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 |
---|---|
30 |
|
23 |
|
67 |
|
76 |
|
12 |
|
1 |
|
56 |
|
77 |
|
44 |
|
43 |
|
75 |
|
71 |
|
18 |
|
84 |
|
14 |
|
26 |
|
9 |
|
50 |
|
97 |
|
58 |
|
61 |
|
94 |
|
79 |
|
13 |
|
53 |
|
24 |
|
62 |
|
45 |
|
81 |
|
6 |
|
33 |
|
57 |
|
54 |
|
7 |
|
59 |
|
99 |
|
69 |
|
29 |
|
83 |
|
66 |
|
87 |
|
5 |
|
95 |
|
64 |
|
86 |
|
42 |
|
21 |
|
98 |
|
17 |
|
96 |
|
91 |
|
32 |
|
72 |
|
73 |
|
28 |
|
11 |
|
82 |
|
63 |
|
49 |
|
34 |
|
80 |
|
78 |
|
51 |
|
60 |
|
90 |
|
36 |
|
4 |
|
22 |
|
41 |
|
68 |
|
85 |
|
15 |
|
55 |
|
89 |
|
10 |
|
88 |
|
39 |
|
40 |
|
2 |
|
37 |
|
8 |
|
70 |
|
19 |
|
74 |
|
0 |
|
65 |
|
93 |
|
48 |
|
46 |
|
52 |
|
38 |
|
3 |
|
35 |
|
31 |
|
47 |
|
92 |
|
20 |
|
27 |
|
16 |
|
25 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.835 |
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")
# Run inference
preds = model("일리윤 프레쉬 모이스춰 바디워시 500ml 6개/무배 바디케어 바디워시 바디클렌저")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 3 | 15.699 | 165 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 50 |
8 | 50 |
9 | 50 |
10 | 50 |
11 | 50 |
12 | 50 |
13 | 50 |
14 | 50 |
15 | 50 |
16 | 50 |
17 | 50 |
18 | 50 |
19 | 50 |
20 | 50 |
21 | 50 |
22 | 50 |
23 | 50 |
24 | 50 |
25 | 50 |
26 | 50 |
27 | 50 |
28 | 50 |
29 | 50 |
30 | 50 |
31 | 50 |
32 | 50 |
33 | 50 |
34 | 50 |
35 | 50 |
36 | 50 |
37 | 50 |
38 | 50 |
39 | 50 |
40 | 50 |
41 | 50 |
42 | 50 |
43 | 50 |
44 | 50 |
45 | 50 |
46 | 50 |
47 | 50 |
48 | 50 |
49 | 50 |
50 | 50 |
51 | 50 |
52 | 50 |
53 | 50 |
54 | 50 |
55 | 50 |
56 | 50 |
57 | 50 |
58 | 50 |
59 | 50 |
60 | 50 |
61 | 50 |
62 | 50 |
63 | 50 |
64 | 50 |
65 | 50 |
66 | 50 |
67 | 50 |
68 | 50 |
69 | 50 |
70 | 50 |
71 | 50 |
72 | 50 |
73 | 50 |
74 | 50 |
75 | 50 |
76 | 50 |
77 | 50 |
78 | 50 |
79 | 50 |
80 | 50 |
81 | 50 |
82 | 50 |
83 | 50 |
84 | 50 |
85 | 50 |
86 | 50 |
87 | 50 |
88 | 50 |
89 | 50 |
90 | 50 |
91 | 50 |
92 | 50 |
93 | 50 |
94 | 50 |
95 | 50 |
96 | 50 |
97 | 50 |
98 | 50 |
99 | 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 |
---|---|---|---|
0.0004 | 1 | 0.4752 | - |
0.0213 | 50 | 0.3961 | - |
0.0427 | 100 | 0.4011 | - |
0.0640 | 150 | 0.3747 | - |
0.0853 | 200 | 0.3511 | - |
0.1067 | 250 | 0.3206 | - |
0.1280 | 300 | 0.2945 | - |
0.1493 | 350 | 0.2751 | - |
0.1706 | 400 | 0.2609 | - |
0.1920 | 450 | 0.2486 | - |
0.2133 | 500 | 0.231 | - |
0.2346 | 550 | 0.207 | - |
0.2560 | 600 | 0.1875 | - |
0.2773 | 650 | 0.1697 | - |
0.2986 | 700 | 0.1493 | - |
0.3200 | 750 | 0.1302 | - |
0.3413 | 800 | 0.12 | - |
0.3626 | 850 | 0.1103 | - |
0.3840 | 900 | 0.1089 | - |
0.4053 | 950 | 0.0974 | - |
0.4266 | 1000 | 0.0907 | - |
0.4480 | 1050 | 0.0866 | - |
0.4693 | 1100 | 0.0818 | - |
0.4906 | 1150 | 0.0787 | - |
0.5119 | 1200 | 0.0736 | - |
0.5333 | 1250 | 0.0724 | - |
0.5546 | 1300 | 0.069 | - |
0.5759 | 1350 | 0.0645 | - |
0.5973 | 1400 | 0.0617 | - |
0.6186 | 1450 | 0.0577 | - |
0.6399 | 1500 | 0.0527 | - |
0.6613 | 1550 | 0.0511 | - |
0.6826 | 1600 | 0.0489 | - |
0.7039 | 1650 | 0.05 | - |
0.7253 | 1700 | 0.0464 | - |
0.7466 | 1750 | 0.0438 | - |
0.7679 | 1800 | 0.0457 | - |
0.7892 | 1850 | 0.0393 | - |
0.8106 | 1900 | 0.0391 | - |
0.8319 | 1950 | 0.0383 | - |
0.8532 | 2000 | 0.0368 | - |
0.8746 | 2050 | 0.0366 | - |
0.8959 | 2100 | 0.031 | - |
0.9172 | 2150 | 0.0354 | - |
0.9386 | 2200 | 0.0318 | - |
0.9599 | 2250 | 0.0319 | - |
0.9812 | 2300 | 0.0319 | - |
1.0026 | 2350 | 0.0302 | - |
1.0239 | 2400 | 0.0284 | - |
1.0452 | 2450 | 0.028 | - |
1.0666 | 2500 | 0.03 | - |
1.0879 | 2550 | 0.0262 | - |
1.1092 | 2600 | 0.0241 | - |
1.1305 | 2650 | 0.027 | - |
1.1519 | 2700 | 0.0245 | - |
1.1732 | 2750 | 0.0221 | - |
1.1945 | 2800 | 0.0267 | - |
1.2159 | 2850 | 0.0207 | - |
1.2372 | 2900 | 0.0236 | - |
1.2585 | 2950 | 0.0227 | - |
1.2799 | 3000 | 0.0187 | - |
1.3012 | 3050 | 0.0237 | - |
1.3225 | 3100 | 0.0202 | - |
1.3439 | 3150 | 0.0217 | - |
1.3652 | 3200 | 0.0187 | - |
1.3865 | 3250 | 0.0203 | - |
1.4078 | 3300 | 0.0185 | - |
1.4292 | 3350 | 0.0209 | - |
1.4505 | 3400 | 0.0188 | - |
1.4718 | 3450 | 0.0194 | - |
1.4932 | 3500 | 0.0173 | - |
1.5145 | 3550 | 0.0192 | - |
1.5358 | 3600 | 0.0165 | - |
1.5572 | 3650 | 0.0174 | - |
1.5785 | 3700 | 0.0168 | - |
1.5998 | 3750 | 0.0171 | - |
1.6212 | 3800 | 0.0188 | - |
1.6425 | 3850 | 0.0157 | - |
1.6638 | 3900 | 0.018 | - |
1.6852 | 3950 | 0.0166 | - |
1.7065 | 4000 | 0.0145 | - |
1.7278 | 4050 | 0.0157 | - |
1.7491 | 4100 | 0.0162 | - |
1.7705 | 4150 | 0.0169 | - |
1.7918 | 4200 | 0.0175 | - |
1.8131 | 4250 | 0.0156 | - |
1.8345 | 4300 | 0.0161 | - |
1.8558 | 4350 | 0.0134 | - |
1.8771 | 4400 | 0.0133 | - |
1.8985 | 4450 | 0.012 | - |
1.9198 | 4500 | 0.0137 | - |
1.9411 | 4550 | 0.0125 | - |
1.9625 | 4600 | 0.0145 | - |
1.9838 | 4650 | 0.0135 | - |
2.0051 | 4700 | 0.0141 | - |
2.0265 | 4750 | 0.0135 | - |
2.0478 | 4800 | 0.0123 | - |
2.0691 | 4850 | 0.0145 | - |
2.0904 | 4900 | 0.0138 | - |
2.1118 | 4950 | 0.0117 | - |
2.1331 | 5000 | 0.0124 | - |
2.1544 | 5050 | 0.0118 | - |
2.1758 | 5100 | 0.0107 | - |
2.1971 | 5150 | 0.0101 | - |
2.2184 | 5200 | 0.0106 | - |
2.2398 | 5250 | 0.0114 | - |
2.2611 | 5300 | 0.0093 | - |
2.2824 | 5350 | 0.0099 | - |
2.3038 | 5400 | 0.0108 | - |
2.3251 | 5450 | 0.0105 | - |
2.3464 | 5500 | 0.0092 | - |
2.3677 | 5550 | 0.0098 | - |
2.3891 | 5600 | 0.0098 | - |
2.4104 | 5650 | 0.0083 | - |
2.4317 | 5700 | 0.0108 | - |
2.4531 | 5750 | 0.0099 | - |
2.4744 | 5800 | 0.0097 | - |
2.4957 | 5850 | 0.0089 | - |
2.5171 | 5900 | 0.008 | - |
2.5384 | 5950 | 0.0097 | - |
2.5597 | 6000 | 0.0092 | - |
2.5811 | 6050 | 0.0079 | - |
2.6024 | 6100 | 0.0105 | - |
2.6237 | 6150 | 0.0093 | - |
2.6451 | 6200 | 0.0082 | - |
2.6664 | 6250 | 0.0084 | - |
2.6877 | 6300 | 0.009 | - |
2.7090 | 6350 | 0.0094 | - |
2.7304 | 6400 | 0.0074 | - |
2.7517 | 6450 | 0.0076 | - |
2.7730 | 6500 | 0.0087 | - |
2.7944 | 6550 | 0.0071 | - |
2.8157 | 6600 | 0.0077 | - |
2.8370 | 6650 | 0.0064 | - |
2.8584 | 6700 | 0.0064 | - |
2.8797 | 6750 | 0.0071 | - |
2.9010 | 6800 | 0.0084 | - |
2.9224 | 6850 | 0.0068 | - |
2.9437 | 6900 | 0.0068 | - |
2.9650 | 6950 | 0.0068 | - |
2.9863 | 7000 | 0.0069 | - |
3.0077 | 7050 | 0.0059 | - |
3.0290 | 7100 | 0.0062 | - |
3.0503 | 7150 | 0.0066 | - |
3.0717 | 7200 | 0.0048 | - |
3.0930 | 7250 | 0.0062 | - |
3.1143 | 7300 | 0.006 | - |
3.1357 | 7350 | 0.0053 | - |
3.1570 | 7400 | 0.0054 | - |
3.1783 | 7450 | 0.0059 | - |
3.1997 | 7500 | 0.0051 | - |
3.2210 | 7550 | 0.0048 | - |
3.2423 | 7600 | 0.004 | - |
3.2637 | 7650 | 0.0066 | - |
3.2850 | 7700 | 0.0061 | - |
3.3063 | 7750 | 0.0055 | - |
3.3276 | 7800 | 0.0045 | - |
3.3490 | 7850 | 0.0046 | - |
3.3703 | 7900 | 0.0045 | - |
3.3916 | 7950 | 0.0047 | - |
3.4130 | 8000 | 0.0042 | - |
3.4343 | 8050 | 0.0054 | - |
3.4556 | 8100 | 0.005 | - |
3.4770 | 8150 | 0.0062 | - |
3.4983 | 8200 | 0.0059 | - |
3.5196 | 8250 | 0.0056 | - |
3.5410 | 8300 | 0.0069 | - |
3.5623 | 8350 | 0.0059 | - |
3.5836 | 8400 | 0.006 | - |
3.6049 | 8450 | 0.0057 | - |
3.6263 | 8500 | 0.0059 | - |
3.6476 | 8550 | 0.0063 | - |
3.6689 | 8600 | 0.0044 | - |
3.6903 | 8650 | 0.0063 | - |
3.7116 | 8700 | 0.0045 | - |
3.7329 | 8750 | 0.0049 | - |
3.7543 | 8800 | 0.0045 | - |
3.7756 | 8850 | 0.0041 | - |
3.7969 | 8900 | 0.0057 | - |
3.8183 | 8950 | 0.0039 | - |
3.8396 | 9000 | 0.0054 | - |
3.8609 | 9050 | 0.0038 | - |
3.8823 | 9100 | 0.005 | - |
3.9036 | 9150 | 0.0035 | - |
3.9249 | 9200 | 0.0043 | - |
3.9462 | 9250 | 0.0039 | - |
3.9676 | 9300 | 0.0038 | - |
3.9889 | 9350 | 0.0039 | - |
4.0102 | 9400 | 0.0039 | - |
4.0316 | 9450 | 0.0049 | - |
4.0529 | 9500 | 0.0052 | - |
4.0742 | 9550 | 0.0042 | - |
4.0956 | 9600 | 0.0046 | - |
4.1169 | 9650 | 0.0039 | - |
4.1382 | 9700 | 0.0036 | - |
4.1596 | 9750 | 0.0046 | - |
4.1809 | 9800 | 0.0035 | - |
4.2022 | 9850 | 0.0046 | - |
4.2235 | 9900 | 0.0029 | - |
4.2449 | 9950 | 0.0028 | - |
4.2662 | 10000 | 0.0038 | - |
4.2875 | 10050 | 0.0033 | - |
4.3089 | 10100 | 0.005 | - |
4.3302 | 10150 | 0.0037 | - |
4.3515 | 10200 | 0.0047 | - |
4.3729 | 10250 | 0.0051 | - |
4.3942 | 10300 | 0.0048 | - |
4.4155 | 10350 | 0.0038 | - |
4.4369 | 10400 | 0.0037 | - |
4.4582 | 10450 | 0.003 | - |
4.4795 | 10500 | 0.0031 | - |
4.5009 | 10550 | 0.0032 | - |
4.5222 | 10600 | 0.0042 | - |
4.5435 | 10650 | 0.0042 | - |
4.5648 | 10700 | 0.0031 | - |
4.5862 | 10750 | 0.003 | - |
4.6075 | 10800 | 0.0033 | - |
4.6288 | 10850 | 0.0022 | - |
4.6502 | 10900 | 0.0019 | - |
4.6715 | 10950 | 0.0026 | - |
4.6928 | 11000 | 0.0037 | - |
4.7142 | 11050 | 0.0032 | - |
4.7355 | 11100 | 0.0027 | - |
4.7568 | 11150 | 0.0033 | - |
4.7782 | 11200 | 0.0037 | - |
4.7995 | 11250 | 0.0027 | - |
4.8208 | 11300 | 0.0037 | - |
4.8422 | 11350 | 0.0047 | - |
4.8635 | 11400 | 0.0064 | - |
4.8848 | 11450 | 0.0043 | - |
4.9061 | 11500 | 0.0043 | - |
4.9275 | 11550 | 0.004 | - |
4.9488 | 11600 | 0.0035 | - |
4.9701 | 11650 | 0.0027 | - |
4.9915 | 11700 | 0.0033 | - |
5.0128 | 11750 | 0.0019 | - |
5.0341 | 11800 | 0.0028 | - |
5.0555 | 11850 | 0.0028 | - |
5.0768 | 11900 | 0.0028 | - |
5.0981 | 11950 | 0.0025 | - |
5.1195 | 12000 | 0.0019 | - |
5.1408 | 12050 | 0.0023 | - |
5.1621 | 12100 | 0.0023 | - |
5.1834 | 12150 | 0.0023 | - |
5.2048 | 12200 | 0.002 | - |
5.2261 | 12250 | 0.0029 | - |
5.2474 | 12300 | 0.0022 | - |
5.2688 | 12350 | 0.0031 | - |
5.2901 | 12400 | 0.0028 | - |
5.3114 | 12450 | 0.0024 | - |
5.3328 | 12500 | 0.0017 | - |
5.3541 | 12550 | 0.0033 | - |
5.3754 | 12600 | 0.0033 | - |
5.3968 | 12650 | 0.0028 | - |
5.4181 | 12700 | 0.0025 | - |
5.4394 | 12750 | 0.0027 | - |
5.4608 | 12800 | 0.0036 | - |
5.4821 | 12850 | 0.0019 | - |
5.5034 | 12900 | 0.0027 | - |
5.5247 | 12950 | 0.0029 | - |
5.5461 | 13000 | 0.0019 | - |
5.5674 | 13050 | 0.0033 | - |
5.5887 | 13100 | 0.0024 | - |
5.6101 | 13150 | 0.0022 | - |
5.6314 | 13200 | 0.0031 | - |
5.6527 | 13250 | 0.0048 | - |
5.6741 | 13300 | 0.0039 | - |
5.6954 | 13350 | 0.0041 | - |
5.7167 | 13400 | 0.003 | - |
5.7381 | 13450 | 0.003 | - |
5.7594 | 13500 | 0.003 | - |
5.7807 | 13550 | 0.0031 | - |
5.8020 | 13600 | 0.002 | - |
5.8234 | 13650 | 0.0033 | - |
5.8447 | 13700 | 0.0026 | - |
5.8660 | 13750 | 0.003 | - |
5.8874 | 13800 | 0.0021 | - |
5.9087 | 13850 | 0.003 | - |
5.9300 | 13900 | 0.0021 | - |
5.9514 | 13950 | 0.0016 | - |
5.9727 | 14000 | 0.0024 | - |
5.9940 | 14050 | 0.0018 | - |
6.0154 | 14100 | 0.0028 | - |
6.0367 | 14150 | 0.0024 | - |
6.0580 | 14200 | 0.0018 | - |
6.0794 | 14250 | 0.0021 | - |
6.1007 | 14300 | 0.0019 | - |
6.1220 | 14350 | 0.0026 | - |
6.1433 | 14400 | 0.0015 | - |
6.1647 | 14450 | 0.0021 | - |
6.1860 | 14500 | 0.0026 | - |
6.2073 | 14550 | 0.0017 | - |
6.2287 | 14600 | 0.0013 | - |
6.25 | 14650 | 0.0025 | - |
6.2713 | 14700 | 0.0007 | - |
6.2927 | 14750 | 0.0031 | - |
6.3140 | 14800 | 0.0024 | - |
6.3353 | 14850 | 0.0026 | - |
6.3567 | 14900 | 0.0029 | - |
6.3780 | 14950 | 0.0019 | - |
6.3993 | 15000 | 0.0015 | - |
6.4206 | 15050 | 0.0015 | - |
6.4420 | 15100 | 0.0015 | - |
6.4633 | 15150 | 0.0023 | - |
6.4846 | 15200 | 0.0011 | - |
6.5060 | 15250 | 0.0023 | - |
6.5273 | 15300 | 0.0021 | - |
6.5486 | 15350 | 0.0017 | - |
6.5700 | 15400 | 0.0024 | - |
6.5913 | 15450 | 0.0025 | - |
6.6126 | 15500 | 0.0035 | - |
6.6340 | 15550 | 0.0022 | - |
6.6553 | 15600 | 0.002 | - |
6.6766 | 15650 | 0.0025 | - |
6.6980 | 15700 | 0.0025 | - |
6.7193 | 15750 | 0.0027 | - |
6.7406 | 15800 | 0.0024 | - |
6.7619 | 15850 | 0.002 | - |
6.7833 | 15900 | 0.003 | - |
6.8046 | 15950 | 0.0024 | - |
6.8259 | 16000 | 0.0021 | - |
6.8473 | 16050 | 0.0036 | - |
6.8686 | 16100 | 0.0034 | - |
6.8899 | 16150 | 0.0023 | - |
6.9113 | 16200 | 0.0022 | - |
6.9326 | 16250 | 0.0025 | - |
6.9539 | 16300 | 0.0031 | - |
6.9753 | 16350 | 0.0038 | - |
6.9966 | 16400 | 0.0028 | - |
7.0179 | 16450 | 0.0023 | - |
7.0392 | 16500 | 0.0017 | - |
7.0606 | 16550 | 0.002 | - |
7.0819 | 16600 | 0.0027 | - |
7.1032 | 16650 | 0.0024 | - |
7.1246 | 16700 | 0.0028 | - |
7.1459 | 16750 | 0.0032 | - |
7.1672 | 16800 | 0.0023 | - |
7.1886 | 16850 | 0.0021 | - |
7.2099 | 16900 | 0.0025 | - |
7.2312 | 16950 | 0.0015 | - |
7.2526 | 17000 | 0.0022 | - |
7.2739 | 17050 | 0.0014 | - |
7.2952 | 17100 | 0.0018 | - |
7.3166 | 17150 | 0.0018 | - |
7.3379 | 17200 | 0.0021 | - |
7.3592 | 17250 | 0.0014 | - |
7.3805 | 17300 | 0.0019 | - |
7.4019 | 17350 | 0.002 | - |
7.4232 | 17400 | 0.0025 | - |
7.4445 | 17450 | 0.0018 | - |
7.4659 | 17500 | 0.0015 | - |
7.4872 | 17550 | 0.0017 | - |
7.5085 | 17600 | 0.0017 | - |
7.5299 | 17650 | 0.0016 | - |
7.5512 | 17700 | 0.0019 | - |
7.5725 | 17750 | 0.0017 | - |
7.5939 | 17800 | 0.0022 | - |
7.6152 | 17850 | 0.0013 | - |
7.6365 | 17900 | 0.0012 | - |
7.6578 | 17950 | 0.0019 | - |
7.6792 | 18000 | 0.0026 | - |
7.7005 | 18050 | 0.0021 | - |
7.7218 | 18100 | 0.0016 | - |
7.7432 | 18150 | 0.0014 | - |
7.7645 | 18200 | 0.0014 | - |
7.7858 | 18250 | 0.0011 | - |
7.8072 | 18300 | 0.0013 | - |
7.8285 | 18350 | 0.0019 | - |
7.8498 | 18400 | 0.0017 | - |
7.8712 | 18450 | 0.0017 | - |
7.8925 | 18500 | 0.0019 | - |
7.9138 | 18550 | 0.0017 | - |
7.9352 | 18600 | 0.0015 | - |
7.9565 | 18650 | 0.0008 | - |
7.9778 | 18700 | 0.0009 | - |
7.9991 | 18750 | 0.0015 | - |
8.0205 | 18800 | 0.0015 | - |
8.0418 | 18850 | 0.0011 | - |
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18.8993 | 44300 | 0.0 | - |
18.9206 | 44350 | 0.0001 | - |
18.9420 | 44400 | 0.0001 | - |
18.9633 | 44450 | 0.0 | - |
18.9846 | 44500 | 0.0001 | - |
19.0060 | 44550 | 0.0001 | - |
19.0273 | 44600 | 0.0 | - |
19.0486 | 44650 | 0.0 | - |
19.0700 | 44700 | 0.0001 | - |
19.0913 | 44750 | 0.0001 | - |
19.1126 | 44800 | 0.0001 | - |
19.1340 | 44850 | 0.0001 | - |
19.1553 | 44900 | 0.0 | - |
19.1766 | 44950 | 0.0001 | - |
19.1980 | 45000 | 0.0 | - |
19.2193 | 45050 | 0.0 | - |
19.2406 | 45100 | 0.0 | - |
19.2619 | 45150 | 0.0 | - |
19.2833 | 45200 | 0.0001 | - |
19.3046 | 45250 | 0.0 | - |
19.3259 | 45300 | 0.0001 | - |
19.3473 | 45350 | 0.0 | - |
19.3686 | 45400 | 0.0001 | - |
19.3899 | 45450 | 0.0001 | - |
19.4113 | 45500 | 0.0 | - |
19.4326 | 45550 | 0.0 | - |
19.4539 | 45600 | 0.0 | - |
19.4753 | 45650 | 0.0 | - |
19.4966 | 45700 | 0.0 | - |
19.5179 | 45750 | 0.0 | - |
19.5392 | 45800 | 0.0001 | - |
19.5606 | 45850 | 0.0001 | - |
19.5819 | 45900 | 0.0 | - |
19.6032 | 45950 | 0.0001 | - |
19.6246 | 46000 | 0.0001 | - |
19.6459 | 46050 | 0.0 | - |
19.6672 | 46100 | 0.0 | - |
19.6886 | 46150 | 0.0001 | - |
19.7099 | 46200 | 0.0 | - |
19.7312 | 46250 | 0.0 | - |
19.7526 | 46300 | 0.0 | - |
19.7739 | 46350 | 0.0001 | - |
19.7952 | 46400 | 0.0 | - |
19.8166 | 46450 | 0.0 | - |
19.8379 | 46500 | 0.0 | - |
19.8592 | 46550 | 0.0 | - |
19.8805 | 46600 | 0.0 | - |
19.9019 | 46650 | 0.0001 | - |
19.9232 | 46700 | 0.0 | - |
19.9445 | 46750 | 0.0 | - |
19.9659 | 46800 | 0.0001 | - |
19.9872 | 46850 | 0.0 | - |
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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