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best_model-yelp_polarity-64-42

Text Classification
Transformers
PyTorch
albert
Generated from Trainer
Inference Endpoints
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  ---
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- license: mit
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- 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-64-42
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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: 0.8838
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- - Accuracy: 0.9141
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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 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
- | No log | 1.0 | 4 | 1.0394 | 0.9141 |
54
- | No log | 2.0 | 8 | 1.0413 | 0.9141 |
55
- | 0.5047 | 3.0 | 12 | 1.0408 | 0.9141 |
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- | 0.5047 | 4.0 | 16 | 1.0386 | 0.9141 |
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- | 0.4566 | 5.0 | 20 | 1.0336 | 0.9141 |
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- | 0.4566 | 6.0 | 24 | 1.0248 | 0.9141 |
59
- | 0.4566 | 7.0 | 28 | 1.0128 | 0.9141 |
60
- | 0.4026 | 8.0 | 32 | 1.0000 | 0.9141 |
61
- | 0.4026 | 9.0 | 36 | 0.9823 | 0.9141 |
62
- | 0.3103 | 10.0 | 40 | 0.9632 | 0.9141 |
63
- | 0.3103 | 11.0 | 44 | 0.9553 | 0.9219 |
64
- | 0.3103 | 12.0 | 48 | 0.9610 | 0.9141 |
65
- | 0.2537 | 13.0 | 52 | 0.9575 | 0.9141 |
66
- | 0.2537 | 14.0 | 56 | 0.9497 | 0.9141 |
67
- | 0.1335 | 15.0 | 60 | 0.9510 | 0.9141 |
68
- | 0.1335 | 16.0 | 64 | 0.9465 | 0.9141 |
69
- | 0.1335 | 17.0 | 68 | 0.9379 | 0.9141 |
70
- | 0.0655 | 18.0 | 72 | 0.9312 | 0.9141 |
71
- | 0.0655 | 19.0 | 76 | 0.9317 | 0.9141 |
72
- | 0.051 | 20.0 | 80 | 0.9246 | 0.9141 |
73
- | 0.051 | 21.0 | 84 | 0.9026 | 0.9141 |
74
- | 0.051 | 22.0 | 88 | 0.8836 | 0.9141 |
75
- | 0.0012 | 23.0 | 92 | 0.8697 | 0.9141 |
76
- | 0.0012 | 24.0 | 96 | 0.8588 | 0.9141 |
77
- | 0.0003 | 25.0 | 100 | 0.8458 | 0.9141 |
78
- | 0.0003 | 26.0 | 104 | 0.8323 | 0.9141 |
79
- | 0.0003 | 27.0 | 108 | 0.8499 | 0.9141 |
80
- | 0.0019 | 28.0 | 112 | 0.8750 | 0.9219 |
81
- | 0.0019 | 29.0 | 116 | 0.8897 | 0.9219 |
82
- | 0.0 | 30.0 | 120 | 0.8943 | 0.9219 |
83
- | 0.0 | 31.0 | 124 | 0.8570 | 0.9219 |
84
- | 0.0 | 32.0 | 128 | 0.8162 | 0.9219 |
85
- | 0.0065 | 33.0 | 132 | 0.8156 | 0.9141 |
86
- | 0.0065 | 34.0 | 136 | 0.8147 | 0.9141 |
87
- | 0.0137 | 35.0 | 140 | 0.8191 | 0.9219 |
88
- | 0.0137 | 36.0 | 144 | 0.8258 | 0.9219 |
89
- | 0.0137 | 37.0 | 148 | 0.8316 | 0.9141 |
90
- | 0.0 | 38.0 | 152 | 0.8362 | 0.9219 |
91
- | 0.0 | 39.0 | 156 | 0.8188 | 0.9141 |
92
- | 0.0001 | 40.0 | 160 | 0.8255 | 0.9141 |
93
- | 0.0001 | 41.0 | 164 | 0.8535 | 0.9062 |
94
- | 0.0001 | 42.0 | 168 | 0.8499 | 0.9062 |
95
- | 0.0017 | 43.0 | 172 | 0.8184 | 0.9141 |
96
- | 0.0017 | 44.0 | 176 | 0.8120 | 0.9297 |
97
- | 0.0 | 45.0 | 180 | 0.8277 | 0.9219 |
98
- | 0.0 | 46.0 | 184 | 0.8434 | 0.9219 |
99
- | 0.0 | 47.0 | 188 | 0.8535 | 0.9219 |
100
- | 0.0 | 48.0 | 192 | 0.8597 | 0.9219 |
101
- | 0.0 | 49.0 | 196 | 0.8633 | 0.9219 |
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- | 0.0 | 50.0 | 200 | 0.8651 | 0.9219 |
103
- | 0.0 | 51.0 | 204 | 0.8617 | 0.9219 |
104
- | 0.0 | 52.0 | 208 | 0.8571 | 0.9219 |
105
- | 0.0 | 53.0 | 212 | 0.8538 | 0.9219 |
106
- | 0.0 | 54.0 | 216 | 0.8514 | 0.9219 |
107
- | 0.0 | 55.0 | 220 | 0.8346 | 0.9219 |
108
- | 0.0 | 56.0 | 224 | 0.8153 | 0.9219 |
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- | 0.0 | 57.0 | 228 | 0.8087 | 0.9219 |
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- | 0.0 | 58.0 | 232 | 0.8083 | 0.9141 |
111
- | 0.0 | 59.0 | 236 | 0.8168 | 0.9141 |
112
- | 0.0002 | 60.0 | 240 | 0.8424 | 0.9141 |
113
- | 0.0002 | 61.0 | 244 | 0.8614 | 0.9141 |
114
- | 0.0002 | 62.0 | 248 | 0.8736 | 0.9141 |
115
- | 0.0 | 63.0 | 252 | 0.8817 | 0.9141 |
116
- | 0.0 | 64.0 | 256 | 0.8848 | 0.9141 |
117
- | 0.0 | 65.0 | 260 | 0.8876 | 0.9141 |
118
- | 0.0 | 66.0 | 264 | 0.8896 | 0.9141 |
119
- | 0.0 | 67.0 | 268 | 0.8868 | 0.9141 |
120
- | 0.0 | 68.0 | 272 | 0.8831 | 0.9141 |
121
- | 0.0 | 69.0 | 276 | 0.8792 | 0.9141 |
122
- | 0.0001 | 70.0 | 280 | 0.8107 | 0.9141 |
123
- | 0.0001 | 71.0 | 284 | 0.9166 | 0.9219 |
124
- | 0.0001 | 72.0 | 288 | 0.8786 | 0.9219 |
125
- | 0.0232 | 73.0 | 292 | 0.8429 | 0.9219 |
126
- | 0.0232 | 74.0 | 296 | 0.8228 | 0.9297 |
127
- | 0.0 | 75.0 | 300 | 0.8332 | 0.9219 |
128
- | 0.0 | 76.0 | 304 | 0.8651 | 0.9062 |
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- | 0.0 | 77.0 | 308 | 0.8879 | 0.9062 |
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- | 0.0 | 78.0 | 312 | 0.9017 | 0.9062 |
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- | 0.0 | 79.0 | 316 | 0.9093 | 0.9062 |
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- | 0.0 | 80.0 | 320 | 0.9133 | 0.9062 |
133
- | 0.0 | 81.0 | 324 | 0.9160 | 0.9062 |
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- | 0.0 | 82.0 | 328 | 0.9180 | 0.9062 |
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- | 0.0 | 83.0 | 332 | 0.9192 | 0.9062 |
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- | 0.0 | 84.0 | 336 | 0.9196 | 0.9062 |
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- | 0.0 | 85.0 | 340 | 0.9209 | 0.9062 |
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- | 0.0 | 86.0 | 344 | 0.9250 | 0.9062 |
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- | 0.0 | 87.0 | 348 | 0.9289 | 0.9062 |
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- | 0.0 | 88.0 | 352 | 0.9314 | 0.9062 |
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- | 0.0 | 89.0 | 356 | 0.9330 | 0.9062 |
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- | 0.0 | 90.0 | 360 | 0.9340 | 0.9062 |
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- | 0.0 | 91.0 | 364 | 0.9346 | 0.9062 |
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- | 0.0 | 92.0 | 368 | 0.9348 | 0.9062 |
145
- | 0.0 | 93.0 | 372 | 0.9351 | 0.9062 |
146
- | 0.0 | 94.0 | 376 | 0.9354 | 0.9062 |
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- | 0.0 | 95.0 | 380 | 0.9355 | 0.9062 |
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- | 0.0 | 96.0 | 384 | 0.9354 | 0.9062 |
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- | 0.0 | 97.0 | 388 | 0.9339 | 0.9062 |
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- | 0.0 | 98.0 | 392 | 0.9310 | 0.9062 |
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- | 0.0 | 99.0 | 396 | 0.9290 | 0.9062 |
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- | 0.0 | 100.0 | 400 | 0.9276 | 0.9062 |
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- | 0.0 | 101.0 | 404 | 0.9271 | 0.9062 |
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- | 0.0 | 102.0 | 408 | 0.9274 | 0.9062 |
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- | 0.0 | 103.0 | 412 | 0.9277 | 0.9062 |
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- | 0.0 | 104.0 | 416 | 0.9282 | 0.9062 |
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- | 0.0 | 105.0 | 420 | 0.9285 | 0.9062 |
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- | 0.0 | 106.0 | 424 | 0.9289 | 0.9062 |
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- | 0.0 | 107.0 | 428 | 0.9293 | 0.9062 |
160
- | 0.0 | 108.0 | 432 | 0.9297 | 0.9062 |
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- | 0.0 | 109.0 | 436 | 0.9296 | 0.9062 |
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- | 0.0 | 110.0 | 440 | 0.9297 | 0.9062 |
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- | 0.0 | 111.0 | 444 | 0.9328 | 0.9062 |
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- | 0.0 | 112.0 | 448 | 0.9376 | 0.9062 |
165
- | 0.0 | 113.0 | 452 | 0.9408 | 0.9062 |
166
- | 0.0 | 114.0 | 456 | 0.9428 | 0.9062 |
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- | 0.0 | 115.0 | 460 | 0.9442 | 0.9062 |
168
- | 0.0 | 116.0 | 464 | 0.9455 | 0.9062 |
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- | 0.0 | 117.0 | 468 | 0.9464 | 0.9062 |
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- | 0.0 | 118.0 | 472 | 0.9470 | 0.9062 |
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- | 0.0 | 119.0 | 476 | 0.9478 | 0.9062 |
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- | 0.0 | 120.0 | 480 | 0.9487 | 0.9062 |
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- | 0.0 | 121.0 | 484 | 0.9492 | 0.9062 |
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- | 0.0 | 122.0 | 488 | 0.9496 | 0.9062 |
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- | 0.0 | 123.0 | 492 | 0.9499 | 0.9062 |
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- | 0.0 | 124.0 | 496 | 0.9504 | 0.9062 |
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- | 0.0 | 125.0 | 500 | 0.9505 | 0.9062 |
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- | 0.0 | 126.0 | 504 | 0.9507 | 0.9062 |
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- | 0.0 | 127.0 | 508 | 0.9509 | 0.9062 |
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- | 0.0 | 128.0 | 512 | 0.9504 | 0.9062 |
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- | 0.0 | 129.0 | 516 | 0.9502 | 0.9062 |
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- | 0.0 | 130.0 | 520 | 0.9500 | 0.9062 |
183
- | 0.0 | 131.0 | 524 | 0.9497 | 0.9062 |
184
- | 0.0 | 132.0 | 528 | 0.9496 | 0.9062 |
185
- | 0.0 | 133.0 | 532 | 0.9496 | 0.9062 |
186
- | 0.0 | 134.0 | 536 | 0.9498 | 0.9062 |
187
- | 0.0 | 135.0 | 540 | 0.9502 | 0.9062 |
188
- | 0.0 | 136.0 | 544 | 0.9398 | 0.9062 |
189
- | 0.0 | 137.0 | 548 | 0.9199 | 0.9062 |
190
- | 0.0 | 138.0 | 552 | 0.9047 | 0.9062 |
191
- | 0.0 | 139.0 | 556 | 0.8950 | 0.9141 |
192
- | 0.0 | 140.0 | 560 | 0.8894 | 0.9141 |
193
- | 0.0 | 141.0 | 564 | 0.8862 | 0.9141 |
194
- | 0.0 | 142.0 | 568 | 0.8846 | 0.9141 |
195
- | 0.0 | 143.0 | 572 | 0.8840 | 0.9141 |
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- | 0.0 | 144.0 | 576 | 0.8837 | 0.9141 |
197
- | 0.0 | 145.0 | 580 | 0.8836 | 0.9141 |
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- | 0.0 | 146.0 | 584 | 0.8836 | 0.9141 |
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- | 0.0 | 147.0 | 588 | 0.8837 | 0.9141 |
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- | 0.0 | 148.0 | 592 | 0.8838 | 0.9141 |
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- | 0.0 | 149.0 | 596 | 0.8838 | 0.9141 |
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- | 0.0 | 150.0 | 600 | 0.8838 | 0.9141 |
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  ### 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-64-42
17
 
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+ 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: 0.6069
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+ - Accuracy: 0.9375
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  ## Model description
24
 
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 4 | 0.7342 | 0.9219 |
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+ | No log | 2.0 | 8 | 0.7290 | 0.9219 |
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+ | 0.5102 | 3.0 | 12 | 0.7270 | 0.9219 |
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+ | 0.5102 | 4.0 | 16 | 0.7253 | 0.9219 |
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+ | 0.4089 | 5.0 | 20 | 0.7208 | 0.9219 |
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+ | 0.4089 | 6.0 | 24 | 0.7191 | 0.9219 |
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+ | 0.4089 | 7.0 | 28 | 0.7271 | 0.9297 |
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+ | 0.3981 | 8.0 | 32 | 0.7192 | 0.9297 |
61
+ | 0.3981 | 9.0 | 36 | 0.7009 | 0.9219 |
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+ | 0.1982 | 10.0 | 40 | 0.6963 | 0.9141 |
63
+ | 0.1982 | 11.0 | 44 | 0.6904 | 0.9219 |
64
+ | 0.1982 | 12.0 | 48 | 0.6924 | 0.9219 |
65
+ | 0.2128 | 13.0 | 52 | 0.6921 | 0.9297 |
66
+ | 0.2128 | 14.0 | 56 | 0.6866 | 0.9219 |
67
+ | 0.0935 | 15.0 | 60 | 0.6841 | 0.9219 |
68
+ | 0.0935 | 16.0 | 64 | 0.6494 | 0.9219 |
69
+ | 0.0935 | 17.0 | 68 | 0.6201 | 0.9219 |
70
+ | 0.0365 | 18.0 | 72 | 0.6122 | 0.9219 |
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+ | 0.0365 | 19.0 | 76 | 0.6047 | 0.9219 |
72
+ | 0.026 | 20.0 | 80 | 0.5870 | 0.9219 |
73
+ | 0.026 | 21.0 | 84 | 0.5739 | 0.9219 |
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+ | 0.026 | 22.0 | 88 | 0.5737 | 0.9219 |
75
+ | 0.0139 | 23.0 | 92 | 0.5677 | 0.9219 |
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+ | 0.0139 | 24.0 | 96 | 0.5579 | 0.9219 |
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+ | 0.0149 | 25.0 | 100 | 0.5468 | 0.9219 |
78
+ | 0.0149 | 26.0 | 104 | 0.5277 | 0.9219 |
79
+ | 0.0149 | 27.0 | 108 | 0.5168 | 0.9219 |
80
+ | 0.0085 | 28.0 | 112 | 0.5036 | 0.9141 |
81
+ | 0.0085 | 29.0 | 116 | 0.4960 | 0.9141 |
82
+ | 0.0 | 30.0 | 120 | 0.4941 | 0.9219 |
83
+ | 0.0 | 31.0 | 124 | 0.4956 | 0.9297 |
84
+ | 0.0 | 32.0 | 128 | 0.4987 | 0.9297 |
85
+ | 0.0 | 33.0 | 132 | 0.5018 | 0.9297 |
86
+ | 0.0 | 34.0 | 136 | 0.5053 | 0.9297 |
87
+ | 0.0 | 35.0 | 140 | 0.5081 | 0.9297 |
88
+ | 0.0 | 36.0 | 144 | 0.5107 | 0.9297 |
89
+ | 0.0 | 37.0 | 148 | 0.5125 | 0.9297 |
90
+ | 0.0 | 38.0 | 152 | 0.5135 | 0.9297 |
91
+ | 0.0 | 39.0 | 156 | 0.5146 | 0.9297 |
92
+ | 0.0 | 40.0 | 160 | 0.5157 | 0.9297 |
93
+ | 0.0 | 41.0 | 164 | 0.5168 | 0.9297 |
94
+ | 0.0 | 42.0 | 168 | 0.5182 | 0.9297 |
95
+ | 0.0 | 43.0 | 172 | 0.5197 | 0.9297 |
96
+ | 0.0 | 44.0 | 176 | 0.5209 | 0.9297 |
97
+ | 0.0 | 45.0 | 180 | 0.5224 | 0.9297 |
98
+ | 0.0 | 46.0 | 184 | 0.5240 | 0.9297 |
99
+ | 0.0 | 47.0 | 188 | 0.5257 | 0.9297 |
100
+ | 0.0 | 48.0 | 192 | 0.5272 | 0.9297 |
101
+ | 0.0 | 49.0 | 196 | 0.5286 | 0.9297 |
102
+ | 0.0 | 50.0 | 200 | 0.5300 | 0.9297 |
103
+ | 0.0 | 51.0 | 204 | 0.5313 | 0.9297 |
104
+ | 0.0 | 52.0 | 208 | 0.5329 | 0.9297 |
105
+ | 0.0 | 53.0 | 212 | 0.5343 | 0.9297 |
106
+ | 0.0 | 54.0 | 216 | 0.5355 | 0.9297 |
107
+ | 0.0 | 55.0 | 220 | 0.5369 | 0.9297 |
108
+ | 0.0 | 56.0 | 224 | 0.5382 | 0.9297 |
109
+ | 0.0 | 57.0 | 228 | 0.5395 | 0.9297 |
110
+ | 0.0 | 58.0 | 232 | 0.5407 | 0.9297 |
111
+ | 0.0 | 59.0 | 236 | 0.5419 | 0.9297 |
112
+ | 0.0 | 60.0 | 240 | 0.5431 | 0.9297 |
113
+ | 0.0 | 61.0 | 244 | 0.5444 | 0.9297 |
114
+ | 0.0 | 62.0 | 248 | 0.5455 | 0.9297 |
115
+ | 0.0 | 63.0 | 252 | 0.5466 | 0.9297 |
116
+ | 0.0 | 64.0 | 256 | 0.5478 | 0.9297 |
117
+ | 0.0 | 65.0 | 260 | 0.5489 | 0.9297 |
118
+ | 0.0 | 66.0 | 264 | 0.5501 | 0.9297 |
119
+ | 0.0 | 67.0 | 268 | 0.5513 | 0.9297 |
120
+ | 0.0 | 68.0 | 272 | 0.5524 | 0.9297 |
121
+ | 0.0 | 69.0 | 276 | 0.5535 | 0.9297 |
122
+ | 0.0 | 70.0 | 280 | 0.5548 | 0.9297 |
123
+ | 0.0 | 71.0 | 284 | 0.5559 | 0.9297 |
124
+ | 0.0 | 72.0 | 288 | 0.5570 | 0.9297 |
125
+ | 0.0 | 73.0 | 292 | 0.5581 | 0.9297 |
126
+ | 0.0 | 74.0 | 296 | 0.5592 | 0.9297 |
127
+ | 0.0 | 75.0 | 300 | 0.5601 | 0.9297 |
128
+ | 0.0 | 76.0 | 304 | 0.5610 | 0.9297 |
129
+ | 0.0 | 77.0 | 308 | 0.5620 | 0.9297 |
130
+ | 0.0 | 78.0 | 312 | 0.5630 | 0.9297 |
131
+ | 0.0 | 79.0 | 316 | 0.5640 | 0.9297 |
132
+ | 0.0 | 80.0 | 320 | 0.5648 | 0.9297 |
133
+ | 0.0 | 81.0 | 324 | 0.5658 | 0.9297 |
134
+ | 0.0 | 82.0 | 328 | 0.5667 | 0.9297 |
135
+ | 0.0 | 83.0 | 332 | 0.5675 | 0.9297 |
136
+ | 0.0 | 84.0 | 336 | 0.5684 | 0.9297 |
137
+ | 0.0 | 85.0 | 340 | 0.5693 | 0.9297 |
138
+ | 0.0 | 86.0 | 344 | 0.5701 | 0.9297 |
139
+ | 0.0 | 87.0 | 348 | 0.5710 | 0.9297 |
140
+ | 0.0 | 88.0 | 352 | 0.5719 | 0.9297 |
141
+ | 0.0 | 89.0 | 356 | 0.5728 | 0.9297 |
142
+ | 0.0 | 90.0 | 360 | 0.5736 | 0.9297 |
143
+ | 0.0 | 91.0 | 364 | 0.5745 | 0.9297 |
144
+ | 0.0 | 92.0 | 368 | 0.5754 | 0.9297 |
145
+ | 0.0 | 93.0 | 372 | 0.5762 | 0.9297 |
146
+ | 0.0 | 94.0 | 376 | 0.5771 | 0.9297 |
147
+ | 0.0 | 95.0 | 380 | 0.5779 | 0.9297 |
148
+ | 0.0 | 96.0 | 384 | 0.5788 | 0.9297 |
149
+ | 0.0 | 97.0 | 388 | 0.5796 | 0.9297 |
150
+ | 0.0 | 98.0 | 392 | 0.5804 | 0.9297 |
151
+ | 0.0 | 99.0 | 396 | 0.5812 | 0.9297 |
152
+ | 0.0 | 100.0 | 400 | 0.5820 | 0.9297 |
153
+ | 0.0 | 101.0 | 404 | 0.5828 | 0.9297 |
154
+ | 0.0 | 102.0 | 408 | 0.5836 | 0.9297 |
155
+ | 0.0 | 103.0 | 412 | 0.5843 | 0.9297 |
156
+ | 0.0 | 104.0 | 416 | 0.5851 | 0.9297 |
157
+ | 0.0 | 105.0 | 420 | 0.5859 | 0.9297 |
158
+ | 0.0 | 106.0 | 424 | 0.5866 | 0.9297 |
159
+ | 0.0 | 107.0 | 428 | 0.5874 | 0.9297 |
160
+ | 0.0 | 108.0 | 432 | 0.5881 | 0.9297 |
161
+ | 0.0 | 109.0 | 436 | 0.5889 | 0.9297 |
162
+ | 0.0 | 110.0 | 440 | 0.5896 | 0.9297 |
163
+ | 0.0 | 111.0 | 444 | 0.5902 | 0.9297 |
164
+ | 0.0 | 112.0 | 448 | 0.5910 | 0.9375 |
165
+ | 0.0 | 113.0 | 452 | 0.5916 | 0.9375 |
166
+ | 0.0 | 114.0 | 456 | 0.5924 | 0.9375 |
167
+ | 0.0 | 115.0 | 460 | 0.5931 | 0.9375 |
168
+ | 0.0 | 116.0 | 464 | 0.5938 | 0.9375 |
169
+ | 0.0 | 117.0 | 468 | 0.5945 | 0.9375 |
170
+ | 0.0 | 118.0 | 472 | 0.5952 | 0.9375 |
171
+ | 0.0 | 119.0 | 476 | 0.5958 | 0.9375 |
172
+ | 0.0 | 120.0 | 480 | 0.5964 | 0.9375 |
173
+ | 0.0 | 121.0 | 484 | 0.5971 | 0.9375 |
174
+ | 0.0 | 122.0 | 488 | 0.5978 | 0.9375 |
175
+ | 0.0 | 123.0 | 492 | 0.5985 | 0.9375 |
176
+ | 0.0 | 124.0 | 496 | 0.5991 | 0.9375 |
177
+ | 0.0 | 125.0 | 500 | 0.5997 | 0.9375 |
178
+ | 0.0 | 126.0 | 504 | 0.6004 | 0.9375 |
179
+ | 0.0 | 127.0 | 508 | 0.6009 | 0.9375 |
180
+ | 0.0 | 128.0 | 512 | 0.6015 | 0.9375 |
181
+ | 0.0 | 129.0 | 516 | 0.6020 | 0.9375 |
182
+ | 0.0 | 130.0 | 520 | 0.6025 | 0.9375 |
183
+ | 0.0 | 131.0 | 524 | 0.6029 | 0.9375 |
184
+ | 0.0 | 132.0 | 528 | 0.6034 | 0.9375 |
185
+ | 0.0 | 133.0 | 532 | 0.6038 | 0.9375 |
186
+ | 0.0 | 134.0 | 536 | 0.6042 | 0.9375 |
187
+ | 0.0 | 135.0 | 540 | 0.6045 | 0.9375 |
188
+ | 0.0 | 136.0 | 544 | 0.6048 | 0.9375 |
189
+ | 0.0 | 137.0 | 548 | 0.6051 | 0.9375 |
190
+ | 0.0 | 138.0 | 552 | 0.6054 | 0.9375 |
191
+ | 0.0 | 139.0 | 556 | 0.6056 | 0.9375 |
192
+ | 0.0 | 140.0 | 560 | 0.6058 | 0.9375 |
193
+ | 0.0 | 141.0 | 564 | 0.6061 | 0.9375 |
194
+ | 0.0 | 142.0 | 568 | 0.6062 | 0.9375 |
195
+ | 0.0 | 143.0 | 572 | 0.6064 | 0.9375 |
196
+ | 0.0 | 144.0 | 576 | 0.6065 | 0.9375 |
197
+ | 0.0 | 145.0 | 580 | 0.6066 | 0.9375 |
198
+ | 0.0 | 146.0 | 584 | 0.6067 | 0.9375 |
199
+ | 0.0 | 147.0 | 588 | 0.6068 | 0.9375 |
200
+ | 0.0 | 148.0 | 592 | 0.6068 | 0.9375 |
201
+ | 0.0 | 149.0 | 596 | 0.6069 | 0.9375 |
202
+ | 0.0 | 150.0 | 600 | 0.6069 | 0.9375 |
203
 
204
 
205
  ### Framework versions