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: 120 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 |
---|---|
19 |
|
104 |
|
77 |
|
8 |
|
30 |
|
99 |
|
61 |
|
36 |
|
37 |
|
91 |
|
108 |
|
60 |
|
66 |
|
26 |
|
27 |
|
53 |
|
3 |
|
48 |
|
39 |
|
4 |
|
62 |
|
67 |
|
71 |
|
94 |
|
79 |
|
72 |
|
5 |
|
100 |
|
51 |
|
38 |
|
70 |
|
1 |
|
117 |
|
55 |
|
98 |
|
118 |
|
12 |
|
97 |
|
90 |
|
83 |
|
35 |
|
113 |
|
80 |
|
88 |
|
84 |
|
65 |
|
59 |
|
52 |
|
107 |
|
28 |
|
43 |
|
63 |
|
34 |
|
21 |
|
54 |
|
101 |
|
115 |
|
44 |
|
42 |
|
73 |
|
68 |
|
9 |
|
25 |
|
116 |
|
112 |
|
120 |
|
6 |
|
23 |
|
15 |
|
96 |
|
95 |
|
92 |
|
103 |
|
85 |
|
20 |
|
47 |
|
33 |
|
106 |
|
78 |
|
86 |
|
24 |
|
111 |
|
10 |
|
57 |
|
105 |
|
13 |
|
18 |
|
64 |
|
76 |
|
89 |
|
14 |
|
87 |
|
0 |
|
75 |
|
2 |
|
45 |
|
50 |
|
114 |
|
41 |
|
58 |
|
109 |
|
32 |
|
40 |
|
110 |
|
69 |
|
119 |
|
93 |
|
22 |
|
102 |
|
49 |
|
81 |
|
16 |
|
56 |
|
82 |
|
31 |
|
7 |
|
29 |
|
46 |
|
74 |
|
17 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.6668 |
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_flat")
# Run inference
preds = model("나투리아 케라틴 워터팩 250g 옵션없음 나투리아 공식몰")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 3 | 9.3949 | 26 |
Label | Training Sample Count |
---|---|
0 | 12 |
1 | 22 |
2 | 19 |
3 | 17 |
4 | 25 |
5 | 20 |
6 | 17 |
7 | 10 |
8 | 22 |
9 | 11 |
10 | 18 |
12 | 19 |
13 | 18 |
14 | 21 |
15 | 16 |
16 | 16 |
17 | 10 |
18 | 19 |
19 | 32 |
20 | 20 |
21 | 16 |
22 | 18 |
23 | 20 |
24 | 21 |
25 | 10 |
26 | 19 |
27 | 42 |
28 | 15 |
29 | 18 |
30 | 23 |
31 | 12 |
32 | 22 |
33 | 21 |
34 | 21 |
35 | 20 |
36 | 23 |
37 | 20 |
38 | 15 |
39 | 20 |
40 | 22 |
41 | 20 |
42 | 11 |
43 | 21 |
44 | 15 |
45 | 20 |
46 | 23 |
47 | 19 |
48 | 21 |
49 | 19 |
50 | 21 |
51 | 10 |
52 | 28 |
53 | 27 |
54 | 13 |
55 | 12 |
56 | 12 |
57 | 12 |
58 | 20 |
59 | 19 |
60 | 15 |
61 | 19 |
62 | 19 |
63 | 21 |
64 | 31 |
65 | 20 |
66 | 21 |
67 | 15 |
68 | 22 |
69 | 24 |
70 | 18 |
71 | 19 |
72 | 16 |
73 | 22 |
74 | 10 |
75 | 20 |
76 | 15 |
77 | 23 |
78 | 17 |
79 | 20 |
80 | 28 |
81 | 14 |
82 | 17 |
83 | 32 |
84 | 23 |
85 | 22 |
86 | 18 |
87 | 23 |
88 | 18 |
89 | 30 |
90 | 20 |
91 | 40 |
92 | 22 |
93 | 15 |
94 | 27 |
95 | 17 |
96 | 20 |
97 | 20 |
98 | 25 |
99 | 20 |
100 | 20 |
101 | 20 |
102 | 18 |
103 | 27 |
104 | 20 |
105 | 23 |
106 | 20 |
107 | 19 |
108 | 14 |
109 | 25 |
110 | 25 |
111 | 15 |
112 | 19 |
113 | 20 |
114 | 12 |
115 | 28 |
116 | 23 |
117 | 18 |
118 | 14 |
119 | 18 |
120 | 19 |
Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (70, 70)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 120
- 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 |
---|---|---|---|
0.0018 | 1 | 0.3884 | - |
0.0903 | 50 | 0.3779 | - |
0.1805 | 100 | 0.3533 | - |
0.2708 | 150 | 0.3071 | - |
0.3610 | 200 | 0.2718 | - |
0.4513 | 250 | 0.2412 | - |
0.5415 | 300 | 0.2182 | - |
0.6318 | 350 | 0.1985 | - |
0.7220 | 400 | 0.1813 | - |
0.8123 | 450 | 0.164 | - |
0.9025 | 500 | 0.1489 | - |
0.9928 | 550 | 0.1352 | - |
1.0830 | 600 | 0.12 | - |
1.1733 | 650 | 0.1097 | - |
1.2635 | 700 | 0.0982 | - |
1.3538 | 750 | 0.0874 | - |
1.4440 | 800 | 0.0783 | - |
1.5343 | 850 | 0.0704 | - |
1.6245 | 900 | 0.0652 | - |
1.7148 | 950 | 0.0599 | - |
1.8051 | 1000 | 0.0562 | - |
1.8953 | 1050 | 0.0524 | - |
1.9856 | 1100 | 0.0507 | - |
2.0758 | 1150 | 0.0455 | - |
2.1661 | 1200 | 0.0434 | - |
2.2563 | 1250 | 0.0424 | - |
2.3466 | 1300 | 0.0403 | - |
2.4368 | 1350 | 0.038 | - |
2.5271 | 1400 | 0.0365 | - |
2.6173 | 1450 | 0.0357 | - |
2.7076 | 1500 | 0.0338 | - |
2.7978 | 1550 | 0.0328 | - |
2.8881 | 1600 | 0.0311 | - |
2.9783 | 1650 | 0.0304 | - |
3.0686 | 1700 | 0.028 | - |
3.1588 | 1750 | 0.0267 | - |
3.2491 | 1800 | 0.0254 | - |
3.3394 | 1850 | 0.0253 | - |
3.4296 | 1900 | 0.024 | - |
3.5199 | 1950 | 0.0217 | - |
3.6101 | 2000 | 0.0214 | - |
3.7004 | 2050 | 0.0207 | - |
3.7906 | 2100 | 0.0197 | - |
3.8809 | 2150 | 0.0187 | - |
3.9711 | 2200 | 0.0179 | - |
4.0614 | 2250 | 0.0177 | - |
4.1516 | 2300 | 0.0163 | - |
4.2419 | 2350 | 0.0157 | - |
4.3321 | 2400 | 0.0155 | - |
4.4224 | 2450 | 0.0155 | - |
4.5126 | 2500 | 0.0139 | - |
4.6029 | 2550 | 0.0133 | - |
4.6931 | 2600 | 0.0126 | - |
4.7834 | 2650 | 0.0127 | - |
4.8736 | 2700 | 0.012 | - |
4.9639 | 2750 | 0.0122 | - |
5.0542 | 2800 | 0.0115 | - |
5.1444 | 2850 | 0.0109 | - |
5.2347 | 2900 | 0.0101 | - |
5.3249 | 2950 | 0.0102 | - |
5.4152 | 3000 | 0.0093 | - |
5.5054 | 3050 | 0.0098 | - |
5.5957 | 3100 | 0.0095 | - |
5.6859 | 3150 | 0.0089 | - |
5.7762 | 3200 | 0.0083 | - |
5.8664 | 3250 | 0.0087 | - |
5.9567 | 3300 | 0.0083 | - |
6.0469 | 3350 | 0.0083 | - |
6.1372 | 3400 | 0.008 | - |
6.2274 | 3450 | 0.0073 | - |
6.3177 | 3500 | 0.0075 | - |
6.4079 | 3550 | 0.0073 | - |
6.4982 | 3600 | 0.0066 | - |
6.5884 | 3650 | 0.0062 | - |
6.6787 | 3700 | 0.0061 | - |
6.7690 | 3750 | 0.0066 | - |
6.8592 | 3800 | 0.0062 | - |
6.9495 | 3850 | 0.0056 | - |
7.0397 | 3900 | 0.006 | - |
7.1300 | 3950 | 0.0057 | - |
7.2202 | 4000 | 0.0054 | - |
7.3105 | 4050 | 0.0051 | - |
7.4007 | 4100 | 0.0055 | - |
7.4910 | 4150 | 0.0047 | - |
7.5812 | 4200 | 0.0048 | - |
7.6715 | 4250 | 0.0048 | - |
7.7617 | 4300 | 0.0049 | - |
7.8520 | 4350 | 0.0046 | - |
7.9422 | 4400 | 0.0045 | - |
8.0325 | 4450 | 0.0046 | - |
8.1227 | 4500 | 0.0045 | - |
8.2130 | 4550 | 0.0044 | - |
8.3032 | 4600 | 0.0048 | - |
8.3935 | 4650 | 0.0044 | - |
8.4838 | 4700 | 0.004 | - |
8.5740 | 4750 | 0.0042 | - |
8.6643 | 4800 | 0.0043 | - |
8.7545 | 4850 | 0.0039 | - |
8.8448 | 4900 | 0.0036 | - |
8.9350 | 4950 | 0.0037 | - |
9.0253 | 5000 | 0.0032 | - |
9.1155 | 5050 | 0.0032 | - |
9.2058 | 5100 | 0.0035 | - |
9.2960 | 5150 | 0.0031 | - |
9.3863 | 5200 | 0.0035 | - |
9.4765 | 5250 | 0.0033 | - |
9.5668 | 5300 | 0.0032 | - |
9.6570 | 5350 | 0.0031 | - |
9.7473 | 5400 | 0.0032 | - |
9.8375 | 5450 | 0.0029 | - |
9.9278 | 5500 | 0.0028 | - |
10.0181 | 5550 | 0.0029 | - |
10.1083 | 5600 | 0.0029 | - |
10.1986 | 5650 | 0.0024 | - |
10.2888 | 5700 | 0.0027 | - |
10.3791 | 5750 | 0.0026 | - |
10.4693 | 5800 | 0.0028 | - |
10.5596 | 5850 | 0.0026 | - |
10.6498 | 5900 | 0.0021 | - |
10.7401 | 5950 | 0.0022 | - |
10.8303 | 6000 | 0.0024 | - |
10.9206 | 6050 | 0.0022 | - |
11.0108 | 6100 | 0.0021 | - |
11.1011 | 6150 | 0.0022 | - |
11.1913 | 6200 | 0.0018 | - |
11.2816 | 6250 | 0.0017 | - |
11.3718 | 6300 | 0.0016 | - |
11.4621 | 6350 | 0.0015 | - |
11.5523 | 6400 | 0.0016 | - |
11.6426 | 6450 | 0.0013 | - |
11.7329 | 6500 | 0.0013 | - |
11.8231 | 6550 | 0.0014 | - |
11.9134 | 6600 | 0.0012 | - |
12.0036 | 6650 | 0.0014 | - |
12.0939 | 6700 | 0.0012 | - |
12.1841 | 6750 | 0.0011 | - |
12.2744 | 6800 | 0.0009 | - |
12.3646 | 6850 | 0.001 | - |
12.4549 | 6900 | 0.0012 | - |
12.5451 | 6950 | 0.001 | - |
12.6354 | 7000 | 0.001 | - |
12.7256 | 7050 | 0.001 | - |
12.8159 | 7100 | 0.001 | - |
12.9061 | 7150 | 0.001 | - |
12.9964 | 7200 | 0.0009 | - |
13.0866 | 7250 | 0.0011 | - |
13.1769 | 7300 | 0.0009 | - |
13.2671 | 7350 | 0.0009 | - |
13.3574 | 7400 | 0.0009 | - |
13.4477 | 7450 | 0.0009 | - |
13.5379 | 7500 | 0.0008 | - |
13.6282 | 7550 | 0.0006 | - |
13.7184 | 7600 | 0.0005 | - |
13.8087 | 7650 | 0.0006 | - |
13.8989 | 7700 | 0.0005 | - |
13.9892 | 7750 | 0.0005 | - |
14.0794 | 7800 | 0.0005 | - |
14.1697 | 7850 | 0.0005 | - |
14.2599 | 7900 | 0.0004 | - |
14.3502 | 7950 | 0.0004 | - |
14.4404 | 8000 | 0.0004 | - |
14.5307 | 8050 | 0.0003 | - |
14.6209 | 8100 | 0.0004 | - |
14.7112 | 8150 | 0.0005 | - |
14.8014 | 8200 | 0.0004 | - |
14.8917 | 8250 | 0.0004 | - |
14.9819 | 8300 | 0.0003 | - |
15.0722 | 8350 | 0.0004 | - |
15.1625 | 8400 | 0.0003 | - |
15.2527 | 8450 | 0.0004 | - |
15.3430 | 8500 | 0.0004 | - |
15.4332 | 8550 | 0.0003 | - |
15.5235 | 8600 | 0.0002 | - |
15.6137 | 8650 | 0.0003 | - |
15.7040 | 8700 | 0.0003 | - |
15.7942 | 8750 | 0.0003 | - |
15.8845 | 8800 | 0.0003 | - |
15.9747 | 8850 | 0.0003 | - |
16.0650 | 8900 | 0.0003 | - |
16.1552 | 8950 | 0.0002 | - |
16.2455 | 9000 | 0.0004 | - |
16.3357 | 9050 | 0.0002 | - |
16.4260 | 9100 | 0.0002 | - |
16.5162 | 9150 | 0.0003 | - |
16.6065 | 9200 | 0.0005 | - |
16.6968 | 9250 | 0.0015 | - |
16.7870 | 9300 | 0.0006 | - |
16.8773 | 9350 | 0.0004 | - |
16.9675 | 9400 | 0.0004 | - |
17.0578 | 9450 | 0.0004 | - |
17.1480 | 9500 | 0.0003 | - |
17.2383 | 9550 | 0.0003 | - |
17.3285 | 9600 | 0.0003 | - |
17.4188 | 9650 | 0.0002 | - |
17.5090 | 9700 | 0.0003 | - |
17.5993 | 9750 | 0.0002 | - |
17.6895 | 9800 | 0.0002 | - |
17.7798 | 9850 | 0.0002 | - |
17.8700 | 9900 | 0.0002 | - |
17.9603 | 9950 | 0.0002 | - |
18.0505 | 10000 | 0.0002 | - |
18.1408 | 10050 | 0.0001 | - |
18.2310 | 10100 | 0.0002 | - |
18.3213 | 10150 | 0.0001 | - |
18.4116 | 10200 | 0.0001 | - |
18.5018 | 10250 | 0.0001 | - |
18.5921 | 10300 | 0.0001 | - |
18.6823 | 10350 | 0.0001 | - |
18.7726 | 10400 | 0.0001 | - |
18.8628 | 10450 | 0.0002 | - |
18.9531 | 10500 | 0.0001 | - |
19.0433 | 10550 | 0.0001 | - |
19.1336 | 10600 | 0.0001 | - |
19.2238 | 10650 | 0.0001 | - |
19.3141 | 10700 | 0.0001 | - |
19.4043 | 10750 | 0.0001 | - |
19.4946 | 10800 | 0.0001 | - |
19.5848 | 10850 | 0.0001 | - |
19.6751 | 10900 | 0.0001 | - |
19.7653 | 10950 | 0.0001 | - |
19.8556 | 11000 | 0.0001 | - |
19.9458 | 11050 | 0.0001 | - |
20.0361 | 11100 | 0.0001 | - |
20.1264 | 11150 | 0.0001 | - |
20.2166 | 11200 | 0.0001 | - |
20.3069 | 11250 | 0.0001 | - |
20.3971 | 11300 | 0.0001 | - |
20.4874 | 11350 | 0.0001 | - |
20.5776 | 11400 | 0.0001 | - |
20.6679 | 11450 | 0.0001 | - |
20.7581 | 11500 | 0.0001 | - |
20.8484 | 11550 | 0.0001 | - |
20.9386 | 11600 | 0.0001 | - |
21.0289 | 11650 | 0.0001 | - |
21.1191 | 11700 | 0.0001 | - |
21.2094 | 11750 | 0.0 | - |
21.2996 | 11800 | 0.0 | - |
21.3899 | 11850 | 0.0 | - |
21.4801 | 11900 | 0.0 | - |
21.5704 | 11950 | 0.0 | - |
21.6606 | 12000 | 0.0 | - |
21.7509 | 12050 | 0.0 | - |
21.8412 | 12100 | 0.0 | - |
21.9314 | 12150 | 0.0 | - |
22.0217 | 12200 | 0.0 | - |
22.1119 | 12250 | 0.0 | - |
22.2022 | 12300 | 0.0 | - |
22.2924 | 12350 | 0.0 | - |
22.3827 | 12400 | 0.0 | - |
22.4729 | 12450 | 0.0 | - |
22.5632 | 12500 | 0.0 | - |
22.6534 | 12550 | 0.0 | - |
22.7437 | 12600 | 0.0 | - |
22.8339 | 12650 | 0.0 | - |
22.9242 | 12700 | 0.0 | - |
23.0144 | 12750 | 0.0 | - |
23.1047 | 12800 | 0.0011 | - |
23.1949 | 12850 | 0.0012 | - |
23.2852 | 12900 | 0.0004 | - |
23.3755 | 12950 | 0.0004 | - |
23.4657 | 13000 | 0.0002 | - |
23.5560 | 13050 | 0.0002 | - |
23.6462 | 13100 | 0.0003 | - |
23.7365 | 13150 | 0.0002 | - |
23.8267 | 13200 | 0.0001 | - |
23.9170 | 13250 | 0.0002 | - |
24.0072 | 13300 | 0.0001 | - |
24.0975 | 13350 | 0.0001 | - |
24.1877 | 13400 | 0.0001 | - |
24.2780 | 13450 | 0.0001 | - |
24.3682 | 13500 | 0.0001 | - |
24.4585 | 13550 | 0.0001 | - |
24.5487 | 13600 | 0.0001 | - |
24.6390 | 13650 | 0.0001 | - |
24.7292 | 13700 | 0.0001 | - |
24.8195 | 13750 | 0.0002 | - |
24.9097 | 13800 | 0.0001 | - |
25.0 | 13850 | 0.0001 | - |
25.0903 | 13900 | 0.0001 | - |
25.1805 | 13950 | 0.0001 | - |
25.2708 | 14000 | 0.0001 | - |
25.3610 | 14050 | 0.0001 | - |
25.4513 | 14100 | 0.0001 | - |
25.5415 | 14150 | 0.0001 | - |
25.6318 | 14200 | 0.0001 | - |
25.7220 | 14250 | 0.0001 | - |
25.8123 | 14300 | 0.0001 | - |
25.9025 | 14350 | 0.0 | - |
25.9928 | 14400 | 0.0 | - |
26.0830 | 14450 | 0.0 | - |
26.1733 | 14500 | 0.0 | - |
26.2635 | 14550 | 0.0 | - |
26.3538 | 14600 | 0.0 | - |
26.4440 | 14650 | 0.0 | - |
26.5343 | 14700 | 0.0 | - |
26.6245 | 14750 | 0.0 | - |
26.7148 | 14800 | 0.0 | - |
26.8051 | 14850 | 0.0 | - |
26.8953 | 14900 | 0.0 | - |
26.9856 | 14950 | 0.0 | - |
27.0758 | 15000 | 0.0 | - |
27.1661 | 15050 | 0.0 | - |
27.2563 | 15100 | 0.0 | - |
27.3466 | 15150 | 0.0001 | - |
27.4368 | 15200 | 0.0004 | - |
27.5271 | 15250 | 0.0006 | - |
27.6173 | 15300 | 0.0002 | - |
27.7076 | 15350 | 0.0001 | - |
27.7978 | 15400 | 0.0002 | - |
27.8881 | 15450 | 0.0002 | - |
27.9783 | 15500 | 0.0001 | - |
28.0686 | 15550 | 0.0001 | - |
28.1588 | 15600 | 0.0001 | - |
28.2491 | 15650 | 0.0 | - |
28.3394 | 15700 | 0.0 | - |
28.4296 | 15750 | 0.0 | - |
28.5199 | 15800 | 0.0 | - |
28.6101 | 15850 | 0.0 | - |
28.7004 | 15900 | 0.0 | - |
28.7906 | 15950 | 0.0 | - |
28.8809 | 16000 | 0.0 | - |
28.9711 | 16050 | 0.0 | - |
29.0614 | 16100 | 0.0 | - |
29.1516 | 16150 | 0.0 | - |
29.2419 | 16200 | 0.0 | - |
29.3321 | 16250 | 0.0 | - |
29.4224 | 16300 | 0.0 | - |
29.5126 | 16350 | 0.0 | - |
29.6029 | 16400 | 0.0 | - |
29.6931 | 16450 | 0.0 | - |
29.7834 | 16500 | 0.0 | - |
29.8736 | 16550 | 0.0 | - |
29.9639 | 16600 | 0.0 | - |
30.0542 | 16650 | 0.0 | - |
30.1444 | 16700 | 0.0 | - |
30.2347 | 16750 | 0.0 | - |
30.3249 | 16800 | 0.0 | - |
30.4152 | 16850 | 0.0 | - |
30.5054 | 16900 | 0.0 | - |
30.5957 | 16950 | 0.0 | - |
30.6859 | 17000 | 0.0 | - |
30.7762 | 17050 | 0.0 | - |
30.8664 | 17100 | 0.0 | - |
30.9567 | 17150 | 0.0 | - |
31.0469 | 17200 | 0.0 | - |
31.1372 | 17250 | 0.0 | - |
31.2274 | 17300 | 0.0 | - |
31.3177 | 17350 | 0.0 | - |
31.4079 | 17400 | 0.0 | - |
31.4982 | 17450 | 0.0 | - |
31.5884 | 17500 | 0.0 | - |
31.6787 | 17550 | 0.0 | - |
31.7690 | 17600 | 0.0 | - |
31.8592 | 17650 | 0.0 | - |
31.9495 | 17700 | 0.0 | - |
32.0397 | 17750 | 0.0 | - |
32.1300 | 17800 | 0.0 | - |
32.2202 | 17850 | 0.0 | - |
32.3105 | 17900 | 0.0 | - |
32.4007 | 17950 | 0.0 | - |
32.4910 | 18000 | 0.0 | - |
32.5812 | 18050 | 0.0 | - |
32.6715 | 18100 | 0.0 | - |
32.7617 | 18150 | 0.0 | - |
32.8520 | 18200 | 0.0 | - |
32.9422 | 18250 | 0.0 | - |
33.0325 | 18300 | 0.0 | - |
33.1227 | 18350 | 0.0 | - |
33.2130 | 18400 | 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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