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: 100 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 |
---|---|
16 |
|
40 |
|
56 |
|
97 |
|
72 |
|
27 |
|
14 |
|
99 |
|
12 |
|
29 |
|
35 |
|
76 |
|
3 |
|
66 |
|
73 |
|
58 |
|
84 |
|
17 |
|
37 |
|
45 |
|
6 |
|
39 |
|
5 |
|
71 |
|
13 |
|
89 |
|
46 |
|
90 |
|
75 |
|
31 |
|
38 |
|
30 |
|
60 |
|
70 |
|
55 |
|
9 |
|
65 |
|
85 |
|
32 |
|
54 |
|
95 |
|
64 |
|
28 |
|
63 |
|
34 |
|
86 |
|
36 |
|
81 |
|
79 |
|
1 |
|
19 |
|
82 |
|
20 |
|
88 |
|
59 |
|
25 |
|
24 |
|
87 |
|
51 |
|
61 |
|
21 |
|
15 |
|
57 |
|
62 |
|
52 |
|
68 |
|
4 |
|
69 |
|
74 |
|
2 |
|
93 |
|
42 |
|
47 |
|
96 |
|
43 |
|
80 |
|
8 |
|
53 |
|
67 |
|
22 |
|
0 |
|
41 |
|
33 |
|
11 |
|
18 |
|
48 |
|
10 |
|
92 |
|
91 |
|
98 |
|
7 |
|
50 |
|
78 |
|
23 |
|
83 |
|
94 |
|
44 |
|
49 |
|
26 |
|
77 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8809 |
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_bt_test_org_gtcate")
# Run inference
preds = model("[닥터 브로너스] 퓨어 캐스틸 솝 950ml+펌프 그린티 950ml (#M)GSSHOP>뷰티>클렌징>클렌징오일 GSSHOP > 뷰티 > 클렌징 > 클렌징오일")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 10 | 22.8545 | 125 |
Label | Training Sample Count |
---|---|
0 | 70 |
1 | 70 |
2 | 70 |
3 | 70 |
4 | 70 |
5 | 70 |
6 | 70 |
7 | 54 |
8 | 70 |
9 | 70 |
10 | 70 |
11 | 70 |
12 | 70 |
13 | 70 |
14 | 70 |
15 | 70 |
16 | 70 |
17 | 70 |
18 | 70 |
19 | 70 |
20 | 70 |
21 | 70 |
22 | 70 |
23 | 70 |
24 | 70 |
25 | 70 |
26 | 70 |
27 | 70 |
28 | 70 |
29 | 70 |
30 | 70 |
31 | 70 |
32 | 70 |
33 | 70 |
34 | 70 |
35 | 70 |
36 | 70 |
37 | 70 |
38 | 70 |
39 | 70 |
40 | 70 |
41 | 70 |
42 | 70 |
43 | 70 |
44 | 70 |
45 | 70 |
46 | 70 |
47 | 70 |
48 | 70 |
49 | 70 |
50 | 70 |
51 | 70 |
52 | 70 |
53 | 70 |
54 | 70 |
55 | 70 |
56 | 70 |
57 | 70 |
58 | 70 |
59 | 70 |
60 | 70 |
61 | 70 |
62 | 70 |
63 | 70 |
64 | 70 |
65 | 70 |
66 | 70 |
67 | 70 |
68 | 70 |
69 | 70 |
70 | 70 |
71 | 70 |
72 | 70 |
73 | 70 |
74 | 70 |
75 | 70 |
76 | 70 |
77 | 53 |
78 | 70 |
79 | 70 |
80 | 70 |
81 | 70 |
82 | 70 |
83 | 70 |
84 | 70 |
85 | 70 |
86 | 70 |
87 | 70 |
88 | 70 |
89 | 70 |
90 | 70 |
91 | 70 |
92 | 56 |
93 | 70 |
94 | 70 |
95 | 70 |
96 | 70 |
97 | 70 |
98 | 70 |
99 | 66 |
Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 35
- 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 |
---|---|---|---|
0.0003 | 1 | 0.4458 | - |
0.0132 | 50 | 0.4359 | - |
0.0263 | 100 | 0.4296 | - |
0.0395 | 150 | 0.4139 | - |
0.0526 | 200 | 0.3997 | - |
0.0658 | 250 | 0.381 | - |
0.0789 | 300 | 0.3764 | - |
0.0921 | 350 | 0.3537 | - |
0.1052 | 400 | 0.3116 | - |
0.1184 | 450 | 0.2756 | - |
0.1315 | 500 | 0.2676 | - |
0.1447 | 550 | 0.2471 | - |
0.1579 | 600 | 0.2209 | - |
0.1710 | 650 | 0.2011 | - |
0.1842 | 700 | 0.1838 | - |
0.1973 | 750 | 0.1715 | - |
0.2105 | 800 | 0.1489 | - |
0.2236 | 850 | 0.1408 | - |
0.2368 | 900 | 0.1287 | - |
0.2499 | 950 | 0.1197 | - |
0.2631 | 1000 | 0.1129 | - |
0.2762 | 1050 | 0.1041 | - |
0.2894 | 1100 | 0.1064 | - |
0.3026 | 1150 | 0.1 | - |
0.3157 | 1200 | 0.0927 | - |
0.3289 | 1250 | 0.0914 | - |
0.3420 | 1300 | 0.0892 | - |
0.3552 | 1350 | 0.0797 | - |
0.3683 | 1400 | 0.0835 | - |
0.3815 | 1450 | 0.0809 | - |
0.3946 | 1500 | 0.0773 | - |
0.4078 | 1550 | 0.0716 | - |
0.4209 | 1600 | 0.0705 | - |
0.4341 | 1650 | 0.0676 | - |
0.4473 | 1700 | 0.0662 | - |
0.4604 | 1750 | 0.064 | - |
0.4736 | 1800 | 0.0651 | - |
0.4867 | 1850 | 0.062 | - |
0.4999 | 1900 | 0.06 | - |
0.5130 | 1950 | 0.056 | - |
0.5262 | 2000 | 0.0556 | - |
0.5393 | 2050 | 0.0535 | - |
0.5525 | 2100 | 0.0519 | - |
0.5656 | 2150 | 0.0546 | - |
0.5788 | 2200 | 0.0502 | - |
0.5919 | 2250 | 0.0484 | - |
0.6051 | 2300 | 0.048 | - |
0.6183 | 2350 | 0.0471 | - |
0.6314 | 2400 | 0.043 | - |
0.6446 | 2450 | 0.0456 | - |
0.6577 | 2500 | 0.0442 | - |
0.6709 | 2550 | 0.0461 | - |
0.6840 | 2600 | 0.0428 | - |
0.6972 | 2650 | 0.0425 | - |
0.7103 | 2700 | 0.0414 | - |
0.7235 | 2750 | 0.0378 | - |
0.7366 | 2800 | 0.0369 | - |
0.7498 | 2850 | 0.0393 | - |
0.7630 | 2900 | 0.0352 | - |
0.7761 | 2950 | 0.0376 | - |
0.7893 | 3000 | 0.0345 | - |
0.8024 | 3050 | 0.0323 | - |
0.8156 | 3100 | 0.0354 | - |
0.8287 | 3150 | 0.0318 | - |
0.8419 | 3200 | 0.0293 | - |
0.8550 | 3250 | 0.0286 | - |
0.8682 | 3300 | 0.0329 | - |
0.8813 | 3350 | 0.0304 | - |
0.8945 | 3400 | 0.029 | - |
0.9077 | 3450 | 0.03 | - |
0.9208 | 3500 | 0.0309 | - |
0.9340 | 3550 | 0.0318 | - |
0.9471 | 3600 | 0.0306 | - |
0.9603 | 3650 | 0.0289 | - |
0.9734 | 3700 | 0.0291 | - |
0.9866 | 3750 | 0.0278 | - |
0.9997 | 3800 | 0.0277 | - |
1.0129 | 3850 | 0.0261 | - |
1.0260 | 3900 | 0.0259 | - |
1.0392 | 3950 | 0.0246 | - |
1.0524 | 4000 | 0.0251 | - |
1.0655 | 4050 | 0.0221 | - |
1.0787 | 4100 | 0.0251 | - |
1.0918 | 4150 | 0.0232 | - |
1.1050 | 4200 | 0.0227 | - |
1.1181 | 4250 | 0.0261 | - |
1.1313 | 4300 | 0.0254 | - |
1.1444 | 4350 | 0.024 | - |
1.1576 | 4400 | 0.0219 | - |
1.1707 | 4450 | 0.0223 | - |
1.1839 | 4500 | 0.0214 | - |
1.1971 | 4550 | 0.0211 | - |
1.2102 | 4600 | 0.0193 | - |
1.2234 | 4650 | 0.0208 | - |
1.2365 | 4700 | 0.0233 | - |
1.2497 | 4750 | 0.0201 | - |
1.2628 | 4800 | 0.0189 | - |
1.2760 | 4850 | 0.0213 | - |
1.2891 | 4900 | 0.0185 | - |
1.3023 | 4950 | 0.0204 | - |
1.3154 | 5000 | 0.0189 | - |
1.3286 | 5050 | 0.0196 | - |
1.3418 | 5100 | 0.021 | - |
1.3549 | 5150 | 0.0181 | - |
1.3681 | 5200 | 0.0184 | - |
1.3812 | 5250 | 0.0178 | - |
1.3944 | 5300 | 0.0179 | - |
1.4075 | 5350 | 0.0183 | - |
1.4207 | 5400 | 0.0185 | - |
1.4338 | 5450 | 0.0173 | - |
1.4470 | 5500 | 0.0192 | - |
1.4601 | 5550 | 0.0188 | - |
1.4733 | 5600 | 0.017 | - |
1.4865 | 5650 | 0.0172 | - |
1.4996 | 5700 | 0.0153 | - |
1.5128 | 5750 | 0.0158 | - |
1.5259 | 5800 | 0.0166 | - |
1.5391 | 5850 | 0.016 | - |
1.5522 | 5900 | 0.0166 | - |
1.5654 | 5950 | 0.0168 | - |
1.5785 | 6000 | 0.0152 | - |
1.5917 | 6050 | 0.0159 | - |
1.6048 | 6100 | 0.0145 | - |
1.6180 | 6150 | 0.0125 | - |
1.6311 | 6200 | 0.0129 | - |
1.6443 | 6250 | 0.0144 | - |
1.6575 | 6300 | 0.015 | - |
1.6706 | 6350 | 0.0129 | - |
1.6838 | 6400 | 0.016 | - |
1.6969 | 6450 | 0.0171 | - |
1.7101 | 6500 | 0.0147 | - |
1.7232 | 6550 | 0.013 | - |
1.7364 | 6600 | 0.0137 | - |
1.7495 | 6650 | 0.0133 | - |
1.7627 | 6700 | 0.012 | - |
1.7758 | 6750 | 0.0128 | - |
1.7890 | 6800 | 0.012 | - |
1.8022 | 6850 | 0.0114 | - |
1.8153 | 6900 | 0.0121 | - |
1.8285 | 6950 | 0.012 | - |
1.8416 | 7000 | 0.0136 | - |
1.8548 | 7050 | 0.0118 | - |
1.8679 | 7100 | 0.0111 | - |
1.8811 | 7150 | 0.0122 | - |
1.8942 | 7200 | 0.0115 | - |
1.9074 | 7250 | 0.0112 | - |
1.9205 | 7300 | 0.0111 | - |
1.9337 | 7350 | 0.0112 | - |
1.9469 | 7400 | 0.0104 | - |
1.9600 | 7450 | 0.0115 | - |
1.9732 | 7500 | 0.0123 | - |
1.9863 | 7550 | 0.0105 | - |
1.9995 | 7600 | 0.0097 | - |
2.0126 | 7650 | 0.0095 | - |
2.0258 | 7700 | 0.0079 | - |
2.0389 | 7750 | 0.0091 | - |
2.0521 | 7800 | 0.0089 | - |
2.0652 | 7850 | 0.0091 | - |
2.0784 | 7900 | 0.0088 | - |
2.0916 | 7950 | 0.0093 | - |
2.1047 | 8000 | 0.009 | - |
2.1179 | 8050 | 0.0107 | - |
2.1310 | 8100 | 0.0108 | - |
2.1442 | 8150 | 0.0105 | - |
2.1573 | 8200 | 0.0072 | - |
2.1705 | 8250 | 0.0077 | - |
2.1836 | 8300 | 0.008 | - |
2.1968 | 8350 | 0.0076 | - |
2.2099 | 8400 | 0.0094 | - |
2.2231 | 8450 | 0.0083 | - |
2.2363 | 8500 | 0.0109 | - |
2.2494 | 8550 | 0.0072 | - |
2.2626 | 8600 | 0.0082 | - |
2.2757 | 8650 | 0.0066 | - |
2.2889 | 8700 | 0.0068 | - |
2.3020 | 8750 | 0.0073 | - |
2.3152 | 8800 | 0.0085 | - |
2.3283 | 8850 | 0.0066 | - |
2.3415 | 8900 | 0.0087 | - |
2.3546 | 8950 | 0.0078 | - |
2.3678 | 9000 | 0.0062 | - |
2.3810 | 9050 | 0.0062 | - |
2.3941 | 9100 | 0.0064 | - |
2.4073 | 9150 | 0.0066 | - |
2.4204 | 9200 | 0.0071 | - |
2.4336 | 9250 | 0.006 | - |
2.4467 | 9300 | 0.0064 | - |
2.4599 | 9350 | 0.0076 | - |
2.4730 | 9400 | 0.0067 | - |
2.4862 | 9450 | 0.0068 | - |
2.4993 | 9500 | 0.0046 | - |
2.5125 | 9550 | 0.0061 | - |
2.5257 | 9600 | 0.0059 | - |
2.5388 | 9650 | 0.0062 | - |
2.5520 | 9700 | 0.0065 | - |
2.5651 | 9750 | 0.0067 | - |
2.5783 | 9800 | 0.0064 | - |
2.5914 | 9850 | 0.0051 | - |
2.6046 | 9900 | 0.007 | - |
2.6177 | 9950 | 0.0065 | - |
2.6309 | 10000 | 0.0051 | - |
2.6440 | 10050 | 0.0066 | - |
2.6572 | 10100 | 0.0058 | - |
2.6703 | 10150 | 0.005 | - |
2.6835 | 10200 | 0.005 | - |
2.6967 | 10250 | 0.0061 | - |
2.7098 | 10300 | 0.008 | - |
2.7230 | 10350 | 0.0063 | - |
2.7361 | 10400 | 0.0068 | - |
2.7493 | 10450 | 0.0071 | - |
2.7624 | 10500 | 0.0069 | - |
2.7756 | 10550 | 0.0053 | - |
2.7887 | 10600 | 0.0043 | - |
2.8019 | 10650 | 0.0056 | - |
2.8150 | 10700 | 0.0056 | - |
2.8282 | 10750 | 0.005 | - |
2.8414 | 10800 | 0.0032 | - |
2.8545 | 10850 | 0.0059 | - |
2.8677 | 10900 | 0.0054 | - |
2.8808 | 10950 | 0.0048 | - |
2.8940 | 11000 | 0.0042 | - |
2.9071 | 11050 | 0.0071 | - |
2.9203 | 11100 | 0.005 | - |
2.9334 | 11150 | 0.0062 | - |
2.9466 | 11200 | 0.0047 | - |
2.9597 | 11250 | 0.0054 | - |
2.9729 | 11300 | 0.0059 | - |
2.9861 | 11350 | 0.006 | - |
2.9992 | 11400 | 0.0063 | - |
3.0124 | 11450 | 0.0046 | - |
3.0255 | 11500 | 0.0046 | - |
3.0387 | 11550 | 0.0053 | - |
3.0518 | 11600 | 0.0053 | - |
3.0650 | 11650 | 0.0049 | - |
3.0781 | 11700 | 0.0043 | - |
3.0913 | 11750 | 0.0042 | - |
3.1044 | 11800 | 0.0033 | - |
3.1176 | 11850 | 0.0038 | - |
3.1308 | 11900 | 0.0038 | - |
3.1439 | 11950 | 0.0032 | - |
3.1571 | 12000 | 0.0031 | - |
3.1702 | 12050 | 0.003 | - |
3.1834 | 12100 | 0.0032 | - |
3.1965 | 12150 | 0.0053 | - |
3.2097 | 12200 | 0.005 | - |
3.2228 | 12250 | 0.0051 | - |
3.2360 | 12300 | 0.0046 | - |
3.2491 | 12350 | 0.0057 | - |
3.2623 | 12400 | 0.0046 | - |
3.2755 | 12450 | 0.0045 | - |
3.2886 | 12500 | 0.0038 | - |
3.3018 | 12550 | 0.0048 | - |
3.3149 | 12600 | 0.004 | - |
3.3281 | 12650 | 0.0041 | - |
3.3412 | 12700 | 0.0049 | - |
3.3544 | 12750 | 0.0029 | - |
3.3675 | 12800 | 0.0039 | - |
3.3807 | 12850 | 0.0046 | - |
3.3938 | 12900 | 0.0062 | - |
3.4070 | 12950 | 0.0044 | - |
3.4202 | 13000 | 0.0039 | - |
3.4333 | 13050 | 0.0034 | - |
3.4465 | 13100 | 0.0031 | - |
3.4596 | 13150 | 0.0029 | - |
3.4728 | 13200 | 0.003 | - |
3.4859 | 13250 | 0.0031 | - |
3.4991 | 13300 | 0.0034 | - |
3.5122 | 13350 | 0.0026 | - |
3.5254 | 13400 | 0.0034 | - |
3.5385 | 13450 | 0.0019 | - |
3.5517 | 13500 | 0.0028 | - |
3.5649 | 13550 | 0.0035 | - |
3.5780 | 13600 | 0.0037 | - |
3.5912 | 13650 | 0.0031 | - |
3.6043 | 13700 | 0.0035 | - |
3.6175 | 13750 | 0.0036 | - |
3.6306 | 13800 | 0.0034 | - |
3.6438 | 13850 | 0.0027 | - |
3.6569 | 13900 | 0.0045 | - |
3.6701 | 13950 | 0.003 | - |
3.6832 | 14000 | 0.003 | - |
3.6964 | 14050 | 0.0035 | - |
3.7096 | 14100 | 0.0031 | - |
3.7227 | 14150 | 0.0018 | - |
3.7359 | 14200 | 0.003 | - |
3.7490 | 14250 | 0.0019 | - |
3.7622 | 14300 | 0.0025 | - |
3.7753 | 14350 | 0.0033 | - |
3.7885 | 14400 | 0.0015 | - |
3.8016 | 14450 | 0.0026 | - |
3.8148 | 14500 | 0.0023 | - |
3.8279 | 14550 | 0.0027 | - |
3.8411 | 14600 | 0.0025 | - |
3.8542 | 14650 | 0.0028 | - |
3.8674 | 14700 | 0.0029 | - |
3.8806 | 14750 | 0.0023 | - |
3.8937 | 14800 | 0.0021 | - |
3.9069 | 14850 | 0.0028 | - |
3.9200 | 14900 | 0.0023 | - |
3.9332 | 14950 | 0.003 | - |
3.9463 | 15000 | 0.0033 | - |
3.9595 | 15050 | 0.003 | - |
3.9726 | 15100 | 0.004 | - |
3.9858 | 15150 | 0.0032 | - |
3.9989 | 15200 | 0.0034 | - |
4.0121 | 15250 | 0.003 | - |
4.0253 | 15300 | 0.0031 | - |
4.0384 | 15350 | 0.003 | - |
4.0516 | 15400 | 0.0039 | - |
4.0647 | 15450 | 0.0032 | - |
4.0779 | 15500 | 0.0031 | - |
4.0910 | 15550 | 0.002 | - |
4.1042 | 15600 | 0.0023 | - |
4.1173 | 15650 | 0.0018 | - |
4.1305 | 15700 | 0.0018 | - |
4.1436 | 15750 | 0.0025 | - |
4.1568 | 15800 | 0.0014 | - |
4.1700 | 15850 | 0.0019 | - |
4.1831 | 15900 | 0.0016 | - |
4.1963 | 15950 | 0.0026 | - |
4.2094 | 16000 | 0.002 | - |
4.2226 | 16050 | 0.0028 | - |
4.2357 | 16100 | 0.0012 | - |
4.2489 | 16150 | 0.0018 | - |
4.2620 | 16200 | 0.0017 | - |
4.2752 | 16250 | 0.0021 | - |
4.2883 | 16300 | 0.002 | - |
4.3015 | 16350 | 0.0015 | - |
4.3147 | 16400 | 0.0012 | - |
4.3278 | 16450 | 0.0019 | - |
4.3410 | 16500 | 0.0015 | - |
4.3541 | 16550 | 0.0014 | - |
4.3673 | 16600 | 0.0013 | - |
4.3804 | 16650 | 0.0021 | - |
4.3936 | 16700 | 0.0009 | - |
4.4067 | 16750 | 0.0023 | - |
4.4199 | 16800 | 0.0025 | - |
4.4330 | 16850 | 0.0023 | - |
4.4462 | 16900 | 0.0018 | - |
4.4594 | 16950 | 0.0023 | - |
4.4725 | 17000 | 0.002 | - |
4.4857 | 17050 | 0.0019 | - |
4.4988 | 17100 | 0.003 | - |
4.5120 | 17150 | 0.0033 | - |
4.5251 | 17200 | 0.002 | - |
4.5383 | 17250 | 0.0019 | - |
4.5514 | 17300 | 0.0016 | - |
4.5646 | 17350 | 0.0015 | - |
4.5777 | 17400 | 0.0026 | - |
4.5909 | 17450 | 0.0016 | - |
4.6041 | 17500 | 0.0017 | - |
4.6172 | 17550 | 0.0015 | - |
4.6304 | 17600 | 0.0023 | - |
4.6435 | 17650 | 0.0024 | - |
4.6567 | 17700 | 0.0008 | - |
4.6698 | 17750 | 0.0025 | - |
4.6830 | 17800 | 0.0011 | - |
4.6961 | 17850 | 0.0013 | - |
4.7093 | 17900 | 0.0015 | - |
4.7224 | 17950 | 0.0015 | - |
4.7356 | 18000 | 0.0021 | - |
4.7488 | 18050 | 0.0042 | - |
4.7619 | 18100 | 0.0021 | - |
4.7751 | 18150 | 0.0026 | - |
4.7882 | 18200 | 0.0019 | - |
4.8014 | 18250 | 0.0022 | - |
4.8145 | 18300 | 0.0032 | - |
4.8277 | 18350 | 0.0018 | - |
4.8408 | 18400 | 0.002 | - |
4.8540 | 18450 | 0.0018 | - |
4.8671 | 18500 | 0.001 | - |
4.8803 | 18550 | 0.0015 | - |
4.8934 | 18600 | 0.0011 | - |
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4.9198 | 18700 | 0.0006 | - |
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4.9987 | 19000 | 0.0017 | - |
5.0118 | 19050 | 0.0015 | - |
5.0250 | 19100 | 0.0017 | - |
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5.0513 | 19200 | 0.0014 | - |
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5.0776 | 19300 | 0.0028 | - |
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7.4454 | 28300 | 0.0029 | - |
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10.2736 | 39050 | 0.0009 | - |
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22.9413 | 87200 | 0.0001 | - |
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24.2042 | 92000 | 0.0001 | - |
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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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