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Pyro-X2
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polish_wikipedia_model

Text Generation
Transformers
TensorBoard
Safetensors
gpt_neox
Generated from Trainer
text-generation-inference
Inference Endpoints
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  1. README.md +304 -34
  2. model.safetensors +1 -1
  3. runs/Jun03_12-28-05_c4a222934390/events.out.tfevents.1717417685.c4a222934390.167.5 +3 -0
  4. runs/Jun03_12-28-19_c4a222934390/events.out.tfevents.1717417701.c4a222934390.167.6 +3 -0
  5. runs/Jun03_12-29-28_c4a222934390/events.out.tfevents.1717417771.c4a222934390.167.7 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -1,8 +1,8 @@
1
  ---
2
  license: apache-2.0
 
3
  tags:
4
  - generated_from_trainer
5
- base_model: EleutherAI/pythia-70m
6
  model-index:
7
  - name: polish_wikipedia_model
8
  results: []
@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
15
 
16
  This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
- - Loss: 0.0319
19
 
20
  ## Model description
21
 
@@ -34,48 +34,318 @@ More information needed
34
  ### Training hyperparameters
35
 
36
  The following hyperparameters were used during training:
37
- - learning_rate: 0.0002
38
  - train_batch_size: 8
39
  - eval_batch_size: 8
40
  - seed: 42
41
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
42
  - lr_scheduler_type: linear
43
- - num_epochs: 30
44
 
45
  ### Training results
46
 
47
  | Training Loss | Epoch | Step | Validation Loss |
48
  |:-------------:|:-----:|:----:|:---------------:|
49
- | No log | 1.0 | 133 | 0.4322 |
50
- | No log | 2.0 | 266 | 0.3766 |
51
- | No log | 3.0 | 399 | 0.3599 |
52
- | 0.4669 | 4.0 | 532 | 0.3247 |
53
- | 0.4669 | 5.0 | 665 | 0.2830 |
54
- | 0.4669 | 6.0 | 798 | 0.2628 |
55
- | 0.4669 | 7.0 | 931 | 0.2573 |
56
- | 0.3481 | 8.0 | 1064 | 0.2443 |
57
- | 0.3481 | 9.0 | 1197 | 0.1904 |
58
- | 0.3481 | 10.0 | 1330 | 0.1799 |
59
- | 0.3481 | 11.0 | 1463 | 0.1475 |
60
- | 0.2502 | 12.0 | 1596 | 0.1292 |
61
- | 0.2502 | 13.0 | 1729 | 0.1168 |
62
- | 0.2502 | 14.0 | 1862 | 0.1103 |
63
- | 0.2502 | 15.0 | 1995 | 0.0989 |
64
- | 0.1572 | 16.0 | 2128 | 0.0890 |
65
- | 0.1572 | 17.0 | 2261 | 0.0736 |
66
- | 0.1572 | 18.0 | 2394 | 0.0672 |
67
- | 0.1007 | 19.0 | 2527 | 0.0592 |
68
- | 0.1007 | 20.0 | 2660 | 0.0550 |
69
- | 0.1007 | 21.0 | 2793 | 0.0517 |
70
- | 0.1007 | 22.0 | 2926 | 0.0497 |
71
- | 0.0674 | 23.0 | 3059 | 0.0458 |
72
- | 0.0674 | 24.0 | 3192 | 0.0421 |
73
- | 0.0674 | 25.0 | 3325 | 0.0394 |
74
- | 0.0674 | 26.0 | 3458 | 0.0378 |
75
- | 0.0491 | 27.0 | 3591 | 0.0357 |
76
- | 0.0491 | 28.0 | 3724 | 0.0337 |
77
- | 0.0491 | 29.0 | 3857 | 0.0323 |
78
- | 0.0491 | 30.0 | 3990 | 0.0319 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
79
 
80
 
81
  ### Framework versions
 
1
  ---
2
  license: apache-2.0
3
+ base_model: EleutherAI/pythia-70m
4
  tags:
5
  - generated_from_trainer
 
6
  model-index:
7
  - name: polish_wikipedia_model
8
  results: []
 
15
 
16
  This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.0137
19
 
20
  ## Model description
21
 
 
34
  ### Training hyperparameters
35
 
36
  The following hyperparameters were used during training:
37
+ - learning_rate: 2e-05
38
  - train_batch_size: 8
39
  - eval_batch_size: 8
40
  - seed: 42
41
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
42
  - lr_scheduler_type: linear
43
+ - num_epochs: 300
44
 
45
  ### Training results
46
 
47
  | Training Loss | Epoch | Step | Validation Loss |
48
  |:-------------:|:-----:|:----:|:---------------:|
49
+ | No log | 1.0 | 9 | 1.1594 |
50
+ | No log | 2.0 | 18 | 1.1355 |
51
+ | No log | 3.0 | 27 | 1.0811 |
52
+ | No log | 4.0 | 36 | 1.0362 |
53
+ | No log | 5.0 | 45 | 1.0053 |
54
+ | No log | 6.0 | 54 | 0.9728 |
55
+ | No log | 7.0 | 63 | 0.9478 |
56
+ | No log | 8.0 | 72 | 0.9089 |
57
+ | No log | 9.0 | 81 | 0.8784 |
58
+ | No log | 10.0 | 90 | 0.8576 |
59
+ | No log | 11.0 | 99 | 0.8391 |
60
+ | No log | 12.0 | 108 | 0.8121 |
61
+ | No log | 13.0 | 117 | 0.7778 |
62
+ | No log | 14.0 | 126 | 0.7642 |
63
+ | No log | 15.0 | 135 | 0.7411 |
64
+ | No log | 16.0 | 144 | 0.7252 |
65
+ | No log | 17.0 | 153 | 0.7541 |
66
+ | No log | 18.0 | 162 | 0.6939 |
67
+ | No log | 19.0 | 171 | 0.6616 |
68
+ | No log | 20.0 | 180 | 0.6834 |
69
+ | No log | 21.0 | 189 | 0.6032 |
70
+ | No log | 22.0 | 198 | 0.5909 |
71
+ | No log | 23.0 | 207 | 0.5899 |
72
+ | No log | 24.0 | 216 | 0.5610 |
73
+ | No log | 25.0 | 225 | 0.5404 |
74
+ | No log | 26.0 | 234 | 0.5576 |
75
+ | No log | 27.0 | 243 | 0.5253 |
76
+ | No log | 28.0 | 252 | 0.5085 |
77
+ | No log | 29.0 | 261 | 0.5035 |
78
+ | No log | 30.0 | 270 | 0.5017 |
79
+ | No log | 31.0 | 279 | 0.4817 |
80
+ | No log | 32.0 | 288 | 0.4690 |
81
+ | No log | 33.0 | 297 | 0.4569 |
82
+ | No log | 34.0 | 306 | 0.4611 |
83
+ | No log | 35.0 | 315 | 0.4389 |
84
+ | No log | 36.0 | 324 | 0.4598 |
85
+ | No log | 37.0 | 333 | 0.4308 |
86
+ | No log | 38.0 | 342 | 0.4101 |
87
+ | No log | 39.0 | 351 | 0.4056 |
88
+ | No log | 40.0 | 360 | 0.3939 |
89
+ | No log | 41.0 | 369 | 0.3801 |
90
+ | No log | 42.0 | 378 | 0.3741 |
91
+ | No log | 43.0 | 387 | 0.3739 |
92
+ | No log | 44.0 | 396 | 0.3779 |
93
+ | No log | 45.0 | 405 | 0.3633 |
94
+ | No log | 46.0 | 414 | 0.3614 |
95
+ | No log | 47.0 | 423 | 0.3497 |
96
+ | No log | 48.0 | 432 | 0.3508 |
97
+ | No log | 49.0 | 441 | 0.3425 |
98
+ | No log | 50.0 | 450 | 0.3399 |
99
+ | No log | 51.0 | 459 | 0.3357 |
100
+ | No log | 52.0 | 468 | 0.3393 |
101
+ | No log | 53.0 | 477 | 0.3241 |
102
+ | No log | 54.0 | 486 | 0.3427 |
103
+ | No log | 55.0 | 495 | 0.3452 |
104
+ | 0.614 | 56.0 | 504 | 0.3283 |
105
+ | 0.614 | 57.0 | 513 | 0.3182 |
106
+ | 0.614 | 58.0 | 522 | 0.3192 |
107
+ | 0.614 | 59.0 | 531 | 0.3118 |
108
+ | 0.614 | 60.0 | 540 | 0.3055 |
109
+ | 0.614 | 61.0 | 549 | 0.3109 |
110
+ | 0.614 | 62.0 | 558 | 0.2976 |
111
+ | 0.614 | 63.0 | 567 | 0.3052 |
112
+ | 0.614 | 64.0 | 576 | 0.2988 |
113
+ | 0.614 | 65.0 | 585 | 0.3035 |
114
+ | 0.614 | 66.0 | 594 | 0.2874 |
115
+ | 0.614 | 67.0 | 603 | 0.2812 |
116
+ | 0.614 | 68.0 | 612 | 0.2828 |
117
+ | 0.614 | 69.0 | 621 | 0.2786 |
118
+ | 0.614 | 70.0 | 630 | 0.2775 |
119
+ | 0.614 | 71.0 | 639 | 0.2828 |
120
+ | 0.614 | 72.0 | 648 | 0.2710 |
121
+ | 0.614 | 73.0 | 657 | 0.2725 |
122
+ | 0.614 | 74.0 | 666 | 0.2930 |
123
+ | 0.614 | 75.0 | 675 | 0.2642 |
124
+ | 0.614 | 76.0 | 684 | 0.2661 |
125
+ | 0.614 | 77.0 | 693 | 0.2493 |
126
+ | 0.614 | 78.0 | 702 | 0.2494 |
127
+ | 0.614 | 79.0 | 711 | 0.2370 |
128
+ | 0.614 | 80.0 | 720 | 0.2497 |
129
+ | 0.614 | 81.0 | 729 | 0.2399 |
130
+ | 0.614 | 82.0 | 738 | 0.2340 |
131
+ | 0.614 | 83.0 | 747 | 0.2248 |
132
+ | 0.614 | 84.0 | 756 | 0.2234 |
133
+ | 0.614 | 85.0 | 765 | 0.2284 |
134
+ | 0.614 | 86.0 | 774 | 0.2099 |
135
+ | 0.614 | 87.0 | 783 | 0.2081 |
136
+ | 0.614 | 88.0 | 792 | 0.1958 |
137
+ | 0.614 | 89.0 | 801 | 0.1969 |
138
+ | 0.614 | 90.0 | 810 | 0.1843 |
139
+ | 0.614 | 91.0 | 819 | 0.1746 |
140
+ | 0.614 | 92.0 | 828 | 0.1718 |
141
+ | 0.614 | 93.0 | 837 | 0.1665 |
142
+ | 0.614 | 94.0 | 846 | 0.1597 |
143
+ | 0.614 | 95.0 | 855 | 0.1633 |
144
+ | 0.614 | 96.0 | 864 | 0.1490 |
145
+ | 0.614 | 97.0 | 873 | 0.1414 |
146
+ | 0.614 | 98.0 | 882 | 0.1344 |
147
+ | 0.614 | 99.0 | 891 | 0.1446 |
148
+ | 0.614 | 100.0 | 900 | 0.1426 |
149
+ | 0.614 | 101.0 | 909 | 0.1364 |
150
+ | 0.614 | 102.0 | 918 | 0.1310 |
151
+ | 0.614 | 103.0 | 927 | 0.1342 |
152
+ | 0.614 | 104.0 | 936 | 0.1312 |
153
+ | 0.614 | 105.0 | 945 | 0.1178 |
154
+ | 0.614 | 106.0 | 954 | 0.1040 |
155
+ | 0.614 | 107.0 | 963 | 0.0998 |
156
+ | 0.614 | 108.0 | 972 | 0.1120 |
157
+ | 0.614 | 109.0 | 981 | 0.1798 |
158
+ | 0.614 | 110.0 | 990 | 0.1072 |
159
+ | 0.614 | 111.0 | 999 | 0.0864 |
160
+ | 0.2254 | 112.0 | 1008 | 0.0876 |
161
+ | 0.2254 | 113.0 | 1017 | 0.0805 |
162
+ | 0.2254 | 114.0 | 1026 | 0.0684 |
163
+ | 0.2254 | 115.0 | 1035 | 0.0826 |
164
+ | 0.2254 | 116.0 | 1044 | 0.0772 |
165
+ | 0.2254 | 117.0 | 1053 | 0.0667 |
166
+ | 0.2254 | 118.0 | 1062 | 0.0616 |
167
+ | 0.2254 | 119.0 | 1071 | 0.0641 |
168
+ | 0.2254 | 120.0 | 1080 | 0.0528 |
169
+ | 0.2254 | 121.0 | 1089 | 0.0520 |
170
+ | 0.2254 | 122.0 | 1098 | 0.0454 |
171
+ | 0.2254 | 123.0 | 1107 | 0.0407 |
172
+ | 0.2254 | 124.0 | 1116 | 0.0440 |
173
+ | 0.2254 | 125.0 | 1125 | 0.0449 |
174
+ | 0.2254 | 126.0 | 1134 | 0.0423 |
175
+ | 0.2254 | 127.0 | 1143 | 0.0503 |
176
+ | 0.2254 | 128.0 | 1152 | 0.0380 |
177
+ | 0.2254 | 129.0 | 1161 | 0.0440 |
178
+ | 0.2254 | 130.0 | 1170 | 0.0435 |
179
+ | 0.2254 | 131.0 | 1179 | 0.0718 |
180
+ | 0.2254 | 132.0 | 1188 | 0.0483 |
181
+ | 0.2254 | 133.0 | 1197 | 0.0474 |
182
+ | 0.2254 | 134.0 | 1206 | 0.0424 |
183
+ | 0.2254 | 135.0 | 1215 | 0.0387 |
184
+ | 0.2254 | 136.0 | 1224 | 0.0357 |
185
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186
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187
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188
+ | 0.2254 | 140.0 | 1260 | 0.0375 |
189
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190
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191
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192
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193
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194
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195
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196
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197
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198
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199
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200
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201
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202
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203
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204
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205
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206
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207
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208
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209
+ | 0.2254 | 161.0 | 1449 | 0.0224 |
210
+ | 0.2254 | 162.0 | 1458 | 0.0237 |
211
+ | 0.2254 | 163.0 | 1467 | 0.0228 |
212
+ | 0.2254 | 164.0 | 1476 | 0.0198 |
213
+ | 0.2254 | 165.0 | 1485 | 0.0315 |
214
+ | 0.2254 | 166.0 | 1494 | 0.0283 |
215
+ | 0.046 | 167.0 | 1503 | 0.0245 |
216
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217
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218
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219
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220
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221
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222
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223
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224
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225
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226
+ | 0.046 | 178.0 | 1602 | 0.0193 |
227
+ | 0.046 | 179.0 | 1611 | 0.0188 |
228
+ | 0.046 | 180.0 | 1620 | 0.0189 |
229
+ | 0.046 | 181.0 | 1629 | 0.0224 |
230
+ | 0.046 | 182.0 | 1638 | 0.0228 |
231
+ | 0.046 | 183.0 | 1647 | 0.0239 |
232
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233
+ | 0.046 | 185.0 | 1665 | 0.0175 |
234
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235
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236
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237
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238
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239
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240
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241
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242
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243
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244
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245
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246
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247
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248
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249
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250
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251
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252
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253
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254
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255
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256
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257
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258
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259
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260
+ | 0.046 | 212.0 | 1908 | 0.0167 |
261
+ | 0.046 | 213.0 | 1917 | 0.0183 |
262
+ | 0.046 | 214.0 | 1926 | 0.0173 |
263
+ | 0.046 | 215.0 | 1935 | 0.0163 |
264
+ | 0.046 | 216.0 | 1944 | 0.0164 |
265
+ | 0.046 | 217.0 | 1953 | 0.0182 |
266
+ | 0.046 | 218.0 | 1962 | 0.0177 |
267
+ | 0.046 | 219.0 | 1971 | 0.0164 |
268
+ | 0.046 | 220.0 | 1980 | 0.0171 |
269
+ | 0.046 | 221.0 | 1989 | 0.0163 |
270
+ | 0.046 | 222.0 | 1998 | 0.0184 |
271
+ | 0.0226 | 223.0 | 2007 | 0.0180 |
272
+ | 0.0226 | 224.0 | 2016 | 0.0198 |
273
+ | 0.0226 | 225.0 | 2025 | 0.0181 |
274
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275
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
+ | 0.0226 | 254.0 | 2286 | 0.0156 |
303
+ | 0.0226 | 255.0 | 2295 | 0.0156 |
304
+ | 0.0226 | 256.0 | 2304 | 0.0146 |
305
+ | 0.0226 | 257.0 | 2313 | 0.0149 |
306
+ | 0.0226 | 258.0 | 2322 | 0.0150 |
307
+ | 0.0226 | 259.0 | 2331 | 0.0158 |
308
+ | 0.0226 | 260.0 | 2340 | 0.0142 |
309
+ | 0.0226 | 261.0 | 2349 | 0.0147 |
310
+ | 0.0226 | 262.0 | 2358 | 0.0144 |
311
+ | 0.0226 | 263.0 | 2367 | 0.0145 |
312
+ | 0.0226 | 264.0 | 2376 | 0.0142 |
313
+ | 0.0226 | 265.0 | 2385 | 0.0143 |
314
+ | 0.0226 | 266.0 | 2394 | 0.0140 |
315
+ | 0.0226 | 267.0 | 2403 | 0.0141 |
316
+ | 0.0226 | 268.0 | 2412 | 0.0153 |
317
+ | 0.0226 | 269.0 | 2421 | 0.0141 |
318
+ | 0.0226 | 270.0 | 2430 | 0.0144 |
319
+ | 0.0226 | 271.0 | 2439 | 0.0139 |
320
+ | 0.0226 | 272.0 | 2448 | 0.0141 |
321
+ | 0.0226 | 273.0 | 2457 | 0.0141 |
322
+ | 0.0226 | 274.0 | 2466 | 0.0139 |
323
+ | 0.0226 | 275.0 | 2475 | 0.0141 |
324
+ | 0.0226 | 276.0 | 2484 | 0.0140 |
325
+ | 0.0226 | 277.0 | 2493 | 0.0142 |
326
+ | 0.0165 | 278.0 | 2502 | 0.0146 |
327
+ | 0.0165 | 279.0 | 2511 | 0.0141 |
328
+ | 0.0165 | 280.0 | 2520 | 0.0138 |
329
+ | 0.0165 | 281.0 | 2529 | 0.0138 |
330
+ | 0.0165 | 282.0 | 2538 | 0.0138 |
331
+ | 0.0165 | 283.0 | 2547 | 0.0138 |
332
+ | 0.0165 | 284.0 | 2556 | 0.0138 |
333
+ | 0.0165 | 285.0 | 2565 | 0.0139 |
334
+ | 0.0165 | 286.0 | 2574 | 0.0137 |
335
+ | 0.0165 | 287.0 | 2583 | 0.0137 |
336
+ | 0.0165 | 288.0 | 2592 | 0.0137 |
337
+ | 0.0165 | 289.0 | 2601 | 0.0138 |
338
+ | 0.0165 | 290.0 | 2610 | 0.0137 |
339
+ | 0.0165 | 291.0 | 2619 | 0.0137 |
340
+ | 0.0165 | 292.0 | 2628 | 0.0137 |
341
+ | 0.0165 | 293.0 | 2637 | 0.0137 |
342
+ | 0.0165 | 294.0 | 2646 | 0.0137 |
343
+ | 0.0165 | 295.0 | 2655 | 0.0137 |
344
+ | 0.0165 | 296.0 | 2664 | 0.0137 |
345
+ | 0.0165 | 297.0 | 2673 | 0.0137 |
346
+ | 0.0165 | 298.0 | 2682 | 0.0137 |
347
+ | 0.0165 | 299.0 | 2691 | 0.0137 |
348
+ | 0.0165 | 300.0 | 2700 | 0.0137 |
349
 
350
 
351
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