SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 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: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 150 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 |
---|---|
34 |
|
12 |
|
36 |
|
55 |
|
32 |
|
20 |
|
77 |
|
2 |
|
93 |
|
19 |
|
146 |
|
75 |
|
119 |
|
136 |
|
18 |
|
98 |
|
90 |
|
33 |
|
105 |
|
121 |
|
14 |
|
6 |
|
13 |
|
130 |
|
80 |
|
26 |
|
5 |
|
65 |
|
111 |
|
95 |
|
104 |
|
30 |
|
73 |
|
22 |
|
150 |
|
60 |
|
4 |
|
41 |
|
133 |
|
120 |
|
40 |
|
125 |
|
79 |
|
116 |
|
149 |
|
88 |
|
140 |
|
84 |
|
101 |
|
50 |
|
139 |
|
110 |
|
118 |
|
45 |
|
69 |
|
63 |
|
138 |
|
106 |
|
128 |
|
123 |
|
44 |
|
102 |
|
132 |
|
141 |
|
74 |
|
10 |
|
83 |
|
92 |
|
87 |
|
82 |
|
29 |
|
56 |
|
8 |
|
78 |
|
64 |
|
91 |
|
134 |
|
54 |
|
39 |
|
1 |
|
43 |
|
16 |
|
31 |
|
148 |
|
47 |
|
99 |
|
53 |
|
58 |
|
131 |
|
85 |
|
46 |
|
17 |
|
48 |
|
117 |
|
37 |
|
68 |
|
113 |
|
109 |
|
103 |
|
51 |
|
76 |
|
145 |
|
38 |
|
71 |
|
61 |
|
27 |
|
62 |
|
3 |
|
126 |
|
144 |
|
129 |
|
23 |
|
28 |
|
107 |
|
94 |
|
35 |
|
112 |
|
49 |
|
72 |
|
21 |
|
66 |
|
86 |
|
122 |
|
52 |
|
42 |
|
143 |
|
11 |
|
142 |
|
7 |
|
24 |
|
147 |
|
97 |
|
25 |
|
9 |
|
67 |
|
89 |
|
135 |
|
59 |
|
115 |
|
57 |
|
124 |
|
96 |
|
100 |
|
137 |
|
108 |
|
127 |
|
81 |
|
114 |
|
15 |
|
70 |
|
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 ๐ค Hub
model = SetFitModel.from_pretrained("huiyeong/setfit-clinc150-neg")
# Run inference
preds = model("do americans need visas in canada")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 8.3237 | 28 |
Label | Training Sample Count |
---|---|
1 | 124 |
2 | 121 |
3 | 124 |
4 | 121 |
5 | 114 |
6 | 119 |
7 | 124 |
8 | 117 |
9 | 123 |
10 | 125 |
11 | 123 |
12 | 115 |
13 | 125 |
14 | 118 |
15 | 117 |
16 | 122 |
17 | 119 |
18 | 119 |
19 | 121 |
20 | 117 |
21 | 123 |
22 | 121 |
23 | 119 |
24 | 122 |
25 | 118 |
26 | 116 |
27 | 126 |
28 | 118 |
29 | 113 |
30 | 118 |
31 | 120 |
32 | 122 |
33 | 130 |
34 | 121 |
35 | 122 |
36 | 121 |
37 | 125 |
38 | 115 |
39 | 118 |
40 | 127 |
41 | 127 |
42 | 115 |
43 | 120 |
44 | 116 |
45 | 124 |
46 | 119 |
47 | 128 |
48 | 117 |
49 | 119 |
50 | 115 |
51 | 127 |
52 | 113 |
53 | 118 |
54 | 115 |
55 | 119 |
56 | 120 |
57 | 124 |
58 | 112 |
59 | 123 |
60 | 123 |
61 | 121 |
62 | 115 |
63 | 111 |
64 | 118 |
65 | 123 |
66 | 121 |
67 | 122 |
68 | 114 |
69 | 121 |
70 | 116 |
71 | 116 |
72 | 118 |
73 | 128 |
74 | 126 |
75 | 116 |
76 | 120 |
77 | 124 |
78 | 119 |
79 | 117 |
80 | 126 |
81 | 124 |
82 | 123 |
83 | 125 |
84 | 124 |
85 | 120 |
86 | 127 |
87 | 125 |
88 | 121 |
89 | 117 |
90 | 121 |
91 | 121 |
92 | 129 |
93 | 121 |
94 | 130 |
95 | 118 |
96 | 122 |
97 | 122 |
98 | 117 |
99 | 120 |
100 | 120 |
101 | 121 |
102 | 121 |
103 | 121 |
104 | 114 |
105 | 122 |
106 | 123 |
107 | 116 |
108 | 113 |
109 | 117 |
110 | 120 |
111 | 121 |
112 | 114 |
113 | 119 |
114 | 120 |
115 | 117 |
116 | 117 |
117 | 121 |
118 | 122 |
119 | 116 |
120 | 129 |
121 | 116 |
122 | 118 |
123 | 119 |
124 | 119 |
125 | 116 |
126 | 119 |
127 | 115 |
128 | 119 |
129 | 114 |
130 | 115 |
131 | 119 |
132 | 121 |
133 | 118 |
134 | 117 |
135 | 118 |
136 | 114 |
137 | 116 |
138 | 117 |
139 | 117 |
140 | 113 |
141 | 119 |
142 | 118 |
143 | 119 |
144 | 116 |
145 | 118 |
146 | 119 |
147 | 113 |
148 | 123 |
149 | 121 |
150 | 126 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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 |
---|---|---|---|
0.0001 | 1 | 0.1815 | - |
0.0045 | 50 | 0.1421 | - |
0.0089 | 100 | 0.1417 | - |
0.0134 | 150 | 0.1453 | - |
0.0178 | 200 | 0.132 | - |
0.0223 | 250 | 0.1309 | - |
0.0267 | 300 | 0.1296 | - |
0.0312 | 350 | 0.1166 | - |
0.0356 | 400 | 0.1265 | - |
0.0401 | 450 | 0.116 | - |
0.0445 | 500 | 0.1063 | - |
0.0490 | 550 | 0.1039 | - |
0.0534 | 600 | 0.0933 | - |
0.0579 | 650 | 0.1033 | - |
0.0623 | 700 | 0.1099 | - |
0.0668 | 750 | 0.1013 | - |
0.0712 | 800 | 0.0943 | - |
0.0757 | 850 | 0.0863 | - |
0.0801 | 900 | 0.0852 | - |
0.0846 | 950 | 0.0883 | - |
0.0890 | 1000 | 0.0946 | - |
0.0935 | 1050 | 0.0793 | - |
0.0979 | 1100 | 0.0871 | - |
0.1024 | 1150 | 0.0851 | - |
0.1068 | 1200 | 0.0756 | - |
0.1113 | 1250 | 0.0719 | - |
0.1157 | 1300 | 0.078 | - |
0.1202 | 1350 | 0.0728 | - |
0.1246 | 1400 | 0.0644 | - |
0.1291 | 1450 | 0.0755 | - |
0.1335 | 1500 | 0.0686 | - |
0.1380 | 1550 | 0.0701 | - |
0.1424 | 1600 | 0.0736 | - |
0.1469 | 1650 | 0.0674 | - |
0.1513 | 1700 | 0.0645 | - |
0.1558 | 1750 | 0.0589 | - |
0.1602 | 1800 | 0.0655 | - |
0.1647 | 1850 | 0.0623 | - |
0.1691 | 1900 | 0.0659 | - |
0.1736 | 1950 | 0.0607 | - |
0.1780 | 2000 | 0.0565 | - |
0.1825 | 2050 | 0.0548 | - |
0.1869 | 2100 | 0.063 | - |
0.1914 | 2150 | 0.0554 | - |
0.1959 | 2200 | 0.0549 | - |
0.2003 | 2250 | 0.0533 | - |
0.2048 | 2300 | 0.0511 | - |
0.2092 | 2350 | 0.0573 | - |
0.2137 | 2400 | 0.0496 | - |
0.2181 | 2450 | 0.0585 | - |
0.2226 | 2500 | 0.0526 | - |
0.2270 | 2550 | 0.0534 | - |
0.2315 | 2600 | 0.0472 | - |
0.2359 | 2650 | 0.0476 | - |
0.2404 | 2700 | 0.0472 | - |
0.2448 | 2750 | 0.0524 | - |
0.2493 | 2800 | 0.0371 | - |
0.2537 | 2850 | 0.0456 | - |
0.2582 | 2900 | 0.0454 | - |
0.2626 | 2950 | 0.0438 | - |
0.2671 | 3000 | 0.0428 | - |
0.2715 | 3050 | 0.0411 | - |
0.2760 | 3100 | 0.0439 | - |
0.2804 | 3150 | 0.0465 | - |
0.2849 | 3200 | 0.0447 | - |
0.2893 | 3250 | 0.039 | - |
0.2938 | 3300 | 0.0407 | - |
0.2982 | 3350 | 0.0441 | - |
0.3027 | 3400 | 0.0329 | - |
0.3071 | 3450 | 0.0426 | - |
0.3116 | 3500 | 0.0398 | - |
0.3160 | 3550 | 0.0388 | - |
0.3205 | 3600 | 0.0435 | - |
0.3249 | 3650 | 0.0372 | - |
0.3294 | 3700 | 0.0364 | - |
0.3338 | 3750 | 0.0361 | - |
0.3383 | 3800 | 0.0355 | - |
0.3427 | 3850 | 0.0438 | - |
0.3472 | 3900 | 0.0322 | - |
0.3516 | 3950 | 0.0297 | - |
0.3561 | 4000 | 0.0425 | - |
0.3605 | 4050 | 0.0333 | - |
0.3650 | 4100 | 0.0288 | - |
0.3694 | 4150 | 0.0331 | - |
0.3739 | 4200 | 0.0321 | - |
0.3783 | 4250 | 0.0327 | - |
0.3828 | 4300 | 0.0354 | - |
0.3873 | 4350 | 0.0329 | - |
0.3917 | 4400 | 0.0274 | - |
0.3962 | 4450 | 0.0322 | - |
0.4006 | 4500 | 0.0348 | - |
0.4051 | 4550 | 0.0324 | - |
0.4095 | 4600 | 0.0358 | - |
0.4140 | 4650 | 0.0279 | - |
0.4184 | 4700 | 0.0312 | - |
0.4229 | 4750 | 0.0275 | - |
0.4273 | 4800 | 0.029 | - |
0.4318 | 4850 | 0.027 | - |
0.4362 | 4900 | 0.0246 | - |
0.4407 | 4950 | 0.0315 | - |
0.4451 | 5000 | 0.0226 | - |
0.4496 | 5050 | 0.0264 | - |
0.4540 | 5100 | 0.0315 | - |
0.4585 | 5150 | 0.0305 | - |
0.4629 | 5200 | 0.0258 | - |
0.4674 | 5250 | 0.03 | - |
0.4718 | 5300 | 0.0273 | - |
0.4763 | 5350 | 0.0272 | - |
0.4807 | 5400 | 0.0259 | - |
0.4852 | 5450 | 0.0207 | - |
0.4896 | 5500 | 0.0264 | - |
0.4941 | 5550 | 0.0295 | - |
0.4985 | 5600 | 0.0233 | - |
0.5030 | 5650 | 0.0242 | - |
0.5074 | 5700 | 0.0216 | - |
0.5119 | 5750 | 0.023 | - |
0.5163 | 5800 | 0.022 | - |
0.5208 | 5850 | 0.0225 | - |
0.5252 | 5900 | 0.0257 | - |
0.5297 | 5950 | 0.0231 | - |
0.5341 | 6000 | 0.0241 | - |
0.5386 | 6050 | 0.0219 | - |
0.5430 | 6100 | 0.0269 | - |
0.5475 | 6150 | 0.0258 | - |
0.5519 | 6200 | 0.0229 | - |
0.5564 | 6250 | 0.0206 | - |
0.5608 | 6300 | 0.0252 | - |
0.5653 | 6350 | 0.0212 | - |
0.5697 | 6400 | 0.0228 | - |
0.5742 | 6450 | 0.0224 | - |
0.5787 | 6500 | 0.0197 | - |
0.5831 | 6550 | 0.0162 | - |
0.5876 | 6600 | 0.0221 | - |
0.5920 | 6650 | 0.0213 | - |
0.5965 | 6700 | 0.0234 | - |
0.6009 | 6750 | 0.0227 | - |
0.6054 | 6800 | 0.0184 | - |
0.6098 | 6850 | 0.0241 | - |
0.6143 | 6900 | 0.0252 | - |
0.6187 | 6950 | 0.0194 | - |
0.6232 | 7000 | 0.0196 | - |
0.6276 | 7050 | 0.0179 | - |
0.6321 | 7100 | 0.0206 | - |
0.6365 | 7150 | 0.0196 | - |
0.6410 | 7200 | 0.0184 | - |
0.6454 | 7250 | 0.0165 | - |
0.6499 | 7300 | 0.0196 | - |
0.6543 | 7350 | 0.0264 | - |
0.6588 | 7400 | 0.0243 | - |
0.6632 | 7450 | 0.0168 | - |
0.6677 | 7500 | 0.0216 | - |
0.6721 | 7550 | 0.0169 | - |
0.6766 | 7600 | 0.0163 | - |
0.6810 | 7650 | 0.015 | - |
0.6855 | 7700 | 0.0191 | - |
0.6899 | 7750 | 0.0142 | - |
0.6944 | 7800 | 0.0164 | - |
0.6988 | 7850 | 0.0171 | - |
0.7033 | 7900 | 0.0147 | - |
0.7077 | 7950 | 0.0172 | - |
0.7122 | 8000 | 0.018 | - |
0.7166 | 8050 | 0.0215 | - |
0.7211 | 8100 | 0.0188 | - |
0.7255 | 8150 | 0.0134 | - |
0.7300 | 8200 | 0.0146 | - |
0.7344 | 8250 | 0.0131 | - |
0.7389 | 8300 | 0.0174 | - |
0.7433 | 8350 | 0.0156 | - |
0.7478 | 8400 | 0.0139 | - |
0.7522 | 8450 | 0.0158 | - |
0.7567 | 8500 | 0.0192 | - |
0.7612 | 8550 | 0.0117 | - |
0.7656 | 8600 | 0.0145 | - |
0.7701 | 8650 | 0.0149 | - |
0.7745 | 8700 | 0.0157 | - |
0.7790 | 8750 | 0.0175 | - |
0.7834 | 8800 | 0.0159 | - |
0.7879 | 8850 | 0.0139 | - |
0.7923 | 8900 | 0.01 | - |
0.7968 | 8950 | 0.0135 | - |
0.8012 | 9000 | 0.0144 | - |
0.8057 | 9050 | 0.0131 | - |
0.8101 | 9100 | 0.013 | - |
0.8146 | 9150 | 0.0147 | - |
0.8190 | 9200 | 0.0124 | - |
0.8235 | 9250 | 0.0141 | - |
0.8279 | 9300 | 0.0105 | - |
0.8324 | 9350 | 0.0128 | - |
0.8368 | 9400 | 0.012 | - |
0.8413 | 9450 | 0.0115 | - |
0.8457 | 9500 | 0.0127 | - |
0.8502 | 9550 | 0.0165 | - |
0.8546 | 9600 | 0.0149 | - |
0.8591 | 9650 | 0.0102 | - |
0.8635 | 9700 | 0.0164 | - |
0.8680 | 9750 | 0.0135 | - |
0.8724 | 9800 | 0.0168 | - |
0.8769 | 9850 | 0.0083 | - |
0.8813 | 9900 | 0.0099 | - |
0.8858 | 9950 | 0.0135 | - |
0.8902 | 10000 | 0.0149 | - |
0.8947 | 10050 | 0.012 | - |
0.8991 | 10100 | 0.0149 | - |
0.9036 | 10150 | 0.012 | - |
0.9080 | 10200 | 0.0152 | - |
0.9125 | 10250 | 0.008 | - |
0.9169 | 10300 | 0.014 | - |
0.9214 | 10350 | 0.0121 | - |
0.9258 | 10400 | 0.0129 | - |
0.9303 | 10450 | 0.0111 | - |
0.9347 | 10500 | 0.0127 | - |
0.9392 | 10550 | 0.0117 | - |
0.9436 | 10600 | 0.0093 | - |
0.9481 | 10650 | 0.0171 | - |
0.9526 | 10700 | 0.0152 | - |
0.9570 | 10750 | 0.0158 | - |
0.9615 | 10800 | 0.0122 | - |
0.9659 | 10850 | 0.0087 | - |
0.9704 | 10900 | 0.01 | - |
0.9748 | 10950 | 0.0113 | - |
0.9793 | 11000 | 0.0101 | - |
0.9837 | 11050 | 0.0128 | - |
0.9882 | 11100 | 0.0173 | - |
0.9926 | 11150 | 0.0126 | - |
0.9971 | 11200 | 0.0124 | - |
1.0015 | 11250 | 0.0101 | - |
1.0060 | 11300 | 0.0146 | - |
1.0104 | 11350 | 0.011 | - |
1.0149 | 11400 | 0.0116 | - |
1.0193 | 11450 | 0.0114 | - |
1.0238 | 11500 | 0.0103 | - |
1.0282 | 11550 | 0.0111 | - |
1.0327 | 11600 | 0.0082 | - |
1.0371 | 11650 | 0.011 | - |
1.0416 | 11700 | 0.0103 | - |
1.0460 | 11750 | 0.0061 | - |
1.0505 | 11800 | 0.0134 | - |
1.0549 | 11850 | 0.0081 | - |
1.0594 | 11900 | 0.0067 | - |
1.0638 | 11950 | 0.0108 | - |
1.0683 | 12000 | 0.0081 | - |
1.0727 | 12050 | 0.0093 | - |
1.0772 | 12100 | 0.012 | - |
1.0816 | 12150 | 0.0096 | - |
1.0861 | 12200 | 0.0091 | - |
1.0905 | 12250 | 0.0112 | - |
1.0950 | 12300 | 0.0087 | - |
1.0994 | 12350 | 0.0143 | - |
1.1039 | 12400 | 0.0078 | - |
1.1083 | 12450 | 0.0075 | - |
1.1128 | 12500 | 0.0112 | - |
1.1172 | 12550 | 0.01 | - |
1.1217 | 12600 | 0.0102 | - |
1.1261 | 12650 | 0.0079 | - |
1.1306 | 12700 | 0.0093 | - |
1.1350 | 12750 | 0.0117 | - |
1.1395 | 12800 | 0.0085 | - |
1.1440 | 12850 | 0.0082 | - |
1.1484 | 12900 | 0.0108 | - |
1.1529 | 12950 | 0.0125 | - |
1.1573 | 13000 | 0.0058 | - |
1.1618 | 13050 | 0.0082 | - |
1.1662 | 13100 | 0.0131 | - |
1.1707 | 13150 | 0.0067 | - |
1.1751 | 13200 | 0.0085 | - |
1.1796 | 13250 | 0.0108 | - |
1.1840 | 13300 | 0.0061 | - |
1.1885 | 13350 | 0.0052 | - |
1.1929 | 13400 | 0.0055 | - |
1.1974 | 13450 | 0.0086 | - |
1.2018 | 13500 | 0.0117 | - |
1.2063 | 13550 | 0.0083 | - |
1.2107 | 13600 | 0.0107 | - |
1.2152 | 13650 | 0.0102 | - |
1.2196 | 13700 | 0.0053 | - |
1.2241 | 13750 | 0.0059 | - |
1.2285 | 13800 | 0.0056 | - |
1.2330 | 13850 | 0.009 | - |
1.2374 | 13900 | 0.0073 | - |
1.2419 | 13950 | 0.0067 | - |
1.2463 | 14000 | 0.007 | - |
1.2508 | 14050 | 0.0104 | - |
1.2552 | 14100 | 0.0092 | - |
1.2597 | 14150 | 0.006 | - |
1.2641 | 14200 | 0.0077 | - |
1.2686 | 14250 | 0.0089 | - |
1.2730 | 14300 | 0.0181 | - |
1.2775 | 14350 | 0.0113 | - |
1.2819 | 14400 | 0.0158 | - |
1.2864 | 14450 | 0.0109 | - |
1.2908 | 14500 | 0.0078 | - |
1.2953 | 14550 | 0.007 | - |
1.2997 | 14600 | 0.0112 | - |
1.3042 | 14650 | 0.0084 | - |
1.3086 | 14700 | 0.0094 | - |
1.3131 | 14750 | 0.0044 | - |
1.3175 | 14800 | 0.0075 | - |
1.3220 | 14850 | 0.0052 | - |
1.3264 | 14900 | 0.0055 | - |
1.3309 | 14950 | 0.0076 | - |
1.3354 | 15000 | 0.0052 | - |
1.3398 | 15050 | 0.0051 | - |
1.3443 | 15100 | 0.0061 | - |
1.3487 | 15150 | 0.0072 | - |
1.3532 | 15200 | 0.0052 | - |
1.3576 | 15250 | 0.0084 | - |
1.3621 | 15300 | 0.0075 | - |
1.3665 | 15350 | 0.0087 | - |
1.3710 | 15400 | 0.0037 | - |
1.3754 | 15450 | 0.0055 | - |
1.3799 | 15500 | 0.007 | - |
1.3843 | 15550 | 0.006 | - |
1.3888 | 15600 | 0.0085 | - |
1.3932 | 15650 | 0.0086 | - |
1.3977 | 15700 | 0.0126 | - |
1.4021 | 15750 | 0.0096 | - |
1.4066 | 15800 | 0.0084 | - |
1.4110 | 15850 | 0.0086 | - |
1.4155 | 15900 | 0.0082 | - |
1.4199 | 15950 | 0.0077 | - |
1.4244 | 16000 | 0.0075 | - |
1.4288 | 16050 | 0.0053 | - |
1.4333 | 16100 | 0.0069 | - |
1.4377 | 16150 | 0.0097 | - |
1.4422 | 16200 | 0.0036 | - |
1.4466 | 16250 | 0.007 | - |
1.4511 | 16300 | 0.0067 | - |
1.4555 | 16350 | 0.0085 | - |
1.4600 | 16400 | 0.0105 | - |
1.4644 | 16450 | 0.0111 | - |
1.4689 | 16500 | 0.0078 | - |
1.4733 | 16550 | 0.0112 | - |
1.4778 | 16600 | 0.0096 | - |
1.4822 | 16650 | 0.0063 | - |
1.4867 | 16700 | 0.0074 | - |
1.4911 | 16750 | 0.0092 | - |
1.4956 | 16800 | 0.0084 | - |
1.5000 | 16850 | 0.0076 | - |
1.5045 | 16900 | 0.007 | - |
1.5089 | 16950 | 0.0106 | - |
1.5134 | 17000 | 0.0061 | - |
1.5178 | 17050 | 0.0068 | - |
1.5223 | 17100 | 0.0047 | - |
1.5268 | 17150 | 0.0076 | - |
1.5312 | 17200 | 0.008 | - |
1.5357 | 17250 | 0.0047 | - |
1.5401 | 17300 | 0.0072 | - |
1.5446 | 17350 | 0.0061 | - |
1.5490 | 17400 | 0.0037 | - |
1.5535 | 17450 | 0.0051 | - |
1.5579 | 17500 | 0.0069 | - |
1.5624 | 17550 | 0.0059 | - |
1.5668 | 17600 | 0.0064 | - |
1.5713 | 17650 | 0.0096 | - |
1.5757 | 17700 | 0.0046 | - |
1.5802 | 17750 | 0.0052 | - |
1.5846 | 17800 | 0.0061 | - |
1.5891 | 17850 | 0.0059 | - |
1.5935 | 17900 | 0.0094 | - |
1.5980 | 17950 | 0.0076 | - |
1.6024 | 18000 | 0.0077 | - |
1.6069 | 18050 | 0.0081 | - |
1.6113 | 18100 | 0.0055 | - |
1.6158 | 18150 | 0.0062 | - |
1.6202 | 18200 | 0.0086 | - |
1.6247 | 18250 | 0.0065 | - |
1.6291 | 18300 | 0.009 | - |
1.6336 | 18350 | 0.0092 | - |
1.6380 | 18400 | 0.0073 | - |
1.6425 | 18450 | 0.0114 | - |
1.6469 | 18500 | 0.009 | - |
1.6514 | 18550 | 0.0086 | - |
1.6558 | 18600 | 0.0046 | - |
1.6603 | 18650 | 0.0085 | - |
1.6647 | 18700 | 0.0109 | - |
1.6692 | 18750 | 0.0044 | - |
1.6736 | 18800 | 0.0054 | - |
1.6781 | 18850 | 0.0064 | - |
1.6825 | 18900 | 0.0057 | - |
1.6870 | 18950 | 0.0077 | - |
1.6914 | 19000 | 0.0077 | - |
1.6959 | 19050 | 0.0043 | - |
1.7003 | 19100 | 0.0104 | - |
1.7048 | 19150 | 0.0029 | - |
1.7092 | 19200 | 0.0092 | - |
1.7137 | 19250 | 0.0055 | - |
1.7182 | 19300 | 0.0061 | - |
1.7226 | 19350 | 0.0051 | - |
1.7271 | 19400 | 0.0096 | - |
1.7315 | 19450 | 0.005 | - |
1.7360 | 19500 | 0.0037 | - |
1.7404 | 19550 | 0.0089 | - |
1.7449 | 19600 | 0.0041 | - |
1.7493 | 19650 | 0.0059 | - |
1.7538 | 19700 | 0.0029 | - |
1.7582 | 19750 | 0.0077 | - |
1.7627 | 19800 | 0.0026 | - |
1.7671 | 19850 | 0.0112 | - |
1.7716 | 19900 | 0.0062 | - |
1.7760 | 19950 | 0.007 | - |
1.7805 | 20000 | 0.0068 | - |
1.7849 | 20050 | 0.0071 | - |
1.7894 | 20100 | 0.0082 | - |
1.7938 | 20150 | 0.0069 | - |
1.7983 | 20200 | 0.0078 | - |
1.8027 | 20250 | 0.0055 | - |
1.8072 | 20300 | 0.01 | - |
1.8116 | 20350 | 0.0123 | - |
1.8161 | 20400 | 0.0061 | - |
1.8205 | 20450 | 0.0076 | - |
1.8250 | 20500 | 0.0046 | - |
1.8294 | 20550 | 0.0042 | - |
1.8339 | 20600 | 0.0042 | - |
1.8383 | 20650 | 0.0059 | - |
1.8428 | 20700 | 0.005 | - |
1.8472 | 20750 | 0.0046 | - |
1.8517 | 20800 | 0.0102 | - |
1.8561 | 20850 | 0.0068 | - |
1.8606 | 20900 | 0.0054 | - |
1.8650 | 20950 | 0.0072 | - |
1.8695 | 21000 | 0.0057 | - |
1.8739 | 21050 | 0.0022 | - |
1.8784 | 21100 | 0.0036 | - |
1.8828 | 21150 | 0.0038 | - |
1.8873 | 21200 | 0.0078 | - |
1.8917 | 21250 | 0.0039 | - |
1.8962 | 21300 | 0.0031 | - |
1.9006 | 21350 | 0.0052 | - |
1.9051 | 21400 | 0.0066 | - |
1.9096 | 21450 | 0.0043 | - |
1.9140 | 21500 | 0.0052 | - |
1.9185 | 21550 | 0.0076 | - |
1.9229 | 21600 | 0.0067 | - |
1.9274 | 21650 | 0.0069 | - |
1.9318 | 21700 | 0.0028 | - |
1.9363 | 21750 | 0.0054 | - |
1.9407 | 21800 | 0.0078 | - |
1.9452 | 21850 | 0.0061 | - |
1.9496 | 21900 | 0.0045 | - |
1.9541 | 21950 | 0.0064 | - |
1.9585 | 22000 | 0.0077 | - |
1.9630 | 22050 | 0.0029 | - |
1.9674 | 22100 | 0.0054 | - |
1.9719 | 22150 | 0.0057 | - |
1.9763 | 22200 | 0.009 | - |
1.9808 | 22250 | 0.005 | - |
1.9852 | 22300 | 0.0026 | - |
1.9897 | 22350 | 0.0026 | - |
1.9941 | 22400 | 0.0075 | - |
1.9986 | 22450 | 0.0083 | - |
2.0030 | 22500 | 0.0055 | - |
2.0075 | 22550 | 0.007 | - |
2.0119 | 22600 | 0.0022 | - |
2.0164 | 22650 | 0.0026 | - |
2.0208 | 22700 | 0.0025 | - |
2.0253 | 22750 | 0.0038 | - |
2.0297 | 22800 | 0.0072 | - |
2.0342 | 22850 | 0.0063 | - |
2.0386 | 22900 | 0.0028 | - |
2.0431 | 22950 | 0.0032 | - |
2.0475 | 23000 | 0.0046 | - |
2.0520 | 23050 | 0.0057 | - |
2.0564 | 23100 | 0.0022 | - |
2.0609 | 23150 | 0.0061 | - |
2.0653 | 23200 | 0.0072 | - |
2.0698 | 23250 | 0.0043 | - |
2.0742 | 23300 | 0.0059 | - |
2.0787 | 23350 | 0.0054 | - |
2.0831 | 23400 | 0.0031 | - |
2.0876 | 23450 | 0.0085 | - |
2.0921 | 23500 | 0.0044 | - |
2.0965 | 23550 | 0.005 | - |
2.1010 | 23600 | 0.0064 | - |
2.1054 | 23650 | 0.0064 | - |
2.1099 | 23700 | 0.0065 | - |
2.1143 | 23750 | 0.0046 | - |
2.1188 | 23800 | 0.0062 | - |
2.1232 | 23850 | 0.0028 | - |
2.1277 | 23900 | 0.0058 | - |
2.1321 | 23950 | 0.0052 | - |
2.1366 | 24000 | 0.0027 | - |
2.1410 | 24050 | 0.0073 | - |
2.1455 | 24100 | 0.0029 | - |
2.1499 | 24150 | 0.0022 | - |
2.1544 | 24200 | 0.0039 | - |
2.1588 | 24250 | 0.006 | - |
2.1633 | 24300 | 0.0038 | - |
2.1677 | 24350 | 0.006 | - |
2.1722 | 24400 | 0.0044 | - |
2.1766 | 24450 | 0.0023 | - |
2.1811 | 24500 | 0.0032 | - |
2.1855 | 24550 | 0.0039 | - |
2.1900 | 24600 | 0.0039 | - |
2.1944 | 24650 | 0.0038 | - |
2.1989 | 24700 | 0.0046 | - |
2.2033 | 24750 | 0.0024 | - |
2.2078 | 24800 | 0.0053 | - |
2.2122 | 24850 | 0.0033 | - |
2.2167 | 24900 | 0.0032 | - |
2.2211 | 24950 | 0.0054 | - |
2.2256 | 25000 | 0.0053 | - |
2.2300 | 25050 | 0.0044 | - |
2.2345 | 25100 | 0.005 | - |
2.2389 | 25150 | 0.0049 | - |
2.2434 | 25200 | 0.0046 | - |
2.2478 | 25250 | 0.0071 | - |
2.2523 | 25300 | 0.0063 | - |
2.2567 | 25350 | 0.0052 | - |
2.2612 | 25400 | 0.0031 | - |
2.2656 | 25450 | 0.0026 | - |
2.2701 | 25500 | 0.0026 | - |
2.2745 | 25550 | 0.0039 | - |
2.2790 | 25600 | 0.0078 | - |
2.2835 | 25650 | 0.0034 | - |
2.2879 | 25700 | 0.0033 | - |
2.2924 | 25750 | 0.0051 | - |
2.2968 | 25800 | 0.0042 | - |
2.3013 | 25850 | 0.0052 | - |
2.3057 | 25900 | 0.0029 | - |
2.3102 | 25950 | 0.0034 | - |
2.3146 | 26000 | 0.0046 | - |
2.3191 | 26050 | 0.0103 | - |
2.3235 | 26100 | 0.0044 | - |
2.3280 | 26150 | 0.0033 | - |
2.3324 | 26200 | 0.0034 | - |
2.3369 | 26250 | 0.0028 | - |
2.3413 | 26300 | 0.0045 | - |
2.3458 | 26350 | 0.0028 | - |
2.3502 | 26400 | 0.0034 | - |
2.3547 | 26450 | 0.0023 | - |
2.3591 | 26500 | 0.0068 | - |
2.3636 | 26550 | 0.0042 | - |
2.3680 | 26600 | 0.0028 | - |
2.3725 | 26650 | 0.0022 | - |
2.3769 | 26700 | 0.0083 | - |
2.3814 | 26750 | 0.0045 | - |
2.3858 | 26800 | 0.0043 | - |
2.3903 | 26850 | 0.003 | - |
2.3947 | 26900 | 0.0057 | - |
2.3992 | 26950 | 0.0055 | - |
2.4036 | 27000 | 0.0036 | - |
2.4081 | 27050 | 0.0057 | - |
2.4125 | 27100 | 0.0035 | - |
2.4170 | 27150 | 0.0055 | - |
2.4214 | 27200 | 0.0029 | - |
2.4259 | 27250 | 0.0032 | - |
2.4303 | 27300 | 0.0035 | - |
2.4348 | 27350 | 0.0038 | - |
2.4392 | 27400 | 0.0046 | - |
2.4437 | 27450 | 0.0037 | - |
2.4481 | 27500 | 0.0036 | - |
2.4526 | 27550 | 0.0083 | - |
2.4570 | 27600 | 0.0034 | - |
2.4615 | 27650 | 0.0022 | - |
2.4659 | 27700 | 0.0054 | - |
2.4704 | 27750 | 0.0032 | - |
2.4749 | 27800 | 0.0054 | - |
2.4793 | 27850 | 0.0071 | - |
2.4838 | 27900 | 0.0052 | - |
2.4882 | 27950 | 0.0029 | - |
2.4927 | 28000 | 0.0024 | - |
2.4971 | 28050 | 0.0053 | - |
2.5016 | 28100 | 0.0054 | - |
2.5060 | 28150 | 0.0038 | - |
2.5105 | 28200 | 0.0059 | - |
2.5149 | 28250 | 0.0024 | - |
2.5194 | 28300 | 0.0061 | - |
2.5238 | 28350 | 0.0013 | - |
2.5283 | 28400 | 0.0056 | - |
2.5327 | 28450 | 0.003 | - |
2.5372 | 28500 | 0.0078 | - |
2.5416 | 28550 | 0.0051 | - |
2.5461 | 28600 | 0.009 | - |
2.5505 | 28650 | 0.0022 | - |
2.5550 | 28700 | 0.0041 | - |
2.5594 | 28750 | 0.0045 | - |
2.5639 | 28800 | 0.0035 | - |
2.5683 | 28850 | 0.0046 | - |
2.5728 | 28900 | 0.0055 | - |
2.5772 | 28950 | 0.0044 | - |
2.5817 | 29000 | 0.0039 | - |
2.5861 | 29050 | 0.0072 | - |
2.5906 | 29100 | 0.004 | - |
2.5950 | 29150 | 0.0085 | - |
2.5995 | 29200 | 0.0029 | - |
2.6039 | 29250 | 0.0024 | - |
2.6084 | 29300 | 0.0063 | - |
2.6128 | 29350 | 0.0047 | - |
2.6173 | 29400 | 0.0067 | - |
2.6217 | 29450 | 0.0025 | - |
2.6262 | 29500 | 0.0044 | - |
2.6306 | 29550 | 0.0031 | - |
2.6351 | 29600 | 0.0019 | - |
2.6395 | 29650 | 0.0031 | - |
2.6440 | 29700 | 0.0029 | - |
2.6484 | 29750 | 0.005 | - |
2.6529 | 29800 | 0.0067 | - |
2.6573 | 29850 | 0.0025 | - |
2.6618 | 29900 | 0.0045 | - |
2.6663 | 29950 | 0.0046 | - |
2.6707 | 30000 | 0.0035 | - |
2.6752 | 30050 | 0.0052 | - |
2.6796 | 30100 | 0.0027 | - |
2.6841 | 30150 | 0.0024 | - |
2.6885 | 30200 | 0.0062 | - |
2.6930 | 30250 | 0.0044 | - |
2.6974 | 30300 | 0.0054 | - |
2.7019 | 30350 | 0.0038 | - |
2.7063 | 30400 | 0.0037 | - |
2.7108 | 30450 | 0.0036 | - |
2.7152 | 30500 | 0.0036 | - |
2.7197 | 30550 | 0.0029 | - |
2.7241 | 30600 | 0.0013 | - |
2.7286 | 30650 | 0.0062 | - |
2.7330 | 30700 | 0.0043 | - |
2.7375 | 30750 | 0.0064 | - |
2.7419 | 30800 | 0.0027 | - |
2.7464 | 30850 | 0.0041 | - |
2.7508 | 30900 | 0.0076 | - |
2.7553 | 30950 | 0.0093 | - |
2.7597 | 31000 | 0.0036 | - |
2.7642 | 31050 | 0.0065 | - |
2.7686 | 31100 | 0.0052 | - |
2.7731 | 31150 | 0.0066 | - |
2.7775 | 31200 | 0.0078 | - |
2.7820 | 31250 | 0.0026 | - |
2.7864 | 31300 | 0.0022 | - |
2.7909 | 31350 | 0.0022 | - |
2.7953 | 31400 | 0.0098 | - |
2.7998 | 31450 | 0.0062 | - |
2.8042 | 31500 | 0.0055 | - |
2.8087 | 31550 | 0.0033 | - |
2.8131 | 31600 | 0.0044 | - |
2.8176 | 31650 | 0.0047 | - |
2.8220 | 31700 | 0.0022 | - |
2.8265 | 31750 | 0.0043 | - |
2.8309 | 31800 | 0.0012 | - |
2.8354 | 31850 | 0.0008 | - |
2.8398 | 31900 | 0.0013 | - |
2.8443 | 31950 | 0.0033 | - |
2.8487 | 32000 | 0.0062 | - |
2.8532 | 32050 | 0.003 | - |
2.8577 | 32100 | 0.003 | - |
2.8621 | 32150 | 0.0033 | - |
2.8666 | 32200 | 0.004 | - |
2.8710 | 32250 | 0.0026 | - |
2.8755 | 32300 | 0.0043 | - |
2.8799 | 32350 | 0.0027 | - |
2.8844 | 32400 | 0.0071 | - |
2.8888 | 32450 | 0.0042 | - |
2.8933 | 32500 | 0.0055 | - |
2.8977 | 32550 | 0.0033 | - |
2.9022 | 32600 | 0.0056 | - |
2.9066 | 32650 | 0.0013 | - |
2.9111 | 32700 | 0.006 | - |
2.9155 | 32750 | 0.0038 | - |
2.9200 | 32800 | 0.0048 | - |
2.9244 | 32850 | 0.0028 | - |
2.9289 | 32900 | 0.0069 | - |
2.9333 | 32950 | 0.0013 | - |
2.9378 | 33000 | 0.0035 | - |
2.9422 | 33050 | 0.0058 | - |
2.9467 | 33100 | 0.001 | - |
2.9511 | 33150 | 0.0053 | - |
2.9556 | 33200 | 0.0058 | - |
2.9600 | 33250 | 0.0034 | - |
2.9645 | 33300 | 0.0023 | - |
2.9689 | 33350 | 0.0055 | - |
2.9734 | 33400 | 0.0069 | - |
2.9778 | 33450 | 0.0034 | - |
2.9823 | 33500 | 0.0031 | - |
2.9867 | 33550 | 0.0031 | - |
2.9912 | 33600 | 0.0039 | - |
2.9956 | 33650 | 0.0027 | - |
3.0001 | 33700 | 0.0041 | - |
3.0045 | 33750 | 0.0043 | - |
3.0090 | 33800 | 0.0041 | - |
3.0134 | 33850 | 0.0029 | - |
3.0179 | 33900 | 0.0013 | - |
3.0223 | 33950 | 0.0046 | - |
3.0268 | 34000 | 0.0018 | - |
3.0312 | 34050 | 0.0042 | - |
3.0357 | 34100 | 0.002 | - |
3.0401 | 34150 | 0.0018 | - |
3.0446 | 34200 | 0.0054 | - |
3.0491 | 34250 | 0.002 | - |
3.0535 | 34300 | 0.0036 | - |
3.0580 | 34350 | 0.0038 | - |
3.0624 | 34400 | 0.0018 | - |
3.0669 | 34450 | 0.0009 | - |
3.0713 | 34500 | 0.0042 | - |
3.0758 | 34550 | 0.0042 | - |
3.0802 | 34600 | 0.0018 | - |
3.0847 | 34650 | 0.0009 | - |
3.0891 | 34700 | 0.0043 | - |
3.0936 | 34750 | 0.0076 | - |
3.0980 | 34800 | 0.002 | - |
3.1025 | 34850 | 0.0034 | - |
3.1069 | 34900 | 0.0031 | - |
3.1114 | 34950 | 0.0034 | - |
3.1158 | 35000 | 0.0029 | - |
3.1203 | 35050 | 0.0027 | - |
3.1247 | 35100 | 0.0053 | - |
3.1292 | 35150 | 0.0031 | - |
3.1336 | 35200 | 0.0014 | - |
3.1381 | 35250 | 0.0031 | - |
3.1425 | 35300 | 0.0038 | - |
3.1470 | 35350 | 0.0034 | - |
3.1514 | 35400 | 0.0037 | - |
3.1559 | 35450 | 0.0027 | - |
3.1603 | 35500 | 0.0077 | - |
3.1648 | 35550 | 0.0031 | - |
3.1692 | 35600 | 0.0042 | - |
3.1737 | 35650 | 0.0055 | - |
3.1781 | 35700 | 0.0078 | - |
3.1826 | 35750 | 0.0038 | - |
3.1870 | 35800 | 0.004 | - |
3.1915 | 35850 | 0.0045 | - |
3.1959 | 35900 | 0.003 | - |
3.2004 | 35950 | 0.0023 | - |
3.2048 | 36000 | 0.0035 | - |
3.2093 | 36050 | 0.0028 | - |
3.2137 | 36100 | 0.0064 | - |
3.2182 | 36150 | 0.0027 | - |
3.2226 | 36200 | 0.0038 | - |
3.2271 | 36250 | 0.0022 | - |
3.2315 | 36300 | 0.0015 | - |
3.2360 | 36350 | 0.0011 | - |
3.2405 | 36400 | 0.0019 | - |
3.2449 | 36450 | 0.003 | - |
3.2494 | 36500 | 0.0041 | - |
3.2538 | 36550 | 0.0033 | - |
3.2583 | 36600 | 0.0008 | - |
3.2627 | 36650 | 0.0012 | - |
3.2672 | 36700 | 0.0061 | - |
3.2716 | 36750 | 0.0025 | - |
3.2761 | 36800 | 0.0028 | - |
3.2805 | 36850 | 0.0026 | - |
3.2850 | 36900 | 0.0053 | - |
3.2894 | 36950 | 0.0033 | - |
3.2939 | 37000 | 0.0022 | - |
3.2983 | 37050 | 0.0062 | - |
3.3028 | 37100 | 0.0021 | - |
3.3072 | 37150 | 0.0024 | - |
3.3117 | 37200 | 0.0042 | - |
3.3161 | 37250 | 0.0061 | - |
3.3206 | 37300 | 0.002 | - |
3.3250 | 37350 | 0.0009 | - |
3.3295 | 37400 | 0.0048 | - |
3.3339 | 37450 | 0.0027 | - |
3.3384 | 37500 | 0.0042 | - |
3.3428 | 37550 | 0.0046 | - |
3.3473 | 37600 | 0.001 | - |
3.3517 | 37650 | 0.0026 | - |
3.3562 | 37700 | 0.0029 | - |
3.3606 | 37750 | 0.0027 | - |
3.3651 | 37800 | 0.0032 | - |
3.3695 | 37850 | 0.0018 | - |
3.3740 | 37900 | 0.0021 | - |
3.3784 | 37950 | 0.0022 | - |
3.3829 | 38000 | 0.0019 | - |
3.3873 | 38050 | 0.0038 | - |
3.3918 | 38100 | 0.0018 | - |
3.3962 | 38150 | 0.0022 | - |
3.4007 | 38200 | 0.0024 | - |
3.4051 | 38250 | 0.0032 | - |
3.4096 | 38300 | 0.0032 | - |
3.4140 | 38350 | 0.0042 | - |
3.4185 | 38400 | 0.0032 | - |
3.4230 | 38450 | 0.0024 | - |
3.4274 | 38500 | 0.0048 | - |
3.4319 | 38550 | 0.0041 | - |
3.4363 | 38600 | 0.0028 | - |
3.4408 | 38650 | 0.0041 | - |
3.4452 | 38700 | 0.0018 | - |
3.4497 | 38750 | 0.0018 | - |
3.4541 | 38800 | 0.0021 | - |
3.4586 | 38850 | 0.0036 | - |
3.4630 | 38900 | 0.0042 | - |
3.4675 | 38950 | 0.001 | - |
3.4719 | 39000 | 0.0027 | - |
3.4764 | 39050 | 0.0027 | - |
3.4808 | 39100 | 0.0032 | - |
3.4853 | 39150 | 0.001 | - |
3.4897 | 39200 | 0.002 | - |
3.4942 | 39250 | 0.0019 | - |
3.4986 | 39300 | 0.0016 | - |
3.5031 | 39350 | 0.0046 | - |
3.5075 | 39400 | 0.0023 | - |
3.5120 | 39450 | 0.004 | - |
3.5164 | 39500 | 0.002 | - |
3.5209 | 39550 | 0.0056 | - |
3.5253 | 39600 | 0.0019 | - |
3.5298 | 39650 | 0.0025 | - |
3.5342 | 39700 | 0.0037 | - |
3.5387 | 39750 | 0.0021 | - |
3.5431 | 39800 | 0.0051 | - |
3.5476 | 39850 | 0.0032 | - |
3.5520 | 39900 | 0.0058 | - |
3.5565 | 39950 | 0.0015 | - |
3.5609 | 40000 | 0.0026 | - |
3.5654 | 40050 | 0.003 | - |
3.5698 | 40100 | 0.0035 | - |
3.5743 | 40150 | 0.0028 | - |
3.5787 | 40200 | 0.0032 | - |
3.5832 | 40250 | 0.0036 | - |
3.5876 | 40300 | 0.004 | - |
3.5921 | 40350 | 0.0045 | - |
3.5965 | 40400 | 0.002 | - |
3.6010 | 40450 | 0.0038 | - |
3.6054 | 40500 | 0.0053 | - |
3.6099 | 40550 | 0.003 | - |
3.6144 | 40600 | 0.0047 | - |
3.6188 | 40650 | 0.0042 | - |
3.6233 | 40700 | 0.0045 | - |
3.6277 | 40750 | 0.0014 | - |
3.6322 | 40800 | 0.0034 | - |
3.6366 | 40850 | 0.0029 | - |
3.6411 | 40900 | 0.002 | - |
3.6455 | 40950 | 0.0033 | - |
3.6500 | 41000 | 0.0059 | - |
3.6544 | 41050 | 0.0025 | - |
3.6589 | 41100 | 0.0016 | - |
3.6633 | 41150 | 0.0033 | - |
3.6678 | 41200 | 0.003 | - |
3.6722 | 41250 | 0.0011 | - |
3.6767 | 41300 | 0.0027 | - |
3.6811 | 41350 | 0.0041 | - |
3.6856 | 41400 | 0.002 | - |
3.6900 | 41450 | 0.0047 | - |
3.6945 | 41500 | 0.0036 | - |
3.6989 | 41550 | 0.0032 | - |
3.7034 | 41600 | 0.0064 | - |
3.7078 | 41650 | 0.0034 | - |
3.7123 | 41700 | 0.0025 | - |
3.7167 | 41750 | 0.0022 | - |
3.7212 | 41800 | 0.0018 | - |
3.7256 | 41850 | 0.003 | - |
3.7301 | 41900 | 0.0036 | - |
3.7345 | 41950 | 0.0027 | - |
3.7390 | 42000 | 0.0055 | - |
3.7434 | 42050 | 0.0025 | - |
3.7479 | 42100 | 0.0007 | - |
3.7523 | 42150 | 0.0013 | - |
3.7568 | 42200 | 0.0039 | - |
3.7612 | 42250 | 0.0034 | - |
3.7657 | 42300 | 0.0009 | - |
3.7701 | 42350 | 0.0017 | - |
3.7746 | 42400 | 0.0032 | - |
3.7790 | 42450 | 0.0035 | - |
3.7835 | 42500 | 0.0068 | - |
3.7879 | 42550 | 0.0018 | - |
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3.7968 | 42650 | 0.0025 | - |
3.8013 | 42700 | 0.0052 | - |
3.8058 | 42750 | 0.0022 | - |
3.8102 | 42800 | 0.004 | - |
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3.8191 | 42900 | 0.0052 | - |
3.8236 | 42950 | 0.0033 | - |
3.8280 | 43000 | 0.0025 | - |
3.8325 | 43050 | 0.0044 | - |
3.8369 | 43100 | 0.0032 | - |
3.8414 | 43150 | 0.0054 | - |
3.8458 | 43200 | 0.0016 | - |
3.8503 | 43250 | 0.0049 | - |
3.8547 | 43300 | 0.0034 | - |
3.8592 | 43350 | 0.0045 | - |
3.8636 | 43400 | 0.0025 | - |
3.8681 | 43450 | 0.0048 | - |
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3.8814 | 43600 | 0.0026 | - |
3.8859 | 43650 | 0.0024 | - |
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3.8948 | 43750 | 0.0032 | - |
3.8992 | 43800 | 0.0057 | - |
3.9037 | 43850 | 0.003 | - |
3.9081 | 43900 | 0.0019 | - |
3.9126 | 43950 | 0.0038 | - |
3.9170 | 44000 | 0.0033 | - |
3.9215 | 44050 | 0.0021 | - |
3.9259 | 44100 | 0.0023 | - |
3.9304 | 44150 | 0.0013 | - |
3.9348 | 44200 | 0.0019 | - |
3.9393 | 44250 | 0.0037 | - |
3.9437 | 44300 | 0.003 | - |
3.9482 | 44350 | 0.0024 | - |
3.9526 | 44400 | 0.0007 | - |
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3.9660 | 44550 | 0.0022 | - |
3.9704 | 44600 | 0.0029 | - |
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3.9882 | 44800 | 0.0047 | - |
3.9927 | 44850 | 0.0034 | - |
3.9972 | 44900 | 0.0046 | - |
4.0016 | 44950 | 0.0031 | - |
4.0061 | 45000 | 0.0029 | - |
4.0105 | 45050 | 0.0028 | - |
4.0150 | 45100 | 0.005 | - |
4.0194 | 45150 | 0.0029 | - |
4.0239 | 45200 | 0.0038 | - |
4.0283 | 45250 | 0.0008 | - |
4.0328 | 45300 | 0.003 | - |
4.0372 | 45350 | 0.0015 | - |
4.0417 | 45400 | 0.0027 | - |
4.0461 | 45450 | 0.0049 | - |
4.0506 | 45500 | 0.0023 | - |
4.0550 | 45550 | 0.0027 | - |
4.0595 | 45600 | 0.0022 | - |
4.0639 | 45650 | 0.0013 | - |
4.0684 | 45700 | 0.0032 | - |
4.0728 | 45750 | 0.0016 | - |
4.0773 | 45800 | 0.0041 | - |
4.0817 | 45850 | 0.0033 | - |
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4.0906 | 45950 | 0.0043 | - |
4.0951 | 46000 | 0.0007 | - |
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4.1040 | 46100 | 0.0043 | - |
4.1084 | 46150 | 0.0064 | - |
4.1129 | 46200 | 0.0028 | - |
4.1173 | 46250 | 0.0026 | - |
4.1218 | 46300 | 0.0006 | - |
4.1262 | 46350 | 0.0032 | - |
4.1307 | 46400 | 0.0021 | - |
4.1351 | 46450 | 0.0047 | - |
4.1396 | 46500 | 0.0017 | - |
4.1440 | 46550 | 0.0011 | - |
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4.1529 | 46650 | 0.0045 | - |
4.1574 | 46700 | 0.0018 | - |
4.1618 | 46750 | 0.002 | - |
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4.1707 | 46850 | 0.0007 | - |
4.1752 | 46900 | 0.0014 | - |
4.1796 | 46950 | 0.0029 | - |
4.1841 | 47000 | 0.0029 | - |
4.1886 | 47050 | 0.0045 | - |
4.1930 | 47100 | 0.0019 | - |
4.1975 | 47150 | 0.0008 | - |
4.2019 | 47200 | 0.0037 | - |
4.2064 | 47250 | 0.0052 | - |
4.2108 | 47300 | 0.0018 | - |
4.2153 | 47350 | 0.0007 | - |
4.2197 | 47400 | 0.0036 | - |
4.2242 | 47450 | 0.0018 | - |
4.2286 | 47500 | 0.0009 | - |
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4.2375 | 47600 | 0.0018 | - |
4.2420 | 47650 | 0.0014 | - |
4.2464 | 47700 | 0.0022 | - |
4.2509 | 47750 | 0.0028 | - |
4.2553 | 47800 | 0.0044 | - |
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4.2642 | 47900 | 0.0018 | - |
4.2687 | 47950 | 0.0026 | - |
4.2731 | 48000 | 0.002 | - |
4.2776 | 48050 | 0.0037 | - |
4.2820 | 48100 | 0.0042 | - |
4.2865 | 48150 | 0.0011 | - |
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4.3043 | 48350 | 0.0027 | - |
4.3087 | 48400 | 0.0007 | - |
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4.3221 | 48550 | 0.0012 | - |
4.3265 | 48600 | 0.001 | - |
4.3310 | 48650 | 0.0031 | - |
4.3354 | 48700 | 0.001 | - |
4.3399 | 48750 | 0.0019 | - |
4.3443 | 48800 | 0.0008 | - |
4.3488 | 48850 | 0.0016 | - |
4.3532 | 48900 | 0.0021 | - |
4.3577 | 48950 | 0.0045 | - |
4.3621 | 49000 | 0.0031 | - |
4.3666 | 49050 | 0.0017 | - |
4.3710 | 49100 | 0.0015 | - |
4.3755 | 49150 | 0.0038 | - |
4.3800 | 49200 | 0.0018 | - |
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4.3933 | 49350 | 0.0029 | - |
4.3978 | 49400 | 0.0043 | - |
4.4022 | 49450 | 0.0022 | - |
4.4067 | 49500 | 0.0015 | - |
4.4111 | 49550 | 0.0011 | - |
4.4156 | 49600 | 0.0013 | - |
4.4200 | 49650 | 0.0049 | - |
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4.4289 | 49750 | 0.0007 | - |
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4.4378 | 49850 | 0.0024 | - |
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4.4512 | 50000 | 0.0028 | - |
4.4556 | 50050 | 0.0032 | - |
4.4601 | 50100 | 0.0014 | - |
4.4645 | 50150 | 0.0026 | - |
4.4690 | 50200 | 0.0006 | - |
4.4734 | 50250 | 0.0022 | - |
4.4779 | 50300 | 0.0037 | - |
4.4823 | 50350 | 0.0049 | - |
4.4868 | 50400 | 0.0021 | - |
4.4912 | 50450 | 0.0042 | - |
4.4957 | 50500 | 0.0008 | - |
4.5001 | 50550 | 0.0006 | - |
4.5046 | 50600 | 0.0018 | - |
4.5090 | 50650 | 0.0027 | - |
4.5135 | 50700 | 0.0028 | - |
4.5179 | 50750 | 0.0019 | - |
4.5224 | 50800 | 0.0046 | - |
4.5268 | 50850 | 0.0027 | - |
4.5313 | 50900 | 0.0025 | - |
4.5357 | 50950 | 0.0012 | - |
4.5402 | 51000 | 0.0031 | - |
4.5446 | 51050 | 0.0051 | - |
4.5491 | 51100 | 0.004 | - |
4.5535 | 51150 | 0.0032 | - |
4.5580 | 51200 | 0.0015 | - |
4.5624 | 51250 | 0.0012 | - |
4.5669 | 51300 | 0.0031 | - |
4.5714 | 51350 | 0.003 | - |
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4.5803 | 51450 | 0.0016 | - |
4.5847 | 51500 | 0.0027 | - |
4.5892 | 51550 | 0.0009 | - |
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4.6070 | 51750 | 0.0009 | - |
4.6114 | 51800 | 0.0015 | - |
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4.6248 | 51950 | 0.0019 | - |
4.6292 | 52000 | 0.004 | - |
4.6337 | 52050 | 0.001 | - |
4.6381 | 52100 | 0.001 | - |
4.6426 | 52150 | 0.0048 | - |
4.6470 | 52200 | 0.0006 | - |
4.6515 | 52250 | 0.0021 | - |
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4.6604 | 52350 | 0.0064 | - |
4.6648 | 52400 | 0.0028 | - |
4.6693 | 52450 | 0.0008 | - |
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4.6826 | 52600 | 0.0035 | - |
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4.6915 | 52700 | 0.0047 | - |
4.6960 | 52750 | 0.0015 | - |
4.7004 | 52800 | 0.0012 | - |
4.7049 | 52850 | 0.002 | - |
4.7093 | 52900 | 0.0019 | - |
4.7138 | 52950 | 0.0027 | - |
4.7182 | 53000 | 0.0025 | - |
4.7227 | 53050 | 0.0008 | - |
4.7271 | 53100 | 0.001 | - |
4.7316 | 53150 | 0.0023 | - |
4.7360 | 53200 | 0.0027 | - |
4.7405 | 53250 | 0.0007 | - |
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4.7494 | 53350 | 0.0045 | - |
4.7539 | 53400 | 0.0054 | - |
4.7583 | 53450 | 0.0047 | - |
4.7628 | 53500 | 0.0021 | - |
4.7672 | 53550 | 0.0025 | - |
4.7717 | 53600 | 0.0036 | - |
4.7761 | 53650 | 0.0039 | - |
4.7806 | 53700 | 0.0008 | - |
4.7850 | 53750 | 0.0043 | - |
4.7895 | 53800 | 0.0016 | - |
4.7939 | 53850 | 0.0011 | - |
4.7984 | 53900 | 0.0027 | - |
4.8028 | 53950 | 0.0013 | - |
4.8073 | 54000 | 0.0028 | - |
4.8117 | 54050 | 0.0031 | - |
4.8162 | 54100 | 0.0014 | - |
4.8206 | 54150 | 0.0007 | - |
4.8251 | 54200 | 0.0031 | - |
4.8295 | 54250 | 0.0009 | - |
4.8340 | 54300 | 0.0006 | - |
4.8384 | 54350 | 0.0015 | - |
4.8429 | 54400 | 0.0021 | - |
4.8473 | 54450 | 0.0045 | - |
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4.8562 | 54550 | 0.0007 | - |
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4.8651 | 54650 | 0.0014 | - |
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4.8740 | 54750 | 0.0023 | - |
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4.9319 | 55400 | 0.0014 | - |
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4.9453 | 55550 | 0.0029 | - |
4.9497 | 55600 | 0.0054 | - |
4.9542 | 55650 | 0.0017 | - |
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4.9675 | 55800 | 0.0006 | - |
4.9720 | 55850 | 0.0018 | - |
4.9764 | 55900 | 0.0018 | - |
4.9809 | 55950 | 0.0006 | - |
4.9853 | 56000 | 0.0032 | - |
4.9898 | 56050 | 0.0067 | - |
4.9942 | 56100 | 0.0023 | - |
4.9987 | 56150 | 0.0033 | - |
Framework Versions
- Python: 3.11.13
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.2
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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