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 |
---|---|
1 |
|
58 |
|
53 |
|
95 |
|
45 |
|
52 |
|
55 |
|
86 |
|
29 |
|
82 |
|
28 |
|
40 |
|
37 |
|
67 |
|
59 |
|
93 |
|
74 |
|
64 |
|
7 |
|
22 |
|
79 |
|
91 |
|
33 |
|
99 |
|
47 |
|
75 |
|
69 |
|
44 |
|
90 |
|
98 |
|
42 |
|
65 |
|
32 |
|
88 |
|
39 |
|
16 |
|
76 |
|
4 |
|
50 |
|
66 |
|
18 |
|
70 |
|
30 |
|
57 |
|
36 |
|
38 |
|
15 |
|
63 |
|
87 |
|
68 |
|
23 |
|
85 |
|
25 |
|
48 |
|
84 |
|
71 |
|
51 |
|
92 |
|
77 |
|
5 |
|
80 |
|
13 |
|
54 |
|
12 |
|
89 |
|
46 |
|
10 |
|
94 |
|
8 |
|
49 |
|
60 |
|
2 |
|
27 |
|
43 |
|
62 |
|
78 |
|
56 |
|
31 |
|
97 |
|
19 |
|
26 |
|
0 |
|
96 |
|
61 |
|
17 |
|
81 |
|
83 |
|
34 |
|
3 |
|
24 |
|
9 |
|
14 |
|
11 |
|
21 |
|
41 |
|
73 |
|
20 |
|
6 |
|
72 |
|
35 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.85 |
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_tcate")
# Run inference
preds = model("[MAC] 러브 미 립스틱 트레 블라제 HMALL > 뷰티 > 메이크업 > 립메이크업")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 6 | 17.1348 | 40 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 41 |
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 | 0.4723 | - |
0.0214 | 50 | 0.4278 | - |
0.0427 | 100 | 0.4098 | - |
0.0641 | 150 | 0.3728 | - |
0.0855 | 200 | 0.3617 | - |
0.1068 | 250 | 0.3294 | - |
0.1282 | 300 | 0.2829 | - |
0.1496 | 350 | 0.2541 | - |
0.1709 | 400 | 0.2338 | - |
0.1923 | 450 | 0.204 | - |
0.2137 | 500 | 0.1824 | - |
0.2350 | 550 | 0.1606 | - |
0.2564 | 600 | 0.1309 | - |
0.2778 | 650 | 0.1195 | - |
0.2991 | 700 | 0.1065 | - |
0.3205 | 750 | 0.0977 | - |
0.3419 | 800 | 0.0946 | - |
0.3632 | 850 | 0.0843 | - |
0.3846 | 900 | 0.0851 | - |
0.4060 | 950 | 0.0772 | - |
0.4274 | 1000 | 0.0717 | - |
0.4487 | 1050 | 0.0695 | - |
0.4701 | 1100 | 0.0653 | - |
0.4915 | 1150 | 0.0585 | - |
0.5128 | 1200 | 0.0633 | - |
0.5342 | 1250 | 0.0569 | - |
0.5556 | 1300 | 0.0528 | - |
0.5769 | 1350 | 0.0518 | - |
0.5983 | 1400 | 0.0499 | - |
0.6197 | 1450 | 0.0428 | - |
0.6410 | 1500 | 0.045 | - |
0.6624 | 1550 | 0.0458 | - |
0.6838 | 1600 | 0.0415 | - |
0.7051 | 1650 | 0.0377 | - |
0.7265 | 1700 | 0.0392 | - |
0.7479 | 1750 | 0.0359 | - |
0.7692 | 1800 | 0.0371 | - |
0.7906 | 1850 | 0.0341 | - |
0.8120 | 1900 | 0.0331 | - |
0.8333 | 1950 | 0.0331 | - |
0.8547 | 2000 | 0.03 | - |
0.8761 | 2050 | 0.03 | - |
0.8974 | 2100 | 0.0321 | - |
0.9188 | 2150 | 0.0266 | - |
0.9402 | 2200 | 0.0281 | - |
0.9615 | 2250 | 0.0294 | - |
0.9829 | 2300 | 0.0277 | - |
1.0043 | 2350 | 0.0277 | - |
1.0256 | 2400 | 0.0267 | - |
1.0470 | 2450 | 0.0266 | - |
1.0684 | 2500 | 0.0256 | - |
1.0897 | 2550 | 0.0251 | - |
1.1111 | 2600 | 0.0233 | - |
1.1325 | 2650 | 0.0215 | - |
1.1538 | 2700 | 0.0219 | - |
1.1752 | 2750 | 0.0254 | - |
1.1966 | 2800 | 0.0221 | - |
1.2179 | 2850 | 0.0218 | - |
1.2393 | 2900 | 0.0221 | - |
1.2607 | 2950 | 0.0193 | - |
1.2821 | 3000 | 0.0207 | - |
1.3034 | 3050 | 0.0197 | - |
1.3248 | 3100 | 0.0183 | - |
1.3462 | 3150 | 0.0178 | - |
1.3675 | 3200 | 0.0192 | - |
1.3889 | 3250 | 0.0182 | - |
1.4103 | 3300 | 0.0174 | - |
1.4316 | 3350 | 0.0186 | - |
1.4530 | 3400 | 0.0187 | - |
1.4744 | 3450 | 0.0189 | - |
1.4957 | 3500 | 0.0176 | - |
1.5171 | 3550 | 0.0161 | - |
1.5385 | 3600 | 0.0164 | - |
1.5598 | 3650 | 0.0161 | - |
1.5812 | 3700 | 0.0161 | - |
1.6026 | 3750 | 0.0169 | - |
1.6239 | 3800 | 0.0143 | - |
1.6453 | 3850 | 0.0162 | - |
1.6667 | 3900 | 0.014 | - |
1.6880 | 3950 | 0.0156 | - |
1.7094 | 4000 | 0.0137 | - |
1.7308 | 4050 | 0.0129 | - |
1.7521 | 4100 | 0.0137 | - |
1.7735 | 4150 | 0.0138 | - |
1.7949 | 4200 | 0.0136 | - |
1.8162 | 4250 | 0.0131 | - |
1.8376 | 4300 | 0.0122 | - |
1.8590 | 4350 | 0.0137 | - |
1.8803 | 4400 | 0.0119 | - |
1.9017 | 4450 | 0.0107 | - |
1.9231 | 4500 | 0.0088 | - |
1.9444 | 4550 | 0.0106 | - |
1.9658 | 4600 | 0.0121 | - |
1.9872 | 4650 | 0.0103 | - |
2.0085 | 4700 | 0.0087 | - |
2.0299 | 4750 | 0.009 | - |
2.0513 | 4800 | 0.0097 | - |
2.0726 | 4850 | 0.0101 | - |
2.0940 | 4900 | 0.0102 | - |
2.1154 | 4950 | 0.0121 | - |
2.1368 | 5000 | 0.0085 | - |
2.1581 | 5050 | 0.0087 | - |
2.1795 | 5100 | 0.0095 | - |
2.2009 | 5150 | 0.008 | - |
2.2222 | 5200 | 0.0109 | - |
2.2436 | 5250 | 0.0111 | - |
2.2650 | 5300 | 0.009 | - |
2.2863 | 5350 | 0.0102 | - |
2.3077 | 5400 | 0.009 | - |
2.3291 | 5450 | 0.0077 | - |
2.3504 | 5500 | 0.01 | - |
2.3718 | 5550 | 0.0103 | - |
2.3932 | 5600 | 0.0072 | - |
2.4145 | 5650 | 0.0104 | - |
2.4359 | 5700 | 0.0076 | - |
2.4573 | 5750 | 0.0099 | - |
2.4786 | 5800 | 0.009 | - |
2.5 | 5850 | 0.0085 | - |
2.5214 | 5900 | 0.0097 | - |
2.5427 | 5950 | 0.0073 | - |
2.5641 | 6000 | 0.0084 | - |
2.5855 | 6050 | 0.0072 | - |
2.6068 | 6100 | 0.0085 | - |
2.6282 | 6150 | 0.0069 | - |
2.6496 | 6200 | 0.0091 | - |
2.6709 | 6250 | 0.0065 | - |
2.6923 | 6300 | 0.0064 | - |
2.7137 | 6350 | 0.0073 | - |
2.7350 | 6400 | 0.008 | - |
2.7564 | 6450 | 0.0091 | - |
2.7778 | 6500 | 0.008 | - |
2.7991 | 6550 | 0.0066 | - |
2.8205 | 6600 | 0.0071 | - |
2.8419 | 6650 | 0.0066 | - |
2.8632 | 6700 | 0.0082 | - |
2.8846 | 6750 | 0.0061 | - |
2.9060 | 6800 | 0.0055 | - |
2.9274 | 6850 | 0.0068 | - |
2.9487 | 6900 | 0.006 | - |
2.9701 | 6950 | 0.0063 | - |
2.9915 | 7000 | 0.0056 | - |
3.0128 | 7050 | 0.0078 | - |
3.0342 | 7100 | 0.0091 | - |
3.0556 | 7150 | 0.0061 | - |
3.0769 | 7200 | 0.0068 | - |
3.0983 | 7250 | 0.0073 | - |
3.1197 | 7300 | 0.0054 | - |
3.1410 | 7350 | 0.0061 | - |
3.1624 | 7400 | 0.0056 | - |
3.1838 | 7450 | 0.0062 | - |
3.2051 | 7500 | 0.0046 | - |
3.2265 | 7550 | 0.0052 | - |
3.2479 | 7600 | 0.0046 | - |
3.2692 | 7650 | 0.0043 | - |
3.2906 | 7700 | 0.0046 | - |
3.3120 | 7750 | 0.0053 | - |
3.3333 | 7800 | 0.0045 | - |
3.3547 | 7850 | 0.0037 | - |
3.3761 | 7900 | 0.0045 | - |
3.3974 | 7950 | 0.0056 | - |
3.4188 | 8000 | 0.005 | - |
3.4402 | 8050 | 0.0059 | - |
3.4615 | 8100 | 0.0042 | - |
3.4829 | 8150 | 0.0049 | - |
3.5043 | 8200 | 0.0041 | - |
3.5256 | 8250 | 0.0042 | - |
3.5470 | 8300 | 0.0041 | - |
3.5684 | 8350 | 0.0033 | - |
3.5897 | 8400 | 0.0041 | - |
3.6111 | 8450 | 0.0035 | - |
3.6325 | 8500 | 0.0041 | - |
3.6538 | 8550 | 0.0027 | - |
3.6752 | 8600 | 0.0033 | - |
3.6966 | 8650 | 0.0043 | - |
3.7179 | 8700 | 0.0038 | - |
3.7393 | 8750 | 0.0041 | - |
3.7607 | 8800 | 0.0034 | - |
3.7821 | 8850 | 0.0045 | - |
3.8034 | 8900 | 0.0041 | - |
3.8248 | 8950 | 0.0042 | - |
3.8462 | 9000 | 0.0034 | - |
3.8675 | 9050 | 0.004 | - |
3.8889 | 9100 | 0.0028 | - |
3.9103 | 9150 | 0.0037 | - |
3.9316 | 9200 | 0.0029 | - |
3.9530 | 9250 | 0.0033 | - |
3.9744 | 9300 | 0.0031 | - |
3.9957 | 9350 | 0.0032 | - |
4.0171 | 9400 | 0.0032 | - |
4.0385 | 9450 | 0.004 | - |
4.0598 | 9500 | 0.0026 | - |
4.0812 | 9550 | 0.0034 | - |
4.1026 | 9600 | 0.0033 | - |
4.1239 | 9650 | 0.0037 | - |
4.1453 | 9700 | 0.003 | - |
4.1667 | 9750 | 0.0042 | - |
4.1880 | 9800 | 0.0032 | - |
4.2094 | 9850 | 0.0035 | - |
4.2308 | 9900 | 0.0027 | - |
4.2521 | 9950 | 0.0031 | - |
4.2735 | 10000 | 0.0023 | - |
4.2949 | 10050 | 0.0031 | - |
4.3162 | 10100 | 0.0028 | - |
4.3376 | 10150 | 0.0027 | - |
4.3590 | 10200 | 0.0029 | - |
4.3803 | 10250 | 0.0023 | - |
4.4017 | 10300 | 0.0023 | - |
4.4231 | 10350 | 0.0026 | - |
4.4444 | 10400 | 0.0025 | - |
4.4658 | 10450 | 0.002 | - |
4.4872 | 10500 | 0.0019 | - |
4.5085 | 10550 | 0.0023 | - |
4.5299 | 10600 | 0.0026 | - |
4.5513 | 10650 | 0.0031 | - |
4.5726 | 10700 | 0.0023 | - |
4.5940 | 10750 | 0.0027 | - |
4.6154 | 10800 | 0.0021 | - |
4.6368 | 10850 | 0.0021 | - |
4.6581 | 10900 | 0.0029 | - |
4.6795 | 10950 | 0.003 | - |
4.7009 | 11000 | 0.0026 | - |
4.7222 | 11050 | 0.0025 | - |
4.7436 | 11100 | 0.002 | - |
4.7650 | 11150 | 0.0017 | - |
4.7863 | 11200 | 0.0023 | - |
4.8077 | 11250 | 0.0021 | - |
4.8291 | 11300 | 0.0033 | - |
4.8504 | 11350 | 0.0024 | - |
4.8718 | 11400 | 0.0016 | - |
4.8932 | 11450 | 0.0013 | - |
4.9145 | 11500 | 0.0017 | - |
4.9359 | 11550 | 0.0023 | - |
4.9573 | 11600 | 0.0014 | - |
4.9786 | 11650 | 0.0022 | - |
5.0 | 11700 | 0.0024 | - |
5.0214 | 11750 | 0.0011 | - |
5.0427 | 11800 | 0.0021 | - |
5.0641 | 11850 | 0.0017 | - |
5.0855 | 11900 | 0.0018 | - |
5.1068 | 11950 | 0.0019 | - |
5.1282 | 12000 | 0.0023 | - |
5.1496 | 12050 | 0.0024 | - |
5.1709 | 12100 | 0.0017 | - |
5.1923 | 12150 | 0.0029 | - |
5.2137 | 12200 | 0.0047 | - |
5.2350 | 12250 | 0.0024 | - |
5.2564 | 12300 | 0.0023 | - |
5.2778 | 12350 | 0.0015 | - |
5.2991 | 12400 | 0.0032 | - |
5.3205 | 12450 | 0.0018 | - |
5.3419 | 12500 | 0.0018 | - |
5.3632 | 12550 | 0.002 | - |
5.3846 | 12600 | 0.0021 | - |
5.4060 | 12650 | 0.0015 | - |
5.4274 | 12700 | 0.0014 | - |
5.4487 | 12750 | 0.002 | - |
5.4701 | 12800 | 0.0018 | - |
5.4915 | 12850 | 0.002 | - |
5.5128 | 12900 | 0.001 | - |
5.5342 | 12950 | 0.0019 | - |
5.5556 | 13000 | 0.0021 | - |
5.5769 | 13050 | 0.0018 | - |
5.5983 | 13100 | 0.0039 | - |
5.6197 | 13150 | 0.0046 | - |
5.6410 | 13200 | 0.0029 | - |
5.6624 | 13250 | 0.0015 | - |
5.6838 | 13300 | 0.0017 | - |
5.7051 | 13350 | 0.0021 | - |
5.7265 | 13400 | 0.0017 | - |
5.7479 | 13450 | 0.0026 | - |
5.7692 | 13500 | 0.0022 | - |
5.7906 | 13550 | 0.0024 | - |
5.8120 | 13600 | 0.0015 | - |
5.8333 | 13650 | 0.0023 | - |
5.8547 | 13700 | 0.0018 | - |
5.8761 | 13750 | 0.0026 | - |
5.8974 | 13800 | 0.0017 | - |
5.9188 | 13850 | 0.001 | - |
5.9402 | 13900 | 0.002 | - |
5.9615 | 13950 | 0.0012 | - |
5.9829 | 14000 | 0.0011 | - |
6.0043 | 14050 | 0.0024 | - |
6.0256 | 14100 | 0.0011 | - |
6.0470 | 14150 | 0.0019 | - |
6.0684 | 14200 | 0.0016 | - |
6.0897 | 14250 | 0.0017 | - |
6.1111 | 14300 | 0.0008 | - |
6.1325 | 14350 | 0.0017 | - |
6.1538 | 14400 | 0.0018 | - |
6.1752 | 14450 | 0.0011 | - |
6.1966 | 14500 | 0.0023 | - |
6.2179 | 14550 | 0.0014 | - |
6.2393 | 14600 | 0.0008 | - |
6.2607 | 14650 | 0.0014 | - |
6.2821 | 14700 | 0.0014 | - |
6.3034 | 14750 | 0.0015 | - |
6.3248 | 14800 | 0.0014 | - |
6.3462 | 14850 | 0.0008 | - |
6.3675 | 14900 | 0.0016 | - |
6.3889 | 14950 | 0.0009 | - |
6.4103 | 15000 | 0.0017 | - |
6.4316 | 15050 | 0.0008 | - |
6.4530 | 15100 | 0.002 | - |
6.4744 | 15150 | 0.0014 | - |
6.4957 | 15200 | 0.0012 | - |
6.5171 | 15250 | 0.0007 | - |
6.5385 | 15300 | 0.0013 | - |
6.5598 | 15350 | 0.003 | - |
6.5812 | 15400 | 0.0049 | - |
6.6026 | 15450 | 0.0024 | - |
6.6239 | 15500 | 0.0023 | - |
6.6453 | 15550 | 0.0019 | - |
6.6667 | 15600 | 0.0022 | - |
6.6880 | 15650 | 0.0018 | - |
6.7094 | 15700 | 0.0019 | - |
6.7308 | 15750 | 0.0014 | - |
6.7521 | 15800 | 0.001 | - |
6.7735 | 15850 | 0.0016 | - |
6.7949 | 15900 | 0.0016 | - |
6.8162 | 15950 | 0.0015 | - |
6.8376 | 16000 | 0.0012 | - |
6.8590 | 16050 | 0.0014 | - |
6.8803 | 16100 | 0.0014 | - |
6.9017 | 16150 | 0.0014 | - |
6.9231 | 16200 | 0.001 | - |
6.9444 | 16250 | 0.0013 | - |
6.9658 | 16300 | 0.0018 | - |
6.9872 | 16350 | 0.0005 | - |
7.0085 | 16400 | 0.0013 | - |
7.0299 | 16450 | 0.0019 | - |
7.0513 | 16500 | 0.0007 | - |
7.0726 | 16550 | 0.0009 | - |
7.0940 | 16600 | 0.0015 | - |
7.1154 | 16650 | 0.0016 | - |
7.1368 | 16700 | 0.001 | - |
7.1581 | 16750 | 0.0011 | - |
7.1795 | 16800 | 0.0015 | - |
7.2009 | 16850 | 0.0012 | - |
7.2222 | 16900 | 0.0015 | - |
7.2436 | 16950 | 0.0011 | - |
7.2650 | 17000 | 0.0013 | - |
7.2863 | 17050 | 0.002 | - |
7.3077 | 17100 | 0.0012 | - |
7.3291 | 17150 | 0.0023 | - |
7.3504 | 17200 | 0.0021 | - |
7.3718 | 17250 | 0.0013 | - |
7.3932 | 17300 | 0.0015 | - |
7.4145 | 17350 | 0.0013 | - |
7.4359 | 17400 | 0.0011 | - |
7.4573 | 17450 | 0.0014 | - |
7.4786 | 17500 | 0.0005 | - |
7.5 | 17550 | 0.0014 | - |
7.5214 | 17600 | 0.0006 | - |
7.5427 | 17650 | 0.0011 | - |
7.5641 | 17700 | 0.0014 | - |
7.5855 | 17750 | 0.0009 | - |
7.6068 | 17800 | 0.0012 | - |
7.6282 | 17850 | 0.0014 | - |
7.6496 | 17900 | 0.001 | - |
7.6709 | 17950 | 0.0012 | - |
7.6923 | 18000 | 0.0013 | - |
7.7137 | 18050 | 0.0013 | - |
7.7350 | 18100 | 0.0007 | - |
7.7564 | 18150 | 0.0009 | - |
7.7778 | 18200 | 0.0015 | - |
7.7991 | 18250 | 0.0006 | - |
7.8205 | 18300 | 0.0012 | - |
7.8419 | 18350 | 0.0007 | - |
7.8632 | 18400 | 0.0005 | - |
7.8846 | 18450 | 0.0007 | - |
7.9060 | 18500 | 0.0003 | - |
7.9274 | 18550 | 0.0007 | - |
7.9487 | 18600 | 0.0005 | - |
7.9701 | 18650 | 0.0015 | - |
7.9915 | 18700 | 0.001 | - |
8.0128 | 18750 | 0.0014 | - |
8.0342 | 18800 | 0.0009 | - |
8.0556 | 18850 | 0.0016 | - |
8.0769 | 18900 | 0.0024 | - |
8.0983 | 18950 | 0.0016 | - |
8.1197 | 19000 | 0.0009 | - |
8.1410 | 19050 | 0.001 | - |
8.1624 | 19100 | 0.0006 | - |
8.1838 | 19150 | 0.0008 | - |
8.2051 | 19200 | 0.0009 | - |
8.2265 | 19250 | 0.0005 | - |
8.2479 | 19300 | 0.0006 | - |
8.2692 | 19350 | 0.0006 | - |
8.2906 | 19400 | 0.0009 | - |
8.3120 | 19450 | 0.0007 | - |
8.3333 | 19500 | 0.001 | - |
8.3547 | 19550 | 0.0011 | - |
8.3761 | 19600 | 0.0004 | - |
8.3974 | 19650 | 0.0009 | - |
8.4188 | 19700 | 0.0009 | - |
8.4402 | 19750 | 0.001 | - |
8.4615 | 19800 | 0.0013 | - |
8.4829 | 19850 | 0.0013 | - |
8.5043 | 19900 | 0.0011 | - |
8.5256 | 19950 | 0.0007 | - |
8.5470 | 20000 | 0.0006 | - |
8.5684 | 20050 | 0.0006 | - |
8.5897 | 20100 | 0.0011 | - |
8.6111 | 20150 | 0.0013 | - |
8.6325 | 20200 | 0.0008 | - |
8.6538 | 20250 | 0.0006 | - |
8.6752 | 20300 | 0.0008 | - |
8.6966 | 20350 | 0.0008 | - |
8.7179 | 20400 | 0.0007 | - |
8.7393 | 20450 | 0.0007 | - |
8.7607 | 20500 | 0.001 | - |
8.7821 | 20550 | 0.0006 | - |
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8.8462 | 20700 | 0.0003 | - |
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8.8889 | 20800 | 0.001 | - |
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9.0385 | 21150 | 0.0007 | - |
9.0598 | 21200 | 0.0008 | - |
9.0812 | 21250 | 0.0008 | - |
9.1026 | 21300 | 0.0011 | - |
9.1239 | 21350 | 0.0017 | - |
9.1453 | 21400 | 0.0014 | - |
9.1667 | 21450 | 0.0008 | - |
9.1880 | 21500 | 0.0011 | - |
9.2094 | 21550 | 0.0003 | - |
9.2308 | 21600 | 0.0003 | - |
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9.3376 | 21850 | 0.0005 | - |
9.3590 | 21900 | 0.0007 | - |
9.3803 | 21950 | 0.0003 | - |
9.4017 | 22000 | 0.0005 | - |
9.4231 | 22050 | 0.0009 | - |
9.4444 | 22100 | 0.0003 | - |
9.4658 | 22150 | 0.0008 | - |
9.4872 | 22200 | 0.0006 | - |
9.5085 | 22250 | 0.0005 | - |
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9.8718 | 23100 | 0.0002 | - |
9.8932 | 23150 | 0.0002 | - |
9.9145 | 23200 | 0.0014 | - |
9.9359 | 23250 | 0.002 | - |
9.9573 | 23300 | 0.0013 | - |
9.9786 | 23350 | 0.0006 | - |
10.0 | 23400 | 0.0011 | - |
10.0214 | 23450 | 0.0018 | - |
10.0427 | 23500 | 0.0013 | - |
10.0641 | 23550 | 0.0006 | - |
10.0855 | 23600 | 0.0011 | - |
10.1068 | 23650 | 0.0004 | - |
10.1282 | 23700 | 0.0006 | - |
10.1496 | 23750 | 0.0007 | - |
10.1709 | 23800 | 0.0008 | - |
10.1923 | 23850 | 0.0009 | - |
10.2137 | 23900 | 0.0006 | - |
10.2350 | 23950 | 0.0008 | - |
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10.2991 | 24100 | 0.0005 | - |
10.3205 | 24150 | 0.0004 | - |
10.3419 | 24200 | 0.0009 | - |
10.3632 | 24250 | 0.0006 | - |
10.3846 | 24300 | 0.0009 | - |
10.4060 | 24350 | 0.0002 | - |
10.4274 | 24400 | 0.0001 | - |
10.4487 | 24450 | 0.0003 | - |
10.4701 | 24500 | 0.0004 | - |
10.4915 | 24550 | 0.0002 | - |
10.5128 | 24600 | 0.0005 | - |
10.5342 | 24650 | 0.0005 | - |
10.5556 | 24700 | 0.0003 | - |
10.5769 | 24750 | 0.0002 | - |
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10.6197 | 24850 | 0.0002 | - |
10.6410 | 24900 | 0.0003 | - |
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10.6838 | 25000 | 0.0003 | - |
10.7051 | 25050 | 0.0007 | - |
10.7265 | 25100 | 0.0004 | - |
10.7479 | 25150 | 0.0007 | - |
10.7692 | 25200 | 0.0004 | - |
10.7906 | 25250 | 0.0005 | - |
10.8120 | 25300 | 0.0003 | - |
10.8333 | 25350 | 0.0005 | - |
10.8547 | 25400 | 0.0005 | - |
10.8761 | 25450 | 0.0002 | - |
10.8974 | 25500 | 0.0004 | - |
10.9188 | 25550 | 0.0009 | - |
10.9402 | 25600 | 0.0003 | - |
10.9615 | 25650 | 0.0003 | - |
10.9829 | 25700 | 0.0004 | - |
11.0043 | 25750 | 0.0001 | - |
11.0256 | 25800 | 0.0004 | - |
11.0470 | 25850 | 0.0007 | - |
11.0684 | 25900 | 0.0005 | - |
11.0897 | 25950 | 0.0006 | - |
11.1111 | 26000 | 0.0003 | - |
11.1325 | 26050 | 0.0007 | - |
11.1538 | 26100 | 0.0013 | - |
11.1752 | 26150 | 0.001 | - |
11.1966 | 26200 | 0.0006 | - |
11.2179 | 26250 | 0.0007 | - |
11.2393 | 26300 | 0.0013 | - |
11.2607 | 26350 | 0.0019 | - |
11.2821 | 26400 | 0.0006 | - |
11.3034 | 26450 | 0.0008 | - |
11.3248 | 26500 | 0.0013 | - |
11.3462 | 26550 | 0.0011 | - |
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11.3889 | 26650 | 0.0006 | - |
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11.6026 | 27150 | 0.0005 | - |
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11.6667 | 27300 | 0.0004 | - |
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11.7735 | 27550 | 0.0007 | - |
11.7949 | 27600 | 0.0011 | - |
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11.8590 | 27750 | 0.0005 | - |
11.8803 | 27800 | 0.0003 | - |
11.9017 | 27850 | 0.0008 | - |
11.9231 | 27900 | 0.0002 | - |
11.9444 | 27950 | 0.0002 | - |
11.9658 | 28000 | 0.0003 | - |
11.9872 | 28050 | 0.0001 | - |
12.0085 | 28100 | 0.0001 | - |
12.0299 | 28150 | 0.0002 | - |
12.0513 | 28200 | 0.0004 | - |
12.0726 | 28250 | 0.0002 | - |
12.0940 | 28300 | 0.0001 | - |
12.1154 | 28350 | 0.0001 | - |
12.1368 | 28400 | 0.0003 | - |
12.1581 | 28450 | 0.0004 | - |
12.1795 | 28500 | 0.0004 | - |
12.2009 | 28550 | 0.0004 | - |
12.2222 | 28600 | 0.0003 | - |
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12.3932 | 29000 | 0.0006 | - |
12.4145 | 29050 | 0.0001 | - |
12.4359 | 29100 | 0.0007 | - |
12.4573 | 29150 | 0.0001 | - |
12.4786 | 29200 | 0.0001 | - |
12.5 | 29250 | 0.0001 | - |
12.5214 | 29300 | 0.0001 | - |
12.5427 | 29350 | 0.0001 | - |
12.5641 | 29400 | 0.0001 | - |
12.5855 | 29450 | 0.0002 | - |
12.6068 | 29500 | 0.0004 | - |
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12.6709 | 29650 | 0.0002 | - |
12.6923 | 29700 | 0.0002 | - |
12.7137 | 29750 | 0.0003 | - |
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12.7564 | 29850 | 0.0001 | - |
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12.9060 | 30200 | 0.0004 | - |
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12.9701 | 30350 | 0.0001 | - |
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13.0128 | 30450 | 0.0001 | - |
13.0342 | 30500 | 0.0002 | - |
13.0556 | 30550 | 0.0002 | - |
13.0769 | 30600 | 0.0005 | - |
13.0983 | 30650 | 0.0005 | - |
13.1197 | 30700 | 0.0001 | - |
13.1410 | 30750 | 0.0001 | - |
13.1624 | 30800 | 0.0001 | - |
13.1838 | 30850 | 0.0001 | - |
13.2051 | 30900 | 0.0002 | - |
13.2265 | 30950 | 0.0002 | - |
13.2479 | 31000 | 0.0006 | - |
13.2692 | 31050 | 0.0002 | - |
13.2906 | 31100 | 0.0004 | - |
13.3120 | 31150 | 0.0001 | - |
13.3333 | 31200 | 0.0001 | - |
13.3547 | 31250 | 0.0002 | - |
13.3761 | 31300 | 0.0002 | - |
13.3974 | 31350 | 0.0001 | - |
13.4188 | 31400 | 0.0001 | - |
13.4402 | 31450 | 0.0003 | - |
13.4615 | 31500 | 0.0004 | - |
13.4829 | 31550 | 0.0003 | - |
13.5043 | 31600 | 0.0003 | - |
13.5256 | 31650 | 0.0001 | - |
13.5470 | 31700 | 0.0001 | - |
13.5684 | 31750 | 0.0003 | - |
13.5897 | 31800 | 0.0001 | - |
13.6111 | 31850 | 0.0001 | - |
13.6325 | 31900 | 0.0001 | - |
13.6538 | 31950 | 0.0001 | - |
13.6752 | 32000 | 0.0001 | - |
13.6966 | 32050 | 0.0 | - |
13.7179 | 32100 | 0.0002 | - |
13.7393 | 32150 | 0.0004 | - |
13.7607 | 32200 | 0.0002 | - |
13.7821 | 32250 | 0.0002 | - |
13.8034 | 32300 | 0.0001 | - |
13.8248 | 32350 | 0.0001 | - |
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13.8889 | 32500 | 0.0001 | - |
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13.9316 | 32600 | 0.0001 | - |
13.9530 | 32650 | 0.0002 | - |
13.9744 | 32700 | 0.0001 | - |
13.9957 | 32750 | 0.0001 | - |
14.0171 | 32800 | 0.0002 | - |
14.0385 | 32850 | 0.0003 | - |
14.0598 | 32900 | 0.0001 | - |
14.0812 | 32950 | 0.0001 | - |
14.1026 | 33000 | 0.0001 | - |
14.1239 | 33050 | 0.0001 | - |
14.1453 | 33100 | 0.0 | - |
14.1667 | 33150 | 0.0005 | - |
14.1880 | 33200 | 0.0001 | - |
14.2094 | 33250 | 0.0001 | - |
14.2308 | 33300 | 0.0001 | - |
14.2521 | 33350 | 0.0001 | - |
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14.2949 | 33450 | 0.0 | - |
14.3162 | 33500 | 0.0003 | - |
14.3376 | 33550 | 0.0003 | - |
14.3590 | 33600 | 0.0001 | - |
14.3803 | 33650 | 0.0 | - |
14.4017 | 33700 | 0.0 | - |
14.4231 | 33750 | 0.0 | - |
14.4444 | 33800 | 0.0002 | - |
14.4658 | 33850 | 0.0001 | - |
14.4872 | 33900 | 0.0 | - |
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14.5299 | 34000 | 0.0002 | - |
14.5513 | 34050 | 0.0 | - |
14.5726 | 34100 | 0.0003 | - |
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14.9359 | 34950 | 0.0002 | - |
14.9573 | 35000 | 0.0001 | - |
14.9786 | 35050 | 0.0002 | - |
15.0 | 35100 | 0.0002 | - |
15.0214 | 35150 | 0.0002 | - |
15.0427 | 35200 | 0.0001 | - |
15.0641 | 35250 | 0.0001 | - |
15.0855 | 35300 | 0.0003 | - |
15.1068 | 35350 | 0.0003 | - |
15.1282 | 35400 | 0.0001 | - |
15.1496 | 35450 | 0.0002 | - |
15.1709 | 35500 | 0.0001 | - |
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15.2137 | 35600 | 0.0004 | - |
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15.2991 | 35800 | 0.0001 | - |
15.3205 | 35850 | 0.0001 | - |
15.3419 | 35900 | 0.0002 | - |
15.3632 | 35950 | 0.0001 | - |
15.3846 | 36000 | 0.0 | - |
15.4060 | 36050 | 0.0 | - |
15.4274 | 36100 | 0.0 | - |
15.4487 | 36150 | 0.0001 | - |
15.4701 | 36200 | 0.0004 | - |
15.4915 | 36250 | 0.0001 | - |
15.5128 | 36300 | 0.0002 | - |
15.5342 | 36350 | 0.0002 | - |
15.5556 | 36400 | 0.0001 | - |
15.5769 | 36450 | 0.0001 | - |
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15.6624 | 36650 | 0.0001 | - |
15.6838 | 36700 | 0.0 | - |
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15.7265 | 36800 | 0.0001 | - |
15.7479 | 36850 | 0.0 | - |
15.7692 | 36900 | 0.0001 | - |
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15.8120 | 37000 | 0.0 | - |
15.8333 | 37050 | 0.0002 | - |
15.8547 | 37100 | 0.0002 | - |
15.8761 | 37150 | 0.0001 | - |
15.8974 | 37200 | 0.0001 | - |
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15.9402 | 37300 | 0.0001 | - |
15.9615 | 37350 | 0.0001 | - |
15.9829 | 37400 | 0.0001 | - |
16.0043 | 37450 | 0.0001 | - |
16.0256 | 37500 | 0.0001 | - |
16.0470 | 37550 | 0.0 | - |
16.0684 | 37600 | 0.0 | - |
16.0897 | 37650 | 0.0001 | - |
16.1111 | 37700 | 0.0001 | - |
16.1325 | 37750 | 0.0001 | - |
16.1538 | 37800 | 0.0001 | - |
16.1752 | 37850 | 0.0001 | - |
16.1966 | 37900 | 0.0002 | - |
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16.2393 | 38000 | 0.0001 | - |
16.2607 | 38050 | 0.0001 | - |
16.2821 | 38100 | 0.0001 | - |
16.3034 | 38150 | 0.0001 | - |
16.3248 | 38200 | 0.0001 | - |
16.3462 | 38250 | 0.0001 | - |
16.3675 | 38300 | 0.0 | - |
16.3889 | 38350 | 0.0001 | - |
16.4103 | 38400 | 0.0001 | - |
16.4316 | 38450 | 0.0 | - |
16.4530 | 38500 | 0.0 | - |
16.4744 | 38550 | 0.0002 | - |
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16.6880 | 39050 | 0.0 | - |
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16.7735 | 39250 | 0.0 | - |
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16.8590 | 39450 | 0.0 | - |
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17.0085 | 39800 | 0.0 | - |
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17.0940 | 40000 | 0.0001 | - |
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17.1368 | 40100 | 0.0 | - |
17.1581 | 40150 | 0.0 | - |
17.1795 | 40200 | 0.0 | - |
17.2009 | 40250 | 0.0 | - |
17.2222 | 40300 | 0.0001 | - |
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17.4359 | 40800 | 0.0001 | - |
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17.5 | 40950 | 0.0 | - |
17.5214 | 41000 | 0.0 | - |
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17.5641 | 41100 | 0.0001 | - |
17.5855 | 41150 | 0.0001 | - |
17.6068 | 41200 | 0.0001 | - |
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17.7350 | 41500 | 0.0001 | - |
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17.9274 | 41950 | 0.0 | - |
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18.3333 | 42900 | 0.0001 | - |
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18.9744 | 44400 | 0.0 | - |
18.9957 | 44450 | 0.0 | - |
19.0171 | 44500 | 0.0 | - |
19.0385 | 44550 | 0.0 | - |
19.0598 | 44600 | 0.0001 | - |
19.0812 | 44650 | 0.0 | - |
19.1026 | 44700 | 0.0 | - |
19.1239 | 44750 | 0.0 | - |
19.1453 | 44800 | 0.0001 | - |
19.1667 | 44850 | 0.0001 | - |
19.1880 | 44900 | 0.0 | - |
19.2094 | 44950 | 0.0 | - |
19.2308 | 45000 | 0.0 | - |
19.2521 | 45050 | 0.0001 | - |
19.2735 | 45100 | 0.0 | - |
19.2949 | 45150 | 0.0 | - |
19.3162 | 45200 | 0.0001 | - |
19.3376 | 45250 | 0.0001 | - |
19.3590 | 45300 | 0.0 | - |
19.3803 | 45350 | 0.0 | - |
19.4017 | 45400 | 0.0 | - |
19.4231 | 45450 | 0.0 | - |
19.4444 | 45500 | 0.0001 | - |
19.4658 | 45550 | 0.0001 | - |
19.4872 | 45600 | 0.0 | - |
19.5085 | 45650 | 0.0 | - |
19.5299 | 45700 | 0.0001 | - |
19.5513 | 45750 | 0.0 | - |
19.5726 | 45800 | 0.0 | - |
19.5940 | 45850 | 0.0 | - |
19.6154 | 45900 | 0.0001 | - |
19.6368 | 45950 | 0.0 | - |
19.6581 | 46000 | 0.0 | - |
19.6795 | 46050 | 0.0001 | - |
19.7009 | 46100 | 0.0001 | - |
19.7222 | 46150 | 0.0001 | - |
19.7436 | 46200 | 0.0 | - |
19.7650 | 46250 | 0.0 | - |
19.7863 | 46300 | 0.0 | - |
19.8077 | 46350 | 0.0 | - |
19.8291 | 46400 | 0.0 | - |
19.8504 | 46450 | 0.0 | - |
19.8718 | 46500 | 0.0 | - |
19.8932 | 46550 | 0.0001 | - |
19.9145 | 46600 | 0.0 | - |
19.9359 | 46650 | 0.0001 | - |
19.9573 | 46700 | 0.0 | - |
19.9786 | 46750 | 0.0001 | - |
20.0 | 46800 | 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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