.1341 | 6450 | 0.0038 | - |
3.1584 | 6500 | 0.0047 | - |
3.1827 | 6550 | 0.0036 | - |
3.2070 | 6600 | 0.0036 | - |
3.2313 | 6650 | 0.0036 | - |
3.2556 | 6700 | 0.0045 | - |
3.2799 | 6750 | 0.0029 | - |
3.3042 | 6800 | 0.0032 | - |
3.3285 | 6850 | 0.0037 | - |
3.3528 | 6900 | 0.0026 | - |
3.3771 | 6950 | 0.0026 | - |
3.4014 | 7000 | 0.005 | - |
3.4257 | 7050 | 0.0028 | - |
3.4500 | 7100 | 0.0025 | - |
3.4742 | 7150 | 0.0035 | - |
3.4985 | 7200 | 0.0045 | - |
3.5228 | 7250 | 0.005 | - |
3.5471 | 7300 | 0.005 | - |
3.5714 | 7350 | 0.0044 | - |
3.5957 | 7400 | 0.0037 | - |
3.6200 | 7450 | 0.0034 | - |
3.6443 | 7500 | 0.0027 | - |
3.6686 | 7550 | 0.0018 | - |
3.6929 | 7600 | 0.0029 | - |
3.7172 | 7650 | 0.0035 | - |
3.7415 | 7700 | 0.0029 | - |
3.7658 | 7750 | 0.0045 | - |
3.7901 | 7800 | 0.0043 | - |
3.8144 | 7850 | 0.0028 | - |
3.8387 | 7900 | 0.0036 | - |
3.8630 | 7950 | 0.0039 | - |
3.8873 | 8000 | 0.0033 | - |
3.9116 | 8050 | 0.0031 | - |
3.9359 | 8100 | 0.0029 | - |
3.9602 | 8150 | 0.002 | - |
3.9845 | 8200 | 0.0024 | - |
4.0087 | 8250 | 0.0026 | - |
4.0330 | 8300 | 0.0016 | - |
4.0573 | 8350 | 0.0036 | - |
4.0816 | 8400 | 0.0035 | - |
4.1059 | 8450 | 0.0039 | - |
4.1302 | 8500 | 0.0034 | - |
4.1545 | 8550 | 0.0024 | - |
4.1788 | 8600 | 0.0025 | - |
4.2031 | 8650 | 0.003 | - |
4.2274 | 8700 | 0.0017 | - |
4.2517 | 8750 | 0.0021 | - |
4.2760 | 8800 | 0.0021 | - |
4.3003 | 8850 | 0.0012 | - |
4.3246 | 8900 | 0.0013 | - |
4.3489 | 8950 | 0.0026 | - |
4.3732 | 9000 | 0.0014 | - |
4.3975 | 9050 | 0.0023 | - |
4.4218 | 9100 | 0.0021 | - |
4.4461 | 9150 | 0.0024 | - |
4.4704 | 9200 | 0.0025 | - |
4.4947 | 9250 | 0.002 | - |
4.5190 | 9300 | 0.0019 | - |
4.5432 | 9350 | 0.002 | - |
4.5675 | 9400 | 0.0012 | - |
4.5918 | 9450 | 0.0022 | - |
4.6161 | 9500 | 0.0015 | - |
4.6404 | 9550 | 0.0013 | - |
4.6647 | 9600 | 0.0014 | - |
4.6890 | 9650 | 0.0025 | - |
4.7133 | 9700 | 0.0016 | - |
4.7376 | 9750 | 0.0014 | - |
4.7619 | 9800 | 0.0019 | - |
4.7862 | 9850 | 0.0016 | - |
4.8105 | 9900 | 0.002 | - |
4.8348 | 9950 | 0.0019 | - |
4.8591 | 10000 | 0.0019 | - |
4.8834 | 10050 | 0.0022 | - |
4.9077 | 10100 | 0.0027 | - |
4.9320 | 10150 | 0.0029 | - |
4.9563 | 10200 | 0.0037 | - |
4.9806 | 10250 | 0.003 | - |
5.0049 | 10300 | 0.0016 | - |
5.0292 | 10350 | 0.0021 | - |
5.0534 | 10400 | 0.0023 | - |
5.0777 | 10450 | 0.0015 | - |
5.1020 | 10500 | 0.0014 | - |
5.1263 | 10550 | 0.0013 | - |
5.1506 | 10600 | 0.0015 | - |
5.1749 | 10650 | 0.0018 | - |
5.1992 | 10700 | 0.0017 | - |
5.2235 | 10750 | 0.0019 | - |
5.2478 | 10800 | 0.0025 | - |
5.2721 | 10850 | 0.002 | - |
5.2964 | 10900 | 0.0026 | - |
5.3207 | 10950 | 0.0014 | - |
5.3450 | 11000 | 0.0015 | - |
5.3693 | 11050 | 0.0033 | - |
5.3936 | 11100 | 0.0015 | - |
5.4179 | 11150 | 0.0022 | - |
5.4422 | 11200 | 0.0023 | - |
5.4665 | 11250 | 0.0025 | - |
5.4908 | 11300 | 0.0011 | - |
5.5151 | 11350 | 0.0019 | - |
5.5394 | 11400 | 0.0017 | - |
5.5637 | 11450 | 0.0014 | - |
5.5879 | 11500 | 0.0009 | - |
5.6122 | 11550 | 0.0016 | - |
5.6365 | 11600 | 0.0025 | - |
5.6608 | 11650 | 0.0012 | - |
5.6851 | 11700 | 0.0014 | - |
5.7094 | 11750 | 0.0015 | - |
5.7337 | 11800 | 0.0017 | - |
5.7580 | 11850 | 0.0009 | - |
5.7823 | 11900 | 0.0018 | - |
5.8066 | 11950 | 0.0005 | - |
5.8309 | 12000 | 0.0014 | - |
5.8552 | 12050 | 0.0006 | - |
5.8795 | 12100 | 0.0013 | - |
5.9038 | 12150 | 0.001 | - |
5.9281 | 12200 | 0.0009 | - |
5.9524 | 12250 | 0.0018 | - |
5.9767 | 12300 | 0.0021 | - |
6.0010 | 12350 | 0.0027 | - |
6.0253 | 12400 | 0.0031 | - |
6.0496 | 12450 | 0.0032 | - |
6.0739 | 12500 | 0.0026 | - |
6.0982 | 12550 | 0.0019 | - |
6.1224 | 12600 | 0.0012 | - |
6.1467 | 12650 | 0.0006 | - |
6.1710 | 12700 | 0.0015 | - |
6.1953 | 12750 | 0.0011 | - |
6.2196 | 12800 | 0.0014 | - |
6.2439 | 12850 | 0.0017 | - |
6.2682 | 12900 | 0.0016 | - |
6.2925 | 12950 | 0.0008 | - |
6.3168 | 13000 | 0.0006 | - |
6.3411 | 13050 | 0.0019 | - |
6.3654 | 13100 | 0.0018 | - |
6.3897 | 13150 | 0.0007 | - |
6.4140 | 13200 | 0.0011 | - |
6.4383 | 13250 | 0.0009 | - |
6.4626 | 13300 | 0.0012 | - |
6.4869 | 13350 | 0.0012 | - |
6.5112 | 13400 | 0.0013 | - |
6.5355 | 13450 | 0.0018 | - |
6.5598 | 13500 | 0.0011 | - |
6.5841 | 13550 | 0.0016 | - |
6.6084 | 13600 | 0.0007 | - |
6.6327 | 13650 | 0.0007 | - |
6.6569 | 13700 | 0.0014 | - |
6.6812 | 13750 | 0.0017 | - |
6.7055 | 13800 | 0.0018 | - |
6.7298 | 13850 | 0.0012 | - |
6.7541 | 13900 | 0.0016 | - |
6.7784 | 13950 | 0.0012 | - |
6.8027 | 14000 | 0.0007 | - |
6.8270 | 14050 | 0.0015 | - |
6.8513 | 14100 | 0.0019 | - |
6.8756 | 14150 | 0.0016 | - |
6.8999 | 14200 | 0.0017 | - |
6.9242 | 14250 | 0.0014 | - |
6.9485 | 14300 | 0.0016 | - |
6.9728 | 14350 | 0.0007 | - |
6.9971 | 14400 | 0.0007 | - |
7.0214 | 14450 | 0.0011 | - |
7.0457 | 14500 | 0.0007 | - |
7.0700 | 14550 | 0.0008 | - |
7.0943 | 14600 | 0.0004 | - |
7.1186 | 14650 | 0.0012 | - |
7.1429 | 14700 | 0.0009 | - |
7.1672 | 14750 | 0.0007 | - |
7.1914 | 14800 | 0.0023 | - |
7.2157 | 14850 | 0.0023 | - |
7.2400 | 14900 | 0.0016 | - |
7.2643 | 14950 | 0.0021 | - |
7.2886 | 15000 | 0.0014 | - |
7.3129 | 15050 | 0.0009 | - |
7.3372 | 15100 | 0.0009 | - |
7.3615 | 15150 | 0.0013 | - |
7.3858 | 15200 | 0.0009 | - |
7.4101 | 15250 | 0.0012 | - |
7.4344 | 15300 | 0.0007 | - |
7.4587 | 15350 | 0.001 | - |
7.4830 | 15400 | 0.0006 | - |
7.5073 | 15450 | 0.0019 | - |
7.5316 | 15500 | 0.0017 | - |
7.5559 | 15550 | 0.0015 | - |
7.5802 | 15600 | 0.0022 | - |
7.6045 | 15650 | 0.0009 | - |
7.6288 | 15700 | 0.0008 | - |
7.6531 | 15750 | 0.0011 | - |
7.6774 | 15800 | 0.001 | - |
7.7017 | 15850 | 0.001 | - |
7.7259 | 15900 | 0.0009 | - |
7.7502 | 15950 | 0.0007 | - |
7.7745 | 16000 | 0.0004 | - |
7.7988 | 16050 | 0.0016 | - |
7.8231 | 16100 | 0.0003 | - |
7.8474 | 16150 | 0.001 | - |
7.8717 | 16200 | 0.0008 | - |
7.8960 | 16250 | 0.0014 | - |
7.9203 | 16300 | 0.0006 | - |
7.9446 | 16350 | 0.0013 | - |
7.9689 | 16400 | 0.0012 | - |
7.9932 | 16450 | 0.0012 | - |
8.0175 | 16500 | 0.001 | - |
8.0418 | 16550 | 0.0009 | - |
8.0661 | 16600 | 0.0006 | - |
8.0904 | 16650 | 0.0007 | - |
8.1147 | 16700 | 0.0005 | - |
8.1390 | 16750 | 0.001 | - |
8.1633 | 16800 | 0.0008 | - |
8.1876 | 16850 | 0.0014 | - |
8.2119 | 16900 | 0.0011 | - |
8.2362 | 16950 | 0.0009 | - |
8.2604 | 17000 | 0.0003 | - |
8.2847 | 17050 | 0.001 | - |
8.3090 | 17100 | 0.0008 | - |
8.3333 | 17150 | 0.0009 | - |
8.3576 | 17200 | 0.0008 | - |
8.3819 | 17250 | 0.0005 | - |
8.4062 | 17300 | 0.0005 | - |
8.4305 | 17350 | 0.0013 | - |
8.4548 | 17400 | 0.0006 | - |
8.4791 | 17450 | 0.0005 | - |
8.5034 | 17500 | 0.001 | - |
8.5277 | 17550 | 0.0013 | - |
8.5520 | 17600 | 0.0007 | - |
8.5763 | 17650 | 0.0008 | - |
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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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