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best_model-yelp_polarity-64-21

Text Classification
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PyTorch
albert
Generated from Trainer
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  ---
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- license: mit
3
- base_model: roberta-base
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -15,10 +15,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # best_model-yelp_polarity-64-21
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- This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7237
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- - Accuracy: 0.9375
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  ## Model description
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@@ -50,156 +50,156 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
- | No log | 1.0 | 4 | 0.4953 | 0.9531 |
54
- | No log | 2.0 | 8 | 0.4901 | 0.9609 |
55
- | 0.4161 | 3.0 | 12 | 0.4944 | 0.9609 |
56
- | 0.4161 | 4.0 | 16 | 0.5364 | 0.9531 |
57
- | 0.3982 | 5.0 | 20 | 0.5743 | 0.9531 |
58
- | 0.3982 | 6.0 | 24 | 0.5888 | 0.9531 |
59
- | 0.3982 | 7.0 | 28 | 0.5943 | 0.9531 |
60
- | 0.271 | 8.0 | 32 | 0.5953 | 0.9531 |
61
- | 0.271 | 9.0 | 36 | 0.5948 | 0.9531 |
62
- | 0.3643 | 10.0 | 40 | 0.5942 | 0.9531 |
63
- | 0.3643 | 11.0 | 44 | 0.5936 | 0.9531 |
64
- | 0.3643 | 12.0 | 48 | 0.5918 | 0.9531 |
65
- | 0.2103 | 13.0 | 52 | 0.5912 | 0.9531 |
66
- | 0.2103 | 14.0 | 56 | 0.5900 | 0.9531 |
67
- | 0.1932 | 15.0 | 60 | 0.5847 | 0.9531 |
68
- | 0.1932 | 16.0 | 64 | 0.5810 | 0.9531 |
69
- | 0.1932 | 17.0 | 68 | 0.5774 | 0.9531 |
70
- | 0.1372 | 18.0 | 72 | 0.5731 | 0.9531 |
71
- | 0.1372 | 19.0 | 76 | 0.5691 | 0.9531 |
72
- | 0.0774 | 20.0 | 80 | 0.5697 | 0.9531 |
73
- | 0.0774 | 21.0 | 84 | 0.5627 | 0.9531 |
74
- | 0.0774 | 22.0 | 88 | 0.5599 | 0.9531 |
75
- | 0.0831 | 23.0 | 92 | 0.5587 | 0.9531 |
76
- | 0.0831 | 24.0 | 96 | 0.5821 | 0.9453 |
77
- | 0.0236 | 25.0 | 100 | 0.5533 | 0.9531 |
78
- | 0.0236 | 26.0 | 104 | 0.5497 | 0.9531 |
79
- | 0.0236 | 27.0 | 108 | 0.5459 | 0.9531 |
80
- | 0.0245 | 28.0 | 112 | 0.5447 | 0.9531 |
81
- | 0.0245 | 29.0 | 116 | 0.5385 | 0.9531 |
82
- | 0.0123 | 30.0 | 120 | 0.5433 | 0.9453 |
83
- | 0.0123 | 31.0 | 124 | 0.5401 | 0.9453 |
84
- | 0.0123 | 32.0 | 128 | 0.5369 | 0.9453 |
85
- | 0.0 | 33.0 | 132 | 0.5347 | 0.9453 |
86
- | 0.0 | 34.0 | 136 | 0.5363 | 0.9453 |
87
- | 0.0001 | 35.0 | 140 | 0.5268 | 0.9531 |
88
- | 0.0001 | 36.0 | 144 | 0.5327 | 0.9531 |
89
- | 0.0001 | 37.0 | 148 | 0.5355 | 0.9531 |
90
- | 0.0 | 38.0 | 152 | 0.5369 | 0.9531 |
91
- | 0.0 | 39.0 | 156 | 0.5374 | 0.9531 |
92
- | 0.0 | 40.0 | 160 | 0.5374 | 0.9531 |
93
- | 0.0 | 41.0 | 164 | 0.5372 | 0.9531 |
94
- | 0.0 | 42.0 | 168 | 0.5366 | 0.9531 |
95
- | 0.0 | 43.0 | 172 | 0.5345 | 0.9531 |
96
- | 0.0 | 44.0 | 176 | 0.5323 | 0.9531 |
97
- | 0.0 | 45.0 | 180 | 0.5295 | 0.9453 |
98
- | 0.0 | 46.0 | 184 | 0.5441 | 0.9453 |
99
- | 0.0 | 47.0 | 188 | 0.5519 | 0.9453 |
100
- | 0.0 | 48.0 | 192 | 0.5562 | 0.9453 |
101
- | 0.0 | 49.0 | 196 | 0.5588 | 0.9453 |
102
- | 0.0 | 50.0 | 200 | 0.5607 | 0.9453 |
103
- | 0.0 | 51.0 | 204 | 0.5622 | 0.9453 |
104
- | 0.0 | 52.0 | 208 | 0.5632 | 0.9453 |
105
- | 0.0 | 53.0 | 212 | 0.5640 | 0.9453 |
106
- | 0.0 | 54.0 | 216 | 0.5660 | 0.9453 |
107
- | 0.0001 | 55.0 | 220 | 0.5577 | 0.9531 |
108
- | 0.0001 | 56.0 | 224 | 0.6090 | 0.9453 |
109
- | 0.0001 | 57.0 | 228 | 0.5699 | 0.9453 |
110
- | 0.0 | 58.0 | 232 | 0.5844 | 0.9453 |
111
- | 0.0 | 59.0 | 236 | 0.6061 | 0.9375 |
112
- | 0.0318 | 60.0 | 240 | 0.5903 | 0.9453 |
113
- | 0.0318 | 61.0 | 244 | 0.5835 | 0.9453 |
114
- | 0.0318 | 62.0 | 248 | 0.5701 | 0.9453 |
115
- | 0.0 | 63.0 | 252 | 0.5625 | 0.9531 |
116
- | 0.0 | 64.0 | 256 | 0.5609 | 0.9531 |
117
- | 0.0 | 65.0 | 260 | 0.5609 | 0.9531 |
118
- | 0.0 | 66.0 | 264 | 0.5975 | 0.9375 |
119
- | 0.0 | 67.0 | 268 | 0.6321 | 0.9297 |
120
- | 0.0194 | 68.0 | 272 | 0.6293 | 0.9375 |
121
- | 0.0194 | 69.0 | 276 | 0.6356 | 0.9297 |
122
- | 0.0134 | 70.0 | 280 | 0.5923 | 0.9453 |
123
- | 0.0134 | 71.0 | 284 | 0.5733 | 0.9453 |
124
- | 0.0134 | 72.0 | 288 | 0.5553 | 0.9531 |
125
- | 0.0 | 73.0 | 292 | 0.5595 | 0.9453 |
126
- | 0.0 | 74.0 | 296 | 0.5778 | 0.9453 |
127
- | 0.0001 | 75.0 | 300 | 0.6930 | 0.9297 |
128
- | 0.0001 | 76.0 | 304 | 0.6281 | 0.9375 |
129
- | 0.0001 | 77.0 | 308 | 0.6218 | 0.9375 |
130
- | 0.0018 | 78.0 | 312 | 0.5614 | 0.9453 |
131
- | 0.0018 | 79.0 | 316 | 0.5087 | 0.9531 |
132
- | 0.0206 | 80.0 | 320 | 0.4872 | 0.9531 |
133
- | 0.0206 | 81.0 | 324 | 0.4978 | 0.9531 |
134
- | 0.0206 | 82.0 | 328 | 0.5067 | 0.9531 |
135
- | 0.0 | 83.0 | 332 | 0.5116 | 0.9531 |
136
- | 0.0 | 84.0 | 336 | 0.5143 | 0.9531 |
137
- | 0.0 | 85.0 | 340 | 0.5159 | 0.9531 |
138
- | 0.0 | 86.0 | 344 | 0.5175 | 0.9531 |
139
- | 0.0 | 87.0 | 348 | 0.5206 | 0.9531 |
140
- | 0.0 | 88.0 | 352 | 0.5255 | 0.9453 |
141
- | 0.0 | 89.0 | 356 | 0.5319 | 0.9453 |
142
- | 0.0 | 90.0 | 360 | 0.5390 | 0.9375 |
143
- | 0.0 | 91.0 | 364 | 0.5455 | 0.9375 |
144
- | 0.0 | 92.0 | 368 | 0.5516 | 0.9375 |
145
- | 0.0 | 93.0 | 372 | 0.5572 | 0.9375 |
146
- | 0.0 | 94.0 | 376 | 0.5623 | 0.9375 |
147
- | 0.0 | 95.0 | 380 | 0.5664 | 0.9375 |
148
- | 0.0 | 96.0 | 384 | 0.5692 | 0.9375 |
149
- | 0.0 | 97.0 | 388 | 0.5712 | 0.9375 |
150
- | 0.0 | 98.0 | 392 | 0.5734 | 0.9375 |
151
- | 0.0 | 99.0 | 396 | 0.5754 | 0.9375 |
152
- | 0.0 | 100.0 | 400 | 0.5765 | 0.9375 |
153
- | 0.0 | 101.0 | 404 | 0.5815 | 0.9375 |
154
- | 0.0 | 102.0 | 408 | 0.5821 | 0.9375 |
155
- | 0.0 | 103.0 | 412 | 0.5819 | 0.9375 |
156
- | 0.0 | 104.0 | 416 | 0.5818 | 0.9375 |
157
- | 0.0 | 105.0 | 420 | 0.5805 | 0.9375 |
158
- | 0.0 | 106.0 | 424 | 0.5984 | 0.9375 |
159
- | 0.0 | 107.0 | 428 | 0.5581 | 0.9453 |
160
- | 0.0231 | 108.0 | 432 | 0.5229 | 0.9531 |
161
- | 0.0231 | 109.0 | 436 | 0.4868 | 0.9453 |
162
- | 0.0 | 110.0 | 440 | 0.5184 | 0.9531 |
163
- | 0.0 | 111.0 | 444 | 0.5554 | 0.9453 |
164
- | 0.0 | 112.0 | 448 | 0.7197 | 0.9375 |
165
- | 0.0001 | 113.0 | 452 | 0.7466 | 0.9375 |
166
- | 0.0001 | 114.0 | 456 | 0.7533 | 0.9375 |
167
- | 0.0 | 115.0 | 460 | 0.7535 | 0.9375 |
168
- | 0.0 | 116.0 | 464 | 0.7472 | 0.9375 |
169
- | 0.0 | 117.0 | 468 | 0.7407 | 0.9375 |
170
- | 0.0 | 118.0 | 472 | 0.7366 | 0.9375 |
171
- | 0.0 | 119.0 | 476 | 0.7347 | 0.9297 |
172
- | 0.0 | 120.0 | 480 | 0.7338 | 0.9297 |
173
- | 0.0 | 121.0 | 484 | 0.7366 | 0.9297 |
174
- | 0.0 | 122.0 | 488 | 0.7408 | 0.9297 |
175
- | 0.0 | 123.0 | 492 | 0.7434 | 0.9297 |
176
- | 0.0 | 124.0 | 496 | 0.7454 | 0.9297 |
177
- | 0.0073 | 125.0 | 500 | 0.6336 | 0.9453 |
178
- | 0.0073 | 126.0 | 504 | 0.5907 | 0.9453 |
179
- | 0.0073 | 127.0 | 508 | 0.6316 | 0.9453 |
180
- | 0.0023 | 128.0 | 512 | 0.6673 | 0.9453 |
181
- | 0.0023 | 129.0 | 516 | 0.6764 | 0.9453 |
182
- | 0.0 | 130.0 | 520 | 0.6814 | 0.9453 |
183
- | 0.0 | 131.0 | 524 | 0.6917 | 0.9375 |
184
- | 0.0 | 132.0 | 528 | 0.7031 | 0.9375 |
185
- | 0.0 | 133.0 | 532 | 0.7111 | 0.9375 |
186
- | 0.0 | 134.0 | 536 | 0.7161 | 0.9375 |
187
- | 0.0 | 135.0 | 540 | 0.7153 | 0.9375 |
188
- | 0.0 | 136.0 | 544 | 0.7137 | 0.9375 |
189
- | 0.0 | 137.0 | 548 | 0.7130 | 0.9375 |
190
- | 0.0 | 138.0 | 552 | 0.7126 | 0.9375 |
191
- | 0.0 | 139.0 | 556 | 0.7126 | 0.9375 |
192
- | 0.0 | 140.0 | 560 | 0.7127 | 0.9375 |
193
- | 0.0 | 141.0 | 564 | 0.7154 | 0.9375 |
194
- | 0.0 | 142.0 | 568 | 0.7190 | 0.9375 |
195
- | 0.0 | 143.0 | 572 | 0.7211 | 0.9375 |
196
- | 0.0 | 144.0 | 576 | 0.7223 | 0.9375 |
197
- | 0.0 | 145.0 | 580 | 0.7230 | 0.9375 |
198
- | 0.0 | 146.0 | 584 | 0.7233 | 0.9375 |
199
- | 0.0 | 147.0 | 588 | 0.7235 | 0.9375 |
200
- | 0.0 | 148.0 | 592 | 0.7236 | 0.9375 |
201
- | 0.0 | 149.0 | 596 | 0.7237 | 0.9375 |
202
- | 0.0 | 150.0 | 600 | 0.7237 | 0.9375 |
203
 
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205
  ### Framework versions
 
1
  ---
2
+ license: apache-2.0
3
+ base_model: albert-base-v2
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  tags:
5
  - generated_from_trainer
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  metrics:
 
15
 
16
  # best_model-yelp_polarity-64-21
17
 
18
+ This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 0.6300
21
+ - Accuracy: 0.9219
22
 
23
  ## Model description
24
 
 
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
+ | No log | 1.0 | 4 | 0.6653 | 0.9297 |
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+ | No log | 2.0 | 8 | 0.6599 | 0.9375 |
55
+ | 0.3506 | 3.0 | 12 | 0.6517 | 0.9375 |
56
+ | 0.3506 | 4.0 | 16 | 0.6448 | 0.9375 |
57
+ | 0.4992 | 5.0 | 20 | 0.6507 | 0.9375 |
58
+ | 0.4992 | 6.0 | 24 | 0.6967 | 0.9219 |
59
+ | 0.4992 | 7.0 | 28 | 0.7602 | 0.9141 |
60
+ | 0.3039 | 8.0 | 32 | 0.9351 | 0.8984 |
61
+ | 0.3039 | 9.0 | 36 | 0.9244 | 0.8984 |
62
+ | 0.2241 | 10.0 | 40 | 0.7974 | 0.9062 |
63
+ | 0.2241 | 11.0 | 44 | 0.7229 | 0.9219 |
64
+ | 0.2241 | 12.0 | 48 | 0.6981 | 0.9219 |
65
+ | 0.1025 | 13.0 | 52 | 0.6961 | 0.9219 |
66
+ | 0.1025 | 14.0 | 56 | 0.6819 | 0.9219 |
67
+ | 0.1057 | 15.0 | 60 | 0.6655 | 0.9219 |
68
+ | 0.1057 | 16.0 | 64 | 0.6463 | 0.9219 |
69
+ | 0.1057 | 17.0 | 68 | 0.6240 | 0.9219 |
70
+ | 0.0733 | 18.0 | 72 | 0.6086 | 0.9141 |
71
+ | 0.0733 | 19.0 | 76 | 0.6109 | 0.9141 |
72
+ | 0.0366 | 20.0 | 80 | 0.6219 | 0.9141 |
73
+ | 0.0366 | 21.0 | 84 | 0.6291 | 0.9141 |
74
+ | 0.0366 | 22.0 | 88 | 0.6227 | 0.9219 |
75
+ | 0.0449 | 23.0 | 92 | 0.6182 | 0.9219 |
76
+ | 0.0449 | 24.0 | 96 | 0.6148 | 0.9219 |
77
+ | 0.0188 | 25.0 | 100 | 0.5999 | 0.9219 |
78
+ | 0.0188 | 26.0 | 104 | 0.5537 | 0.9297 |
79
+ | 0.0188 | 27.0 | 108 | 0.5538 | 0.9297 |
80
+ | 0.0146 | 28.0 | 112 | 0.5492 | 0.9297 |
81
+ | 0.0146 | 29.0 | 116 | 0.5275 | 0.9297 |
82
+ | 0.0131 | 30.0 | 120 | 0.5212 | 0.9219 |
83
+ | 0.0131 | 31.0 | 124 | 0.5486 | 0.9219 |
84
+ | 0.0131 | 32.0 | 128 | 0.5641 | 0.9141 |
85
+ | 0.0074 | 33.0 | 132 | 0.5489 | 0.9219 |
86
+ | 0.0074 | 34.0 | 136 | 0.5426 | 0.9219 |
87
+ | 0.0042 | 35.0 | 140 | 0.5468 | 0.9141 |
88
+ | 0.0042 | 36.0 | 144 | 0.5411 | 0.9141 |
89
+ | 0.0042 | 37.0 | 148 | 0.5366 | 0.9219 |
90
+ | 0.0027 | 38.0 | 152 | 0.5306 | 0.9219 |
91
+ | 0.0027 | 39.0 | 156 | 0.5182 | 0.9219 |
92
+ | 0.0011 | 40.0 | 160 | 0.5096 | 0.9219 |
93
+ | 0.0011 | 41.0 | 164 | 0.5059 | 0.9219 |
94
+ | 0.0011 | 42.0 | 168 | 0.5130 | 0.9219 |
95
+ | 0.0007 | 43.0 | 172 | 0.5198 | 0.9219 |
96
+ | 0.0007 | 44.0 | 176 | 0.5172 | 0.9219 |
97
+ | 0.0007 | 45.0 | 180 | 0.5129 | 0.9219 |
98
+ | 0.0007 | 46.0 | 184 | 0.5337 | 0.9062 |
99
+ | 0.0007 | 47.0 | 188 | 0.5600 | 0.9141 |
100
+ | 0.0003 | 48.0 | 192 | 0.5687 | 0.9141 |
101
+ | 0.0003 | 49.0 | 196 | 0.5413 | 0.9141 |
102
+ | 0.0003 | 50.0 | 200 | 0.5270 | 0.9062 |
103
+ | 0.0003 | 51.0 | 204 | 0.5249 | 0.9141 |
104
+ | 0.0003 | 52.0 | 208 | 0.5315 | 0.9141 |
105
+ | 0.0002 | 53.0 | 212 | 0.5528 | 0.9141 |
106
+ | 0.0002 | 54.0 | 216 | 0.5732 | 0.9141 |
107
+ | 0.0001 | 55.0 | 220 | 0.5812 | 0.9141 |
108
+ | 0.0001 | 56.0 | 224 | 0.5871 | 0.9141 |
109
+ | 0.0001 | 57.0 | 228 | 0.5854 | 0.9141 |
110
+ | 0.0001 | 58.0 | 232 | 0.5846 | 0.9141 |
111
+ | 0.0001 | 59.0 | 236 | 0.5842 | 0.9141 |
112
+ | 0.0 | 60.0 | 240 | 0.5865 | 0.9141 |
113
+ | 0.0 | 61.0 | 244 | 0.5895 | 0.9141 |
114
+ | 0.0 | 62.0 | 248 | 0.5908 | 0.9141 |
115
+ | 0.0001 | 63.0 | 252 | 0.5911 | 0.9141 |
116
+ | 0.0001 | 64.0 | 256 | 0.5905 | 0.9141 |
117
+ | 0.0 | 65.0 | 260 | 0.5870 | 0.9141 |
118
+ | 0.0 | 66.0 | 264 | 0.5859 | 0.9141 |
119
+ | 0.0 | 67.0 | 268 | 0.5863 | 0.9141 |
120
+ | 0.0 | 68.0 | 272 | 0.5881 | 0.9141 |
121
+ | 0.0 | 69.0 | 276 | 0.5888 | 0.9141 |
122
+ | 0.0 | 70.0 | 280 | 0.5902 | 0.9141 |
123
+ | 0.0 | 71.0 | 284 | 0.5926 | 0.9141 |
124
+ | 0.0 | 72.0 | 288 | 0.5945 | 0.9141 |
125
+ | 0.0 | 73.0 | 292 | 0.5949 | 0.9141 |
126
+ | 0.0 | 74.0 | 296 | 0.5962 | 0.9141 |
127
+ | 0.0 | 75.0 | 300 | 0.5982 | 0.9141 |
128
+ | 0.0 | 76.0 | 304 | 0.6003 | 0.9141 |
129
+ | 0.0 | 77.0 | 308 | 0.6014 | 0.9141 |
130
+ | 0.0 | 78.0 | 312 | 0.6018 | 0.9219 |
131
+ | 0.0 | 79.0 | 316 | 0.6024 | 0.9219 |
132
+ | 0.0 | 80.0 | 320 | 0.6037 | 0.9219 |
133
+ | 0.0 | 81.0 | 324 | 0.6041 | 0.9219 |
134
+ | 0.0 | 82.0 | 328 | 0.6052 | 0.9219 |
135
+ | 0.0 | 83.0 | 332 | 0.6064 | 0.9219 |
136
+ | 0.0 | 84.0 | 336 | 0.6069 | 0.9219 |
137
+ | 0.0 | 85.0 | 340 | 0.6069 | 0.9219 |
138
+ | 0.0 | 86.0 | 344 | 0.6074 | 0.9219 |
139
+ | 0.0 | 87.0 | 348 | 0.6089 | 0.9219 |
140
+ | 0.0 | 88.0 | 352 | 0.6098 | 0.9219 |
141
+ | 0.0 | 89.0 | 356 | 0.6098 | 0.9219 |
142
+ | 0.0 | 90.0 | 360 | 0.6100 | 0.9219 |
143
+ | 0.0 | 91.0 | 364 | 0.6098 | 0.9219 |
144
+ | 0.0 | 92.0 | 368 | 0.6098 | 0.9219 |
145
+ | 0.0 | 93.0 | 372 | 0.6101 | 0.9219 |
146
+ | 0.0 | 94.0 | 376 | 0.6111 | 0.9219 |
147
+ | 0.0 | 95.0 | 380 | 0.6122 | 0.9219 |
148
+ | 0.0 | 96.0 | 384 | 0.6131 | 0.9219 |
149
+ | 0.0 | 97.0 | 388 | 0.6122 | 0.9219 |
150
+ | 0.0 | 98.0 | 392 | 0.6127 | 0.9219 |
151
+ | 0.0 | 99.0 | 396 | 0.6124 | 0.9219 |
152
+ | 0.0 | 100.0 | 400 | 0.6120 | 0.9219 |
153
+ | 0.0 | 101.0 | 404 | 0.6127 | 0.9219 |
154
+ | 0.0 | 102.0 | 408 | 0.6132 | 0.9219 |
155
+ | 0.0 | 103.0 | 412 | 0.6140 | 0.9219 |
156
+ | 0.0 | 104.0 | 416 | 0.6150 | 0.9219 |
157
+ | 0.0 | 105.0 | 420 | 0.6158 | 0.9219 |
158
+ | 0.0 | 106.0 | 424 | 0.6160 | 0.9219 |
159
+ | 0.0 | 107.0 | 428 | 0.6161 | 0.9219 |
160
+ | 0.0 | 108.0 | 432 | 0.6166 | 0.9219 |
161
+ | 0.0 | 109.0 | 436 | 0.6168 | 0.9219 |
162
+ | 0.0 | 110.0 | 440 | 0.6170 | 0.9219 |
163
+ | 0.0 | 111.0 | 444 | 0.6178 | 0.9219 |
164
+ | 0.0 | 112.0 | 448 | 0.6184 | 0.9219 |
165
+ | 0.0 | 113.0 | 452 | 0.6189 | 0.9219 |
166
+ | 0.0 | 114.0 | 456 | 0.6197 | 0.9219 |
167
+ | 0.0 | 115.0 | 460 | 0.6213 | 0.9219 |
168
+ | 0.0 | 116.0 | 464 | 0.6220 | 0.9219 |
169
+ | 0.0 | 117.0 | 468 | 0.6226 | 0.9219 |
170
+ | 0.0 | 118.0 | 472 | 0.6229 | 0.9219 |
171
+ | 0.0 | 119.0 | 476 | 0.6235 | 0.9219 |
172
+ | 0.0 | 120.0 | 480 | 0.6219 | 0.9219 |
173
+ | 0.0 | 121.0 | 484 | 0.6219 | 0.9219 |
174
+ | 0.0 | 122.0 | 488 | 0.6223 | 0.9219 |
175
+ | 0.0 | 123.0 | 492 | 0.6236 | 0.9219 |
176
+ | 0.0 | 124.0 | 496 | 0.6246 | 0.9219 |
177
+ | 0.0 | 125.0 | 500 | 0.6259 | 0.9219 |
178
+ | 0.0 | 126.0 | 504 | 0.6265 | 0.9219 |
179
+ | 0.0 | 127.0 | 508 | 0.6270 | 0.9219 |
180
+ | 0.0 | 128.0 | 512 | 0.6272 | 0.9219 |
181
+ | 0.0 | 129.0 | 516 | 0.6271 | 0.9219 |
182
+ | 0.0 | 130.0 | 520 | 0.6262 | 0.9219 |
183
+ | 0.0 | 131.0 | 524 | 0.6257 | 0.9219 |
184
+ | 0.0 | 132.0 | 528 | 0.6255 | 0.9219 |
185
+ | 0.0 | 133.0 | 532 | 0.6258 | 0.9219 |
186
+ | 0.0 | 134.0 | 536 | 0.6262 | 0.9219 |
187
+ | 0.0 | 135.0 | 540 | 0.6272 | 0.9219 |
188
+ | 0.0 | 136.0 | 544 | 0.6277 | 0.9219 |
189
+ | 0.0 | 137.0 | 548 | 0.6286 | 0.9219 |
190
+ | 0.0 | 138.0 | 552 | 0.6288 | 0.9219 |
191
+ | 0.0 | 139.0 | 556 | 0.6292 | 0.9219 |
192
+ | 0.0 | 140.0 | 560 | 0.6295 | 0.9219 |
193
+ | 0.0 | 141.0 | 564 | 0.6293 | 0.9219 |
194
+ | 0.0 | 142.0 | 568 | 0.6294 | 0.9219 |
195
+ | 0.0 | 143.0 | 572 | 0.6296 | 0.9219 |
196
+ | 0.0 | 144.0 | 576 | 0.6299 | 0.9219 |
197
+ | 0.0 | 145.0 | 580 | 0.6297 | 0.9219 |
198
+ | 0.0 | 146.0 | 584 | 0.6299 | 0.9219 |
199
+ | 0.0 | 147.0 | 588 | 0.6300 | 0.9219 |
200
+ | 0.0 | 148.0 | 592 | 0.6300 | 0.9219 |
201
+ | 0.0 | 149.0 | 596 | 0.6300 | 0.9219 |
202
+ | 0.0 | 150.0 | 600 | 0.6300 | 0.9219 |
203
 
204
 
205
  ### Framework versions