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")
# Run inference
preds = model("do americans need visas in canada")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 8.3394 | 28 |
Label | Training Sample Count |
---|---|
1 | 100 |
2 | 100 |
3 | 100 |
4 | 100 |
5 | 100 |
6 | 100 |
7 | 100 |
8 | 100 |
9 | 100 |
10 | 100 |
11 | 100 |
12 | 100 |
13 | 100 |
14 | 100 |
15 | 100 |
16 | 100 |
17 | 100 |
18 | 100 |
19 | 100 |
20 | 100 |
21 | 100 |
22 | 100 |
23 | 100 |
24 | 100 |
25 | 100 |
26 | 100 |
27 | 100 |
28 | 100 |
29 | 100 |
30 | 100 |
31 | 100 |
32 | 100 |
33 | 100 |
34 | 100 |
35 | 100 |
36 | 100 |
37 | 100 |
38 | 100 |
39 | 100 |
40 | 100 |
41 | 100 |
42 | 100 |
43 | 100 |
44 | 100 |
45 | 100 |
46 | 100 |
47 | 100 |
48 | 100 |
49 | 100 |
50 | 100 |
51 | 100 |
52 | 100 |
53 | 100 |
54 | 100 |
55 | 100 |
56 | 100 |
57 | 100 |
58 | 100 |
59 | 100 |
60 | 100 |
61 | 100 |
62 | 100 |
63 | 100 |
64 | 100 |
65 | 100 |
66 | 100 |
67 | 100 |
68 | 100 |
69 | 100 |
70 | 100 |
71 | 100 |
72 | 100 |
73 | 100 |
74 | 100 |
75 | 100 |
76 | 100 |
77 | 100 |
78 | 100 |
79 | 100 |
80 | 100 |
81 | 100 |
82 | 100 |
83 | 100 |
84 | 100 |
85 | 100 |
86 | 100 |
87 | 100 |
88 | 100 |
89 | 100 |
90 | 100 |
91 | 100 |
92 | 100 |
93 | 100 |
94 | 100 |
95 | 100 |
96 | 100 |
97 | 100 |
98 | 100 |
99 | 100 |
100 | 100 |
101 | 100 |
102 | 100 |
103 | 100 |
104 | 100 |
105 | 100 |
106 | 100 |
107 | 100 |
108 | 100 |
109 | 100 |
110 | 100 |
111 | 100 |
112 | 100 |
113 | 100 |
114 | 100 |
115 | 100 |
116 | 100 |
117 | 100 |
118 | 100 |
119 | 100 |
120 | 100 |
121 | 100 |
122 | 100 |
123 | 100 |
124 | 100 |
125 | 100 |
126 | 100 |
127 | 100 |
128 | 100 |
129 | 100 |
130 | 100 |
131 | 100 |
132 | 100 |
133 | 100 |
134 | 100 |
135 | 100 |
136 | 100 |
137 | 100 |
138 | 100 |
139 | 100 |
140 | 100 |
141 | 100 |
142 | 100 |
143 | 100 |
144 | 100 |
145 | 100 |
146 | 100 |
147 | 100 |
148 | 100 |
149 | 100 |
150 | 100 |
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 | 0.1511 | - |
0.0053 | 50 | 0.1265 | - |
0.0107 | 100 | 0.1279 | - |
0.016 | 150 | 0.1129 | - |
0.0213 | 200 | 0.1168 | - |
0.0267 | 250 | 0.1148 | - |
0.032 | 300 | 0.1007 | - |
0.0373 | 350 | 0.1026 | - |
0.0427 | 400 | 0.1037 | - |
0.048 | 450 | 0.0987 | - |
0.0533 | 500 | 0.0888 | - |
0.0587 | 550 | 0.0851 | - |
0.064 | 600 | 0.0858 | - |
0.0693 | 650 | 0.0913 | - |
0.0747 | 700 | 0.0825 | - |
0.08 | 750 | 0.0733 | - |
0.0853 | 800 | 0.0834 | - |
0.0907 | 850 | 0.0751 | - |
0.096 | 900 | 0.0762 | - |
0.1013 | 950 | 0.0726 | - |
0.1067 | 1000 | 0.0646 | - |
0.112 | 1050 | 0.0637 | - |
0.1173 | 1100 | 0.0604 | - |
0.1227 | 1150 | 0.0586 | - |
0.128 | 1200 | 0.0582 | - |
0.1333 | 1250 | 0.0548 | - |
0.1387 | 1300 | 0.0619 | - |
0.144 | 1350 | 0.0552 | - |
0.1493 | 1400 | 0.0487 | - |
0.1547 | 1450 | 0.0543 | - |
0.16 | 1500 | 0.055 | - |
0.1653 | 1550 | 0.0493 | - |
0.1707 | 1600 | 0.0498 | - |
0.176 | 1650 | 0.0529 | - |
0.1813 | 1700 | 0.0402 | - |
0.1867 | 1750 | 0.0442 | - |
0.192 | 1800 | 0.048 | - |
0.1973 | 1850 | 0.0406 | - |
0.2027 | 1900 | 0.0439 | - |
0.208 | 1950 | 0.0397 | - |
0.2133 | 2000 | 0.0356 | - |
0.2187 | 2050 | 0.0462 | - |
0.224 | 2100 | 0.0381 | - |
0.2293 | 2150 | 0.0382 | - |
0.2347 | 2200 | 0.0322 | - |
0.24 | 2250 | 0.0379 | - |
0.2453 | 2300 | 0.0365 | - |
0.2507 | 2350 | 0.0353 | - |
0.256 | 2400 | 0.0357 | - |
0.2613 | 2450 | 0.0312 | - |
0.2667 | 2500 | 0.0317 | - |
0.272 | 2550 | 0.031 | - |
0.2773 | 2600 | 0.0329 | - |
0.2827 | 2650 | 0.0328 | - |
0.288 | 2700 | 0.0297 | - |
0.2933 | 2750 | 0.0303 | - |
0.2987 | 2800 | 0.0245 | - |
0.304 | 2850 | 0.0296 | - |
0.3093 | 2900 | 0.029 | - |
0.3147 | 2950 | 0.025 | - |
0.32 | 3000 | 0.0289 | - |
0.3253 | 3050 | 0.0326 | - |
0.3307 | 3100 | 0.0298 | - |
0.336 | 3150 | 0.028 | - |
0.3413 | 3200 | 0.0249 | - |
0.3467 | 3250 | 0.028 | - |
0.352 | 3300 | 0.024 | - |
0.3573 | 3350 | 0.0251 | - |
0.3627 | 3400 | 0.0255 | - |
0.368 | 3450 | 0.0231 | - |
0.3733 | 3500 | 0.0208 | - |
0.3787 | 3550 | 0.0275 | - |
0.384 | 3600 | 0.0223 | - |
0.3893 | 3650 | 0.018 | - |
0.3947 | 3700 | 0.0209 | - |
0.4 | 3750 | 0.0226 | - |
0.4053 | 3800 | 0.0199 | - |
0.4107 | 3850 | 0.0203 | - |
0.416 | 3900 | 0.0183 | - |
0.4213 | 3950 | 0.0265 | - |
0.4267 | 4000 | 0.0185 | - |
0.432 | 4050 | 0.0155 | - |
0.4373 | 4100 | 0.0177 | - |
0.4427 | 4150 | 0.0227 | - |
0.448 | 4200 | 0.0179 | - |
0.4533 | 4250 | 0.0203 | - |
0.4587 | 4300 | 0.0198 | - |
0.464 | 4350 | 0.0192 | - |
0.4693 | 4400 | 0.0218 | - |
0.4747 | 4450 | 0.019 | - |
0.48 | 4500 | 0.0203 | - |
0.4853 | 4550 | 0.0178 | - |
0.4907 | 4600 | 0.0196 | - |
0.496 | 4650 | 0.0209 | - |
0.5013 | 4700 | 0.0161 | - |
0.5067 | 4750 | 0.0169 | - |
0.512 | 4800 | 0.0205 | - |
0.5173 | 4850 | 0.0207 | - |
0.5227 | 4900 | 0.0178 | - |
0.528 | 4950 | 0.0144 | - |
0.5333 | 5000 | 0.0177 | - |
0.5387 | 5050 | 0.0178 | - |
0.544 | 5100 | 0.0127 | - |
0.5493 | 5150 | 0.019 | - |
0.5547 | 5200 | 0.017 | - |
0.56 | 5250 | 0.017 | - |
0.5653 | 5300 | 0.0137 | - |
0.5707 | 5350 | 0.0163 | - |
0.576 | 5400 | 0.0137 | - |
0.5813 | 5450 | 0.0148 | - |
0.5867 | 5500 | 0.0164 | - |
0.592 | 5550 | 0.0157 | - |
0.5973 | 5600 | 0.013 | - |
0.6027 | 5650 | 0.0141 | - |
0.608 | 5700 | 0.0144 | - |
0.6133 | 5750 | 0.0138 | - |
0.6187 | 5800 | 0.018 | - |
0.624 | 5850 | 0.0123 | - |
0.6293 | 5900 | 0.014 | - |
0.6347 | 5950 | 0.009 | - |
0.64 | 6000 | 0.01 | - |
0.6453 | 6050 | 0.0134 | - |
0.6507 | 6100 | 0.0161 | - |
0.656 | 6150 | 0.0133 | - |
0.6613 | 6200 | 0.0114 | - |
0.6667 | 6250 | 0.0138 | - |
0.672 | 6300 | 0.0149 | - |
0.6773 | 6350 | 0.0133 | - |
0.6827 | 6400 | 0.01 | - |
0.688 | 6450 | 0.0135 | - |
0.6933 | 6500 | 0.0123 | - |
0.6987 | 6550 | 0.0149 | - |
0.704 | 6600 | 0.0139 | - |
0.7093 | 6650 | 0.0117 | - |
0.7147 | 6700 | 0.013 | - |
0.72 | 6750 | 0.0129 | - |
0.7253 | 6800 | 0.0096 | - |
0.7307 | 6850 | 0.012 | - |
0.736 | 6900 | 0.0093 | - |
0.7413 | 6950 | 0.017 | - |
0.7467 | 7000 | 0.0121 | - |
0.752 | 7050 | 0.0106 | - |
0.7573 | 7100 | 0.0154 | - |
0.7627 | 7150 | 0.01 | - |
0.768 | 7200 | 0.011 | - |
0.7733 | 7250 | 0.0126 | - |
0.7787 | 7300 | 0.0085 | - |
0.784 | 7350 | 0.011 | - |
0.7893 | 7400 | 0.0114 | - |
0.7947 | 7450 | 0.0131 | - |
0.8 | 7500 | 0.0109 | - |
0.8053 | 7550 | 0.0097 | - |
0.8107 | 7600 | 0.011 | - |
0.816 | 7650 | 0.0105 | - |
0.8213 | 7700 | 0.0124 | - |
0.8267 | 7750 | 0.0089 | - |
0.832 | 7800 | 0.0102 | - |
0.8373 | 7850 | 0.012 | - |
0.8427 | 7900 | 0.0079 | - |
0.848 | 7950 | 0.0097 | - |
0.8533 | 8000 | 0.0087 | - |
0.8587 | 8050 | 0.007 | - |
0.864 | 8100 | 0.0119 | - |
0.8693 | 8150 | 0.0085 | - |
0.8747 | 8200 | 0.0114 | - |
0.88 | 8250 | 0.0107 | - |
0.8853 | 8300 | 0.0106 | - |
0.8907 | 8350 | 0.0106 | - |
0.896 | 8400 | 0.0074 | - |
0.9013 | 8450 | 0.0098 | - |
0.9067 | 8500 | 0.01 | - |
0.912 | 8550 | 0.0066 | - |
0.9173 | 8600 | 0.0079 | - |
0.9227 | 8650 | 0.0111 | - |
0.928 | 8700 | 0.012 | - |
0.9333 | 8750 | 0.0127 | - |
0.9387 | 8800 | 0.0043 | - |
0.944 | 8850 | 0.0108 | - |
0.9493 | 8900 | 0.0047 | - |
0.9547 | 8950 | 0.0066 | - |
0.96 | 9000 | 0.0077 | - |
0.9653 | 9050 | 0.0104 | - |
0.9707 | 9100 | 0.0085 | - |
0.976 | 9150 | 0.0098 | - |
0.9813 | 9200 | 0.0077 | - |
0.9867 | 9250 | 0.0097 | - |
0.992 | 9300 | 0.0083 | - |
0.9973 | 9350 | 0.006 | - |
1.0027 | 9400 | 0.0076 | - |
1.008 | 9450 | 0.0067 | - |
1.0133 | 9500 | 0.0081 | - |
1.0187 | 9550 | 0.0082 | - |
1.024 | 9600 | 0.0061 | - |
1.0293 | 9650 | 0.0064 | - |
1.0347 | 9700 | 0.0065 | - |
1.04 | 9750 | 0.0059 | - |
1.0453 | 9800 | 0.009 | - |
1.0507 | 9850 | 0.0079 | - |
1.056 | 9900 | 0.0058 | - |
1.0613 | 9950 | 0.0053 | - |
1.0667 | 10000 | 0.0046 | - |
1.072 | 10050 | 0.0075 | - |
1.0773 | 10100 | 0.0044 | - |
1.0827 | 10150 | 0.0049 | - |
1.088 | 10200 | 0.0097 | - |
1.0933 | 10250 | 0.0059 | - |
1.0987 | 10300 | 0.0074 | - |
1.104 | 10350 | 0.0078 | - |
1.1093 | 10400 | 0.0083 | - |
1.1147 | 10450 | 0.0049 | - |
1.12 | 10500 | 0.0083 | - |
1.1253 | 10550 | 0.0064 | - |
1.1307 | 10600 | 0.0065 | - |
1.1360 | 10650 | 0.0054 | - |
1.1413 | 10700 | 0.0102 | - |
1.1467 | 10750 | 0.0054 | - |
1.152 | 10800 | 0.0067 | - |
1.1573 | 10850 | 0.0082 | - |
1.1627 | 10900 | 0.0063 | - |
1.168 | 10950 | 0.0097 | - |
1.1733 | 11000 | 0.0067 | - |
1.1787 | 11050 | 0.0083 | - |
1.184 | 11100 | 0.0037 | - |
1.1893 | 11150 | 0.0051 | - |
1.1947 | 11200 | 0.0085 | - |
1.2 | 11250 | 0.0072 | - |
1.2053 | 11300 | 0.0097 | - |
1.2107 | 11350 | 0.0072 | - |
1.216 | 11400 | 0.0059 | - |
1.2213 | 11450 | 0.0063 | - |
1.2267 | 11500 | 0.0054 | - |
1.232 | 11550 | 0.0075 | - |
1.2373 | 11600 | 0.0091 | - |
1.2427 | 11650 | 0.0059 | - |
1.248 | 11700 | 0.0066 | - |
1.2533 | 11750 | 0.0066 | - |
1.2587 | 11800 | 0.007 | - |
1.264 | 11850 | 0.0054 | - |
1.2693 | 11900 | 0.0077 | - |
1.2747 | 11950 | 0.009 | - |
1.28 | 12000 | 0.0069 | - |
1.2853 | 12050 | 0.0088 | - |
1.2907 | 12100 | 0.005 | - |
1.296 | 12150 | 0.0086 | - |
1.3013 | 12200 | 0.0063 | - |
1.3067 | 12250 | 0.0048 | - |
1.312 | 12300 | 0.0055 | - |
1.3173 | 12350 | 0.0042 | - |
1.3227 | 12400 | 0.0051 | - |
1.328 | 12450 | 0.0066 | - |
1.3333 | 12500 | 0.01 | - |
1.3387 | 12550 | 0.0049 | - |
1.3440 | 12600 | 0.0045 | - |
1.3493 | 12650 | 0.0054 | - |
1.3547 | 12700 | 0.0041 | - |
1.3600 | 12750 | 0.0051 | - |
1.3653 | 12800 | 0.0037 | - |
1.3707 | 12850 | 0.0053 | - |
1.376 | 12900 | 0.0049 | - |
1.3813 | 12950 | 0.0053 | - |
1.3867 | 13000 | 0.0027 | - |
1.392 | 13050 | 0.0034 | - |
1.3973 | 13100 | 0.0062 | - |
1.4027 | 13150 | 0.0058 | - |
1.408 | 13200 | 0.0068 | - |
1.4133 | 13250 | 0.0053 | - |
1.4187 | 13300 | 0.0074 | - |
1.424 | 13350 | 0.0053 | - |
1.4293 | 13400 | 0.0038 | - |
1.4347 | 13450 | 0.0049 | - |
1.44 | 13500 | 0.0041 | - |
1.4453 | 13550 | 0.0057 | - |
1.4507 | 13600 | 0.0041 | - |
1.456 | 13650 | 0.0059 | - |
1.4613 | 13700 | 0.0063 | - |
1.4667 | 13750 | 0.0047 | - |
1.472 | 13800 | 0.0077 | - |
1.4773 | 13850 | 0.0072 | - |
1.4827 | 13900 | 0.0029 | - |
1.488 | 13950 | 0.0029 | - |
1.4933 | 14000 | 0.0081 | - |
1.4987 | 14050 | 0.0046 | - |
1.504 | 14100 | 0.0057 | - |
1.5093 | 14150 | 0.0077 | - |
1.5147 | 14200 | 0.0049 | - |
1.52 | 14250 | 0.0098 | - |
1.5253 | 14300 | 0.007 | - |
1.5307 | 14350 | 0.0066 | - |
1.536 | 14400 | 0.0045 | - |
1.5413 | 14450 | 0.0044 | - |
1.5467 | 14500 | 0.0033 | - |
1.552 | 14550 | 0.0063 | - |
1.5573 | 14600 | 0.008 | - |
1.5627 | 14650 | 0.0028 | - |
1.568 | 14700 | 0.0067 | - |
1.5733 | 14750 | 0.0086 | - |
1.5787 | 14800 | 0.0062 | - |
1.584 | 14850 | 0.0037 | - |
1.5893 | 14900 | 0.0046 | - |
1.5947 | 14950 | 0.0088 | - |
1.6 | 15000 | 0.0064 | - |
1.6053 | 15050 | 0.0031 | - |
1.6107 | 15100 | 0.0057 | - |
1.616 | 15150 | 0.0038 | - |
1.6213 | 15200 | 0.0102 | - |
1.6267 | 15250 | 0.0045 | - |
1.6320 | 15300 | 0.0049 | - |
1.6373 | 15350 | 0.0044 | - |
1.6427 | 15400 | 0.0074 | - |
1.6480 | 15450 | 0.0042 | - |
1.6533 | 15500 | 0.0088 | - |
1.6587 | 15550 | 0.01 | - |
1.6640 | 15600 | 0.004 | - |
1.6693 | 15650 | 0.0034 | - |
1.6747 | 15700 | 0.0051 | - |
1.6800 | 15750 | 0.0068 | - |
1.6853 | 15800 | 0.0016 | - |
1.6907 | 15850 | 0.0076 | - |
1.696 | 15900 | 0.0049 | - |
1.7013 | 15950 | 0.0083 | - |
1.7067 | 16000 | 0.0025 | - |
1.712 | 16050 | 0.0029 | - |
1.7173 | 16100 | 0.004 | - |
1.7227 | 16150 | 0.0043 | - |
1.728 | 16200 | 0.0049 | - |
1.7333 | 16250 | 0.0057 | - |
1.7387 | 16300 | 0.0086 | - |
1.744 | 16350 | 0.0052 | - |
1.7493 | 16400 | 0.0045 | - |
1.7547 | 16450 | 0.0043 | - |
1.76 | 16500 | 0.0051 | - |
1.7653 | 16550 | 0.0065 | - |
1.7707 | 16600 | 0.0042 | - |
1.776 | 16650 | 0.0044 | - |
1.7813 | 16700 | 0.0036 | - |
1.7867 | 16750 | 0.0044 | - |
1.792 | 16800 | 0.0035 | - |
1.7973 | 16850 | 0.0042 | - |
1.8027 | 16900 | 0.0059 | - |
1.808 | 16950 | 0.0043 | - |
1.8133 | 17000 | 0.0055 | - |
1.8187 | 17050 | 0.0065 | - |
1.8240 | 17100 | 0.0064 | - |
1.8293 | 17150 | 0.0044 | - |
1.8347 | 17200 | 0.0051 | - |
1.8400 | 17250 | 0.0046 | - |
1.8453 | 17300 | 0.0051 | - |
1.8507 | 17350 | 0.006 | - |
1.8560 | 17400 | 0.0057 | - |
1.8613 | 17450 | 0.0038 | - |
1.8667 | 17500 | 0.0065 | - |
1.8720 | 17550 | 0.0052 | - |
1.8773 | 17600 | 0.0028 | - |
1.8827 | 17650 | 0.0064 | - |
1.888 | 17700 | 0.0049 | - |
1.8933 | 17750 | 0.0071 | - |
1.8987 | 17800 | 0.0058 | - |
1.904 | 17850 | 0.0022 | - |
1.9093 | 17900 | 0.0042 | - |
1.9147 | 17950 | 0.003 | - |
1.92 | 18000 | 0.0069 | - |
1.9253 | 18050 | 0.0053 | - |
1.9307 | 18100 | 0.0052 | - |
1.936 | 18150 | 0.002 | - |
1.9413 | 18200 | 0.0068 | - |
1.9467 | 18250 | 0.0056 | - |
1.952 | 18300 | 0.0059 | - |
1.9573 | 18350 | 0.0018 | - |
1.9627 | 18400 | 0.0055 | - |
1.968 | 18450 | 0.0067 | - |
1.9733 | 18500 | 0.0025 | - |
1.9787 | 18550 | 0.0044 | - |
1.984 | 18600 | 0.0024 | - |
1.9893 | 18650 | 0.0064 | - |
1.9947 | 18700 | 0.0029 | - |
2.0 | 18750 | 0.0052 | - |
2.0053 | 18800 | 0.0035 | - |
2.0107 | 18850 | 0.0056 | - |
2.016 | 18900 | 0.0028 | - |
2.0213 | 18950 | 0.0042 | - |
2.0267 | 19000 | 0.0012 | - |
2.032 | 19050 | 0.0027 | - |
2.0373 | 19100 | 0.0042 | - |
2.0427 | 19150 | 0.0074 | - |
2.048 | 19200 | 0.0028 | - |
2.0533 | 19250 | 0.0035 | - |
2.0587 | 19300 | 0.0039 | - |
2.064 | 19350 | 0.0047 | - |
2.0693 | 19400 | 0.0022 | - |
2.0747 | 19450 | 0.0034 | - |
2.08 | 19500 | 0.0036 | - |
2.0853 | 19550 | 0.0061 | - |
2.0907 | 19600 | 0.004 | - |
2.096 | 19650 | 0.003 | - |
2.1013 | 19700 | 0.0049 | - |
2.1067 | 19750 | 0.0034 | - |
2.112 | 19800 | 0.0013 | - |
2.1173 | 19850 | 0.0033 | - |
2.1227 | 19900 | 0.0032 | - |
2.128 | 19950 | 0.0041 | - |
2.1333 | 20000 | 0.0015 | - |
2.1387 | 20050 | 0.0013 | - |
2.144 | 20100 | 0.0051 | - |
2.1493 | 20150 | 0.0026 | - |
2.1547 | 20200 | 0.0037 | - |
2.16 | 20250 | 0.0013 | - |
2.1653 | 20300 | 0.0048 | - |
2.1707 | 20350 | 0.005 | - |
2.176 | 20400 | 0.0032 | - |
2.1813 | 20450 | 0.0064 | - |
2.1867 | 20500 | 0.0047 | - |
2.192 | 20550 | 0.0074 | - |
2.1973 | 20600 | 0.0067 | - |
2.2027 | 20650 | 0.0047 | - |
2.208 | 20700 | 0.0013 | - |
2.2133 | 20750 | 0.0067 | - |
2.2187 | 20800 | 0.006 | - |
2.224 | 20850 | 0.0012 | - |
2.2293 | 20900 | 0.0045 | - |
2.2347 | 20950 | 0.0029 | - |
2.24 | 21000 | 0.0042 | - |
2.2453 | 21050 | 0.0047 | - |
2.2507 | 21100 | 0.0027 | - |
2.2560 | 21150 | 0.0029 | - |
2.2613 | 21200 | 0.006 | - |
2.2667 | 21250 | 0.0037 | - |
2.2720 | 21300 | 0.0027 | - |
2.2773 | 21350 | 0.003 | - |
2.2827 | 21400 | 0.0035 | - |
2.288 | 21450 | 0.0028 | - |
2.2933 | 21500 | 0.004 | - |
2.2987 | 21550 | 0.0025 | - |
2.304 | 21600 | 0.0028 | - |
2.3093 | 21650 | 0.0044 | - |
2.3147 | 21700 | 0.0051 | - |
2.32 | 21750 | 0.0041 | - |
2.3253 | 21800 | 0.0019 | - |
2.3307 | 21850 | 0.0043 | - |
2.336 | 21900 | 0.0015 | - |
2.3413 | 21950 | 0.0058 | - |
2.3467 | 22000 | 0.0026 | - |
2.352 | 22050 | 0.0027 | - |
2.3573 | 22100 | 0.0011 | - |
2.3627 | 22150 | 0.0038 | - |
2.368 | 22200 | 0.0025 | - |
2.3733 | 22250 | 0.0042 | - |
2.3787 | 22300 | 0.0049 | - |
2.384 | 22350 | 0.0022 | - |
2.3893 | 22400 | 0.0031 | - |
2.3947 | 22450 | 0.0026 | - |
2.4 | 22500 | 0.0009 | - |
2.4053 | 22550 | 0.0037 | - |
2.4107 | 22600 | 0.003 | - |
2.416 | 22650 | 0.0099 | - |
2.4213 | 22700 | 0.0052 | - |
2.4267 | 22750 | 0.0017 | - |
2.432 | 22800 | 0.0039 | - |
2.4373 | 22850 | 0.0036 | - |
2.4427 | 22900 | 0.0053 | - |
2.448 | 22950 | 0.0057 | - |
2.4533 | 23000 | 0.0029 | - |
2.4587 | 23050 | 0.0011 | - |
2.464 | 23100 | 0.003 | - |
2.4693 | 23150 | 0.0022 | - |
2.4747 | 23200 | 0.0025 | - |
2.48 | 23250 | 0.0018 | - |
2.4853 | 23300 | 0.0011 | - |
2.4907 | 23350 | 0.0036 | - |
2.496 | 23400 | 0.0028 | - |
2.5013 | 23450 | 0.0022 | - |
2.5067 | 23500 | 0.0011 | - |
2.512 | 23550 | 0.0044 | - |
2.5173 | 23600 | 0.0052 | - |
2.5227 | 23650 | 0.0039 | - |
2.528 | 23700 | 0.0061 | - |
2.5333 | 23750 | 0.0022 | - |
2.5387 | 23800 | 0.0039 | - |
2.544 | 23850 | 0.0036 | - |
2.5493 | 23900 | 0.0033 | - |
2.5547 | 23950 | 0.0017 | - |
2.56 | 24000 | 0.0009 | - |
2.5653 | 24050 | 0.0018 | - |
2.5707 | 24100 | 0.0024 | - |
2.576 | 24150 | 0.0045 | - |
2.5813 | 24200 | 0.0011 | - |
2.5867 | 24250 | 0.0024 | - |
2.592 | 24300 | 0.0027 | - |
2.5973 | 24350 | 0.001 | - |
2.6027 | 24400 | 0.0026 | - |
2.608 | 24450 | 0.0022 | - |
2.6133 | 24500 | 0.0036 | - |
2.6187 | 24550 | 0.0036 | - |
2.624 | 24600 | 0.0017 | - |
2.6293 | 24650 | 0.0043 | - |
2.6347 | 24700 | 0.0031 | - |
2.64 | 24750 | 0.0035 | - |
2.6453 | 24800 | 0.0031 | - |
2.6507 | 24850 | 0.0021 | - |
2.656 | 24900 | 0.0018 | - |
2.6613 | 24950 | 0.0021 | - |
2.6667 | 25000 | 0.0039 | - |
2.672 | 25050 | 0.0013 | - |
2.6773 | 25100 | 0.0024 | - |
2.6827 | 25150 | 0.0036 | - |
2.6880 | 25200 | 0.0023 | - |
2.6933 | 25250 | 0.0042 | - |
2.6987 | 25300 | 0.002 | - |
2.7040 | 25350 | 0.0058 | - |
2.7093 | 25400 | 0.0022 | - |
2.7147 | 25450 | 0.0034 | - |
2.7200 | 25500 | 0.0021 | - |
2.7253 | 25550 | 0.0049 | - |
2.7307 | 25600 | 0.0009 | - |
2.7360 | 25650 | 0.0069 | - |
2.7413 | 25700 | 0.0039 | - |
2.7467 | 25750 | 0.0024 | - |
2.752 | 25800 | 0.0031 | - |
2.7573 | 25850 | 0.0067 | - |
2.7627 | 25900 | 0.005 | - |
2.768 | 25950 | 0.0037 | - |
2.7733 | 26000 | 0.0045 | - |
2.7787 | 26050 | 0.0043 | - |
2.784 | 26100 | 0.004 | - |
2.7893 | 26150 | 0.0052 | - |
2.7947 | 26200 | 0.0008 | - |
2.8 | 26250 | 0.002 | - |
2.8053 | 26300 | 0.001 | - |
2.8107 | 26350 | 0.0034 | - |
2.816 | 26400 | 0.0031 | - |
2.8213 | 26450 | 0.0019 | - |
2.8267 | 26500 | 0.0008 | - |
2.832 | 26550 | 0.0057 | - |
2.8373 | 26600 | 0.0027 | - |
2.8427 | 26650 | 0.0008 | - |
2.848 | 26700 | 0.0022 | - |
2.8533 | 26750 | 0.0032 | - |
2.8587 | 26800 | 0.0029 | - |
2.864 | 26850 | 0.0009 | - |
2.8693 | 26900 | 0.0022 | - |
2.8747 | 26950 | 0.0023 | - |
2.88 | 27000 | 0.0019 | - |
2.8853 | 27050 | 0.0031 | - |
2.8907 | 27100 | 0.002 | - |
2.896 | 27150 | 0.0021 | - |
2.9013 | 27200 | 0.0029 | - |
2.9067 | 27250 | 0.0006 | - |
2.912 | 27300 | 0.0033 | - |
2.9173 | 27350 | 0.0031 | - |
2.9227 | 27400 | 0.0012 | - |
2.928 | 27450 | 0.0009 | - |
2.9333 | 27500 | 0.0021 | - |
2.9387 | 27550 | 0.0014 | - |
2.944 | 27600 | 0.0063 | - |
2.9493 | 27650 | 0.0029 | - |
2.9547 | 27700 | 0.0027 | - |
2.96 | 27750 | 0.0028 | - |
2.9653 | 27800 | 0.0018 | - |
2.9707 | 27850 | 0.0028 | - |
2.976 | 27900 | 0.0019 | - |
2.9813 | 27950 | 0.0027 | - |
2.9867 | 28000 | 0.0024 | - |
2.992 | 28050 | 0.0007 | - |
2.9973 | 28100 | 0.0009 | - |
3.0027 | 28150 | 0.0033 | - |
3.008 | 28200 | 0.0033 | - |
3.0133 | 28250 | 0.0032 | - |
3.0187 | 28300 | 0.0023 | - |
3.024 | 28350 | 0.0038 | - |
3.0293 | 28400 | 0.0011 | - |
3.0347 | 28450 | 0.0035 | - |
3.04 | 28500 | 0.0047 | - |
3.0453 | 28550 | 0.0007 | - |
3.0507 | 28600 | 0.004 | - |
3.056 | 28650 | 0.0019 | - |
3.0613 | 28700 | 0.0052 | - |
3.0667 | 28750 | 0.0022 | - |
3.072 | 28800 | 0.0013 | - |
3.0773 | 28850 | 0.0012 | - |
3.0827 | 28900 | 0.0031 | - |
3.088 | 28950 | 0.002 | - |
3.0933 | 29000 | 0.0018 | - |
3.0987 | 29050 | 0.0019 | - |
3.104 | 29100 | 0.0046 | - |
3.1093 | 29150 | 0.001 | - |
3.1147 | 29200 | 0.0017 | - |
3.12 | 29250 | 0.0008 | - |
3.1253 | 29300 | 0.0006 | - |
3.1307 | 29350 | 0.0024 | - |
3.136 | 29400 | 0.0016 | - |
3.1413 | 29450 | 0.0028 | - |
3.1467 | 29500 | 0.0033 | - |
3.152 | 29550 | 0.0025 | - |
3.1573 | 29600 | 0.0008 | - |
3.1627 | 29650 | 0.003 | - |
3.168 | 29700 | 0.0007 | - |
3.1733 | 29750 | 0.0007 | - |
3.1787 | 29800 | 0.0018 | - |
3.184 | 29850 | 0.0081 | - |
3.1893 | 29900 | 0.0021 | - |
3.1947 | 29950 | 0.0011 | - |
3.2 | 30000 | 0.0005 | - |
3.2053 | 30050 | 0.0039 | - |
3.2107 | 30100 | 0.0042 | - |
3.216 | 30150 | 0.0007 | - |
3.2213 | 30200 | 0.0065 | - |
3.2267 | 30250 | 0.003 | - |
3.232 | 30300 | 0.0045 | - |
3.2373 | 30350 | 0.0008 | - |
3.2427 | 30400 | 0.0029 | - |
3.248 | 30450 | 0.0008 | - |
3.2533 | 30500 | 0.0026 | - |
3.2587 | 30550 | 0.0048 | - |
3.2640 | 30600 | 0.0035 | - |
3.2693 | 30650 | 0.0024 | - |
3.2747 | 30700 | 0.0042 | - |
3.2800 | 30750 | 0.0005 | - |
3.2853 | 30800 | 0.0048 | - |
3.2907 | 30850 | 0.0007 | - |
3.296 | 30900 | 0.0029 | - |
3.3013 | 30950 | 0.0014 | - |
3.3067 | 31000 | 0.0032 | - |
3.312 | 31050 | 0.0019 | - |
3.3173 | 31100 | 0.0014 | - |
3.3227 | 31150 | 0.0006 | - |
3.328 | 31200 | 0.0018 | - |
3.3333 | 31250 | 0.0044 | - |
3.3387 | 31300 | 0.0042 | - |
3.344 | 31350 | 0.0021 | - |
3.3493 | 31400 | 0.0051 | - |
3.3547 | 31450 | 0.0029 | - |
3.36 | 31500 | 0.0015 | - |
3.3653 | 31550 | 0.003 | - |
3.3707 | 31600 | 0.0048 | - |
3.376 | 31650 | 0.0018 | - |
3.3813 | 31700 | 0.0025 | - |
3.3867 | 31750 | 0.0013 | - |
3.392 | 31800 | 0.0012 | - |
3.3973 | 31850 | 0.0016 | - |
3.4027 | 31900 | 0.0028 | - |
3.408 | 31950 | 0.0019 | - |
3.4133 | 32000 | 0.0025 | - |
3.4187 | 32050 | 0.0019 | - |
3.424 | 32100 | 0.0008 | - |
3.4293 | 32150 | 0.0021 | - |
3.4347 | 32200 | 0.0017 | - |
3.44 | 32250 | 0.0027 | - |
3.4453 | 32300 | 0.0018 | - |
3.4507 | 32350 | 0.002 | - |
3.456 | 32400 | 0.0009 | - |
3.4613 | 32450 | 0.0015 | - |
3.4667 | 32500 | 0.0008 | - |
3.472 | 32550 | 0.0016 | - |
3.4773 | 32600 | 0.0014 | - |
3.4827 | 32650 | 0.0005 | - |
3.488 | 32700 | 0.0041 | - |
3.4933 | 32750 | 0.004 | - |
3.4987 | 32800 | 0.0005 | - |
3.504 | 32850 | 0.0026 | - |
3.5093 | 32900 | 0.0028 | - |
3.5147 | 32950 | 0.0022 | - |
3.52 | 33000 | 0.0038 | - |
3.5253 | 33050 | 0.004 | - |
3.5307 | 33100 | 0.0005 | - |
3.536 | 33150 | 0.001 | - |
3.5413 | 33200 | 0.0038 | - |
3.5467 | 33250 | 0.0011 | - |
3.552 | 33300 | 0.0006 | - |
3.5573 | 33350 | 0.0018 | - |
3.5627 | 33400 | 0.0037 | - |
3.568 | 33450 | 0.0032 | - |
3.5733 | 33500 | 0.002 | - |
3.5787 | 33550 | 0.0017 | - |
3.584 | 33600 | 0.0018 | - |
3.5893 | 33650 | 0.0039 | - |
3.5947 | 33700 | 0.0029 | - |
3.6 | 33750 | 0.0038 | - |
3.6053 | 33800 | 0.0031 | - |
3.6107 | 33850 | 0.0027 | - |
3.616 | 33900 | 0.0026 | - |
3.6213 | 33950 | 0.0023 | - |
3.6267 | 34000 | 0.0073 | - |
3.632 | 34050 | 0.0039 | - |
3.6373 | 34100 | 0.0006 | - |
3.6427 | 34150 | 0.0007 | - |
3.648 | 34200 | 0.0016 | - |
3.6533 | 34250 | 0.0025 | - |
3.6587 | 34300 | 0.0005 | - |
3.664 | 34350 | 0.004 | - |
3.6693 | 34400 | 0.0015 | - |
3.6747 | 34450 | 0.0008 | - |
3.68 | 34500 | 0.0017 | - |
3.6853 | 34550 | 0.0035 | - |
3.6907 | 34600 | 0.0008 | - |
3.6960 | 34650 | 0.0014 | - |
3.7013 | 34700 | 0.0026 | - |
3.7067 | 34750 | 0.0005 | - |
3.7120 | 34800 | 0.0027 | - |
3.7173 | 34850 | 0.0006 | - |
3.7227 | 34900 | 0.0005 | - |
3.7280 | 34950 | 0.0012 | - |
3.7333 | 35000 | 0.0018 | - |
3.7387 | 35050 | 0.0015 | - |
3.7440 | 35100 | 0.0017 | - |
3.7493 | 35150 | 0.0009 | - |
3.7547 | 35200 | 0.0016 | - |
3.76 | 35250 | 0.0027 | - |
3.7653 | 35300 | 0.0039 | - |
3.7707 | 35350 | 0.0016 | - |
3.776 | 35400 | 0.002 | - |
3.7813 | 35450 | 0.0015 | - |
3.7867 | 35500 | 0.0015 | - |
3.792 | 35550 | 0.0006 | - |
3.7973 | 35600 | 0.0029 | - |
3.8027 | 35650 | 0.0028 | - |
3.808 | 35700 | 0.0005 | - |
3.8133 | 35750 | 0.0026 | - |
3.8187 | 35800 | 0.0005 | - |
3.824 | 35850 | 0.0004 | - |
3.8293 | 35900 | 0.0015 | - |
3.8347 | 35950 | 0.0037 | - |
3.84 | 36000 | 0.0005 | - |
3.8453 | 36050 | 0.0015 | - |
3.8507 | 36100 | 0.0009 | - |
3.856 | 36150 | 0.0019 | - |
3.8613 | 36200 | 0.0026 | - |
3.8667 | 36250 | 0.0018 | - |
3.872 | 36300 | 0.0005 | - |
3.8773 | 36350 | 0.0027 | - |
3.8827 | 36400 | 0.003 | - |
3.888 | 36450 | 0.0018 | - |
3.8933 | 36500 | 0.0027 | - |
3.8987 | 36550 | 0.0038 | - |
3.904 | 36600 | 0.0005 | - |
3.9093 | 36650 | 0.0026 | - |
3.9147 | 36700 | 0.0024 | - |
3.92 | 36750 | 0.0013 | - |
3.9253 | 36800 | 0.0035 | - |
3.9307 | 36850 | 0.0018 | - |
3.936 | 36900 | 0.0007 | - |
3.9413 | 36950 | 0.0007 | - |
3.9467 | 37000 | 0.0043 | - |
3.952 | 37050 | 0.0017 | - |
3.9573 | 37100 | 0.0036 | - |
3.9627 | 37150 | 0.0018 | - |
3.968 | 37200 | 0.0012 | - |
3.9733 | 37250 | 0.0024 | - |
3.9787 | 37300 | 0.0005 | - |
3.984 | 37350 | 0.005 | - |
3.9893 | 37400 | 0.002 | - |
3.9947 | 37450 | 0.0031 | - |
4.0 | 37500 | 0.0005 | - |
4.0053 | 37550 | 0.0015 | - |
4.0107 | 37600 | 0.0015 | - |
4.016 | 37650 | 0.0017 | - |
4.0213 | 37700 | 0.0012 | - |
4.0267 | 37750 | 0.0027 | - |
4.032 | 37800 | 0.0013 | - |
4.0373 | 37850 | 0.0016 | - |
4.0427 | 37900 | 0.0016 | - |
4.048 | 37950 | 0.0038 | - |
4.0533 | 38000 | 0.0016 | - |
4.0587 | 38050 | 0.0014 | - |
4.064 | 38100 | 0.0005 | - |
4.0693 | 38150 | 0.0005 | - |
4.0747 | 38200 | 0.0039 | - |
4.08 | 38250 | 0.0014 | - |
4.0853 | 38300 | 0.0007 | - |
4.0907 | 38350 | 0.0009 | - |
4.096 | 38400 | 0.0024 | - |
4.1013 | 38450 | 0.0024 | - |
4.1067 | 38500 | 0.0005 | - |
4.112 | 38550 | 0.0016 | - |
4.1173 | 38600 | 0.0004 | - |
4.1227 | 38650 | 0.0009 | - |
4.128 | 38700 | 0.0005 | - |
4.1333 | 38750 | 0.0009 | - |
4.1387 | 38800 | 0.0027 | - |
4.144 | 38850 | 0.003 | - |
4.1493 | 38900 | 0.0036 | - |
4.1547 | 38950 | 0.0008 | - |
4.16 | 39000 | 0.0016 | - |
4.1653 | 39050 | 0.001 | - |
4.1707 | 39100 | 0.0005 | - |
4.176 | 39150 | 0.0013 | - |
4.1813 | 39200 | 0.0005 | - |
4.1867 | 39250 | 0.0011 | - |
4.192 | 39300 | 0.0005 | - |
4.1973 | 39350 | 0.005 | - |
4.2027 | 39400 | 0.0028 | - |
4.208 | 39450 | 0.0027 | - |
4.2133 | 39500 | 0.0029 | - |
4.2187 | 39550 | 0.0016 | - |
4.224 | 39600 | 0.0019 | - |
4.2293 | 39650 | 0.0014 | - |
4.2347 | 39700 | 0.0017 | - |
4.24 | 39750 | 0.0005 | - |
4.2453 | 39800 | 0.0015 | - |
4.2507 | 39850 | 0.0025 | - |
4.256 | 39900 | 0.0017 | - |
4.2613 | 39950 | 0.0016 | - |
4.2667 | 40000 | 0.0024 | - |
4.272 | 40050 | 0.0009 | - |
4.2773 | 40100 | 0.0019 | - |
4.2827 | 40150 | 0.0008 | - |
4.288 | 40200 | 0.0026 | - |
4.2933 | 40250 | 0.0024 | - |
4.2987 | 40300 | 0.0009 | - |
4.304 | 40350 | 0.0006 | - |
4.3093 | 40400 | 0.0014 | - |
4.3147 | 40450 | 0.001 | - |
4.32 | 40500 | 0.0021 | - |
4.3253 | 40550 | 0.0016 | - |
4.3307 | 40600 | 0.0015 | - |
4.336 | 40650 | 0.0016 | - |
4.3413 | 40700 | 0.001 | - |
4.3467 | 40750 | 0.0013 | - |
4.352 | 40800 | 0.001 | - |
4.3573 | 40850 | 0.0003 | - |
4.3627 | 40900 | 0.0005 | - |
4.368 | 40950 | 0.0024 | - |
4.3733 | 41000 | 0.0012 | - |
4.3787 | 41050 | 0.0006 | - |
4.384 | 41100 | 0.0038 | - |
4.3893 | 41150 | 0.0014 | - |
4.3947 | 41200 | 0.0005 | - |
4.4 | 41250 | 0.0005 | - |
4.4053 | 41300 | 0.0016 | - |
4.4107 | 41350 | 0.0011 | - |
4.416 | 41400 | 0.0037 | - |
4.4213 | 41450 | 0.0005 | - |
4.4267 | 41500 | 0.0015 | - |
4.432 | 41550 | 0.0029 | - |
4.4373 | 41600 | 0.0026 | - |
4.4427 | 41650 | 0.0023 | - |
4.448 | 41700 | 0.0006 | - |
4.4533 | 41750 | 0.0014 | - |
4.4587 | 41800 | 0.0018 | - |
4.464 | 41850 | 0.0023 | - |
4.4693 | 41900 | 0.0018 | - |
4.4747 | 41950 | 0.0033 | - |
4.48 | 42000 | 0.0006 | - |
4.4853 | 42050 | 0.0035 | - |
4.4907 | 42100 | 0.0022 | - |
4.496 | 42150 | 0.0058 | - |
4.5013 | 42200 | 0.0047 | - |
4.5067 | 42250 | 0.0011 | - |
4.5120 | 42300 | 0.0027 | - |
4.5173 | 42350 | 0.0028 | - |
4.5227 | 42400 | 0.0004 | - |
4.5280 | 42450 | 0.0019 | - |
4.5333 | 42500 | 0.0015 | - |
4.5387 | 42550 | 0.0016 | - |
4.5440 | 42600 | 0.0013 | - |
4.5493 | 42650 | 0.0005 | - |
4.5547 | 42700 | 0.0019 | - |
4.5600 | 42750 | 0.0015 | - |
4.5653 | 42800 | 0.0016 | - |
4.5707 | 42850 | 0.0027 | - |
4.576 | 42900 | 0.0023 | - |
4.5813 | 42950 | 0.0005 | - |
4.5867 | 43000 | 0.0016 | - |
4.592 | 43050 | 0.0035 | - |
4.5973 | 43100 | 0.0012 | - |
4.6027 | 43150 | 0.0009 | - |
4.608 | 43200 | 0.0026 | - |
4.6133 | 43250 | 0.0021 | - |
4.6187 | 43300 | 0.0016 | - |
4.624 | 43350 | 0.0019 | - |
4.6293 | 43400 | 0.0014 | - |
4.6347 | 43450 | 0.0051 | - |
4.64 | 43500 | 0.0032 | - |
4.6453 | 43550 | 0.0014 | - |
4.6507 | 43600 | 0.0013 | - |
4.656 | 43650 | 0.0004 | - |
4.6613 | 43700 | 0.001 | - |
4.6667 | 43750 | 0.0005 | - |
4.672 | 43800 | 0.0012 | - |
4.6773 | 43850 | 0.0026 | - |
4.6827 | 43900 | 0.0005 | - |
4.688 | 43950 | 0.0011 | - |
4.6933 | 44000 | 0.0005 | - |
4.6987 | 44050 | 0.0022 | - |
4.704 | 44100 | 0.0016 | - |
4.7093 | 44150 | 0.0028 | - |
4.7147 | 44200 | 0.0013 | - |
4.72 | 44250 | 0.0008 | - |
4.7253 | 44300 | 0.0005 | - |
4.7307 | 44350 | 0.0018 | - |
4.736 | 44400 | 0.0014 | - |
4.7413 | 44450 | 0.0033 | - |
4.7467 | 44500 | 0.0025 | - |
4.752 | 44550 | 0.0016 | - |
4.7573 | 44600 | 0.0011 | - |
4.7627 | 44650 | 0.0004 | - |
4.768 | 44700 | 0.0017 | - |
4.7733 | 44750 | 0.004 | - |
4.7787 | 44800 | 0.0026 | - |
4.784 | 44850 | 0.0004 | - |
4.7893 | 44900 | 0.002 | - |
4.7947 | 44950 | 0.0023 | - |
4.8 | 45000 | 0.0015 | - |
4.8053 | 45050 | 0.0027 | - |
4.8107 | 45100 | 0.0004 | - |
4.816 | 45150 | 0.0015 | - |
4.8213 | 45200 | 0.0037 | - |
4.8267 | 45250 | 0.002 | - |
4.832 | 45300 | 0.0026 | - |
4.8373 | 45350 | 0.0028 | - |
4.8427 | 45400 | 0.0016 | - |
4.848 | 45450 | 0.0024 | - |
4.8533 | 45500 | 0.0031 | - |
4.8587 | 45550 | 0.0012 | - |
4.864 | 45600 | 0.0005 | - |
4.8693 | 45650 | 0.0026 | - |
4.8747 | 45700 | 0.0009 | - |
4.88 | 45750 | 0.002 | - |
4.8853 | 45800 | 0.0008 | - |
4.8907 | 45850 | 0.0039 | - |
4.896 | 45900 | 0.0004 | - |
4.9013 | 45950 | 0.0023 | - |
4.9067 | 46000 | 0.0032 | - |
4.912 | 46050 | 0.0037 | - |
4.9173 | 46100 | 0.0005 | - |
4.9227 | 46150 | 0.0022 | - |
4.928 | 46200 | 0.0024 | - |
4.9333 | 46250 | 0.0006 | - |
4.9387 | 46300 | 0.0015 | - |
4.944 | 46350 | 0.0033 | - |
4.9493 | 46400 | 0.0018 | - |
4.9547 | 46450 | 0.0023 | - |
4.96 | 46500 | 0.0041 | - |
4.9653 | 46550 | 0.0011 | - |
4.9707 | 46600 | 0.0013 | - |
4.976 | 46650 | 0.0005 | - |
4.9813 | 46700 | 0.0015 | - |
4.9867 | 46750 | 0.0005 | - |
4.992 | 46800 | 0.0008 | - |
4.9973 | 46850 | 0.0011 | - |
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: 0.21.1
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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