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@@ -1,26 +1,22 @@
1
  ---
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- language:
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- - en
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- license: mit
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  base_model: microsoft/speecht5_tts
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  tags:
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- - en_accent,mozilla,t5,common_voice_1_0
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  - generated_from_trainer
9
  datasets:
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- - mozilla-foundation/common_voice_1_0
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  model-index:
12
- - name: SpeechT5 TTS English Accented
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  results: []
14
  ---
15
 
16
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
17
  should probably proofread and complete it, then remove this comment. -->
18
 
19
- # SpeechT5 TTS English Accented
20
 
21
- This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Common Voice dataset.
22
  It achieves the following results on the evaluation set:
23
- - Loss: 0.5985
24
 
25
  ## Model description
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@@ -46,173 +42,333 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 500
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- - training_steps: 40000
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  - mixed_precision_training: Native AMP
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52
  ### Training results
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54
- | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:-----:|:-----:|:---------------:|
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- | No log | 2.5 | 250 | 0.7148 |
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- | 0.8008 | 5.0 | 500 | 0.5602 |
58
- | 0.8008 | 7.5 | 750 | 0.5386 |
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- | 0.5966 | 10.0 | 1000 | 0.5309 |
60
- | 0.5966 | 12.5 | 1250 | 0.5260 |
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- | 0.5633 | 15.0 | 1500 | 0.5284 |
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- | 0.5633 | 17.5 | 1750 | 0.5258 |
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- | 0.546 | 20.0 | 2000 | 0.5285 |
64
- | 0.546 | 22.5 | 2250 | 0.5295 |
65
- | 0.5272 | 25.0 | 2500 | 0.5311 |
66
- | 0.5272 | 27.5 | 2750 | 0.5281 |
67
- | 0.5214 | 30.0 | 3000 | 0.5295 |
68
- | 0.5214 | 32.5 | 3250 | 0.5414 |
69
- | 0.5168 | 35.0 | 3500 | 0.5299 |
70
- | 0.5168 | 37.5 | 3750 | 0.5359 |
71
- | 0.5072 | 40.0 | 4000 | 0.5336 |
72
- | 0.5072 | 42.5 | 4250 | 0.5305 |
73
- | 0.4997 | 45.0 | 4500 | 0.5321 |
74
- | 0.4997 | 47.5 | 4750 | 0.5410 |
75
- | 0.4964 | 50.0 | 5000 | 0.5436 |
76
- | 0.4964 | 52.5 | 5250 | 0.5374 |
77
- | 0.4921 | 55.0 | 5500 | 0.5451 |
78
- | 0.4921 | 57.5 | 5750 | 0.5517 |
79
- | 0.4861 | 60.0 | 6000 | 0.5454 |
80
- | 0.4861 | 62.5 | 6250 | 0.5454 |
81
- | 0.4855 | 65.0 | 6500 | 0.5440 |
82
- | 0.4855 | 67.5 | 6750 | 0.5463 |
83
- | 0.4801 | 70.0 | 7000 | 0.5450 |
84
- | 0.4801 | 72.5 | 7250 | 0.5479 |
85
- | 0.4811 | 75.0 | 7500 | 0.5450 |
86
- | 0.4811 | 77.5 | 7750 | 0.5483 |
87
- | 0.4769 | 80.0 | 8000 | 0.5509 |
88
- | 0.4769 | 82.5 | 8250 | 0.5462 |
89
- | 0.4759 | 85.0 | 8500 | 0.5538 |
90
- | 0.4759 | 87.5 | 8750 | 0.5521 |
91
- | 0.4712 | 90.0 | 9000 | 0.5535 |
92
- | 0.4712 | 92.5 | 9250 | 0.5533 |
93
- | 0.47 | 95.0 | 9500 | 0.5576 |
94
- | 0.47 | 97.5 | 9750 | 0.5560 |
95
- | 0.4686 | 100.0 | 10000 | 0.5533 |
96
- | 0.4686 | 102.5 | 10250 | 0.5531 |
97
- | 0.4641 | 105.0 | 10500 | 0.5557 |
98
- | 0.4641 | 107.5 | 10750 | 0.5581 |
99
- | 0.4693 | 110.0 | 11000 | 0.5586 |
100
- | 0.4693 | 112.5 | 11250 | 0.5685 |
101
- | 0.463 | 115.0 | 11500 | 0.5621 |
102
- | 0.463 | 117.5 | 11750 | 0.5601 |
103
- | 0.4638 | 120.0 | 12000 | 0.5592 |
104
- | 0.4638 | 122.5 | 12250 | 0.5654 |
105
- | 0.4591 | 125.0 | 12500 | 0.5595 |
106
- | 0.4591 | 127.5 | 12750 | 0.5649 |
107
- | 0.464 | 130.0 | 13000 | 0.5676 |
108
- | 0.464 | 132.5 | 13250 | 0.5600 |
109
- | 0.4556 | 135.0 | 13500 | 0.5627 |
110
- | 0.4556 | 137.5 | 13750 | 0.5626 |
111
- | 0.4574 | 140.0 | 14000 | 0.5703 |
112
- | 0.4574 | 142.5 | 14250 | 0.5715 |
113
- | 0.4583 | 145.0 | 14500 | 0.5638 |
114
- | 0.4583 | 147.5 | 14750 | 0.5681 |
115
- | 0.4505 | 150.0 | 15000 | 0.5683 |
116
- | 0.4505 | 152.5 | 15250 | 0.5661 |
117
- | 0.447 | 155.0 | 15500 | 0.5571 |
118
- | 0.447 | 157.5 | 15750 | 0.5688 |
119
- | 0.4484 | 160.0 | 16000 | 0.5702 |
120
- | 0.4484 | 162.5 | 16250 | 0.5635 |
121
- | 0.4468 | 165.0 | 16500 | 0.5716 |
122
- | 0.4468 | 167.5 | 16750 | 0.5703 |
123
- | 0.4439 | 170.0 | 17000 | 0.5708 |
124
- | 0.4439 | 172.5 | 17250 | 0.5676 |
125
- | 0.4453 | 175.0 | 17500 | 0.5689 |
126
- | 0.4453 | 177.5 | 17750 | 0.5691 |
127
- | 0.4434 | 180.0 | 18000 | 0.5716 |
128
- | 0.4434 | 182.5 | 18250 | 0.5784 |
129
- | 0.4468 | 185.0 | 18500 | 0.5729 |
130
- | 0.4468 | 187.5 | 18750 | 0.5737 |
131
- | 0.4443 | 190.0 | 19000 | 0.5701 |
132
- | 0.4443 | 192.5 | 19250 | 0.5734 |
133
- | 0.4404 | 195.0 | 19500 | 0.5783 |
134
- | 0.4404 | 197.5 | 19750 | 0.5743 |
135
- | 0.4422 | 200.0 | 20000 | 0.5740 |
136
- | 0.4422 | 202.5 | 20250 | 0.5764 |
137
- | 0.4413 | 205.0 | 20500 | 0.5770 |
138
- | 0.4413 | 207.5 | 20750 | 0.5808 |
139
- | 0.4407 | 210.0 | 21000 | 0.5752 |
140
- | 0.4407 | 212.5 | 21250 | 0.5793 |
141
- | 0.4392 | 215.0 | 21500 | 0.5748 |
142
- | 0.4392 | 217.5 | 21750 | 0.5817 |
143
- | 0.437 | 220.0 | 22000 | 0.5762 |
144
- | 0.437 | 222.5 | 22250 | 0.5808 |
145
- | 0.4358 | 225.0 | 22500 | 0.5744 |
146
- | 0.4358 | 227.5 | 22750 | 0.5820 |
147
- | 0.4382 | 230.0 | 23000 | 0.5754 |
148
- | 0.4382 | 232.5 | 23250 | 0.5810 |
149
- | 0.4381 | 235.0 | 23500 | 0.5770 |
150
- | 0.4381 | 237.5 | 23750 | 0.5824 |
151
- | 0.4361 | 240.0 | 24000 | 0.5797 |
152
- | 0.4361 | 242.5 | 24250 | 0.5821 |
153
- | 0.4389 | 245.0 | 24500 | 0.5870 |
154
- | 0.4389 | 247.5 | 24750 | 0.5805 |
155
- | 0.4363 | 250.0 | 25000 | 0.5809 |
156
- | 0.4363 | 252.5 | 25250 | 0.5833 |
157
- | 0.4338 | 255.0 | 25500 | 0.5847 |
158
- | 0.4338 | 257.5 | 25750 | 0.5845 |
159
- | 0.4321 | 260.0 | 26000 | 0.5848 |
160
- | 0.4321 | 262.5 | 26250 | 0.5874 |
161
- | 0.4323 | 265.0 | 26500 | 0.5866 |
162
- | 0.4323 | 267.5 | 26750 | 0.5853 |
163
- | 0.431 | 270.0 | 27000 | 0.5860 |
164
- | 0.431 | 272.5 | 27250 | 0.5893 |
165
- | 0.429 | 275.0 | 27500 | 0.5872 |
166
- | 0.429 | 277.5 | 27750 | 0.5888 |
167
- | 0.4302 | 280.0 | 28000 | 0.5861 |
168
- | 0.4302 | 282.5 | 28250 | 0.5871 |
169
- | 0.4289 | 285.0 | 28500 | 0.5890 |
170
- | 0.4289 | 287.5 | 28750 | 0.5931 |
171
- | 0.4329 | 290.0 | 29000 | 0.5864 |
172
- | 0.4329 | 292.5 | 29250 | 0.5861 |
173
- | 0.4285 | 295.0 | 29500 | 0.5871 |
174
- | 0.4285 | 297.5 | 29750 | 0.5872 |
175
- | 0.4301 | 300.0 | 30000 | 0.5902 |
176
- | 0.4301 | 302.5 | 30250 | 0.5915 |
177
- | 0.431 | 305.0 | 30500 | 0.5919 |
178
- | 0.431 | 307.5 | 30750 | 0.5928 |
179
- | 0.429 | 310.0 | 31000 | 0.5881 |
180
- | 0.429 | 312.5 | 31250 | 0.5930 |
181
- | 0.4266 | 315.0 | 31500 | 0.5935 |
182
- | 0.4266 | 317.5 | 31750 | 0.5945 |
183
- | 0.4302 | 320.0 | 32000 | 0.5910 |
184
- | 0.4302 | 322.5 | 32250 | 0.5923 |
185
- | 0.4247 | 325.0 | 32500 | 0.5933 |
186
- | 0.4247 | 327.5 | 32750 | 0.5938 |
187
- | 0.428 | 330.0 | 33000 | 0.5929 |
188
- | 0.428 | 332.5 | 33250 | 0.5904 |
189
- | 0.4279 | 335.0 | 33500 | 0.5956 |
190
- | 0.4279 | 337.5 | 33750 | 0.5978 |
191
- | 0.4262 | 340.0 | 34000 | 0.5909 |
192
- | 0.4262 | 342.5 | 34250 | 0.5952 |
193
- | 0.4242 | 345.0 | 34500 | 0.5944 |
194
- | 0.4242 | 347.5 | 34750 | 0.5979 |
195
- | 0.4304 | 350.0 | 35000 | 0.5976 |
196
- | 0.4304 | 352.5 | 35250 | 0.5971 |
197
- | 0.4274 | 355.0 | 35500 | 0.5957 |
198
- | 0.4274 | 357.5 | 35750 | 0.5948 |
199
- | 0.4284 | 360.0 | 36000 | 0.5964 |
200
- | 0.4284 | 362.5 | 36250 | 0.5961 |
201
- | 0.4247 | 365.0 | 36500 | 0.5961 |
202
- | 0.4247 | 367.5 | 36750 | 0.5979 |
203
- | 0.4286 | 370.0 | 37000 | 0.5948 |
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- | 0.4286 | 372.5 | 37250 | 0.5965 |
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- | 0.4244 | 375.0 | 37500 | 0.5967 |
206
- | 0.4244 | 377.5 | 37750 | 0.5957 |
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- | 0.4217 | 380.0 | 38000 | 0.5970 |
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- | 0.4217 | 382.5 | 38250 | 0.5983 |
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- | 0.4229 | 385.0 | 38500 | 0.6002 |
210
- | 0.4229 | 387.5 | 38750 | 0.5975 |
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- | 0.4236 | 390.0 | 39000 | 0.6006 |
212
- | 0.4236 | 392.5 | 39250 | 0.5985 |
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- | 0.4243 | 395.0 | 39500 | 0.5980 |
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- | 0.4243 | 397.5 | 39750 | 0.5985 |
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- | 0.4219 | 400.0 | 40000 | 0.5985 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
216
 
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  ### Framework versions
 
1
  ---
 
 
 
2
  base_model: microsoft/speecht5_tts
3
  tags:
 
4
  - generated_from_trainer
5
  datasets:
6
+ - common_voice_13_0
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  model-index:
8
+ - name: speecht5_tts
9
  results: []
10
  ---
11
 
12
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
  should probably proofread and complete it, then remove this comment. -->
14
 
15
+ # speecht5_tts
16
 
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+ This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 0.5888
20
 
21
  ## Model description
22
 
 
42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
43
  - lr_scheduler_type: linear
44
  - lr_scheduler_warmup_steps: 500
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+ - training_steps: 80000
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  - mixed_precision_training: Native AMP
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48
  ### Training results
49
 
50
+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:-----:|:---------------:|
52
+ | No log | 1.25 | 250 | 0.6821 |
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+ | 0.8074 | 2.5 | 500 | 0.5618 |
54
+ | 0.8074 | 3.75 | 750 | 0.5362 |
55
+ | 0.5957 | 5.0 | 1000 | 0.5279 |
56
+ | 0.5957 | 6.25 | 1250 | 0.5183 |
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+ | 0.567 | 7.5 | 1500 | 0.5216 |
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+ | 0.567 | 8.75 | 1750 | 0.5096 |
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+ | 0.5507 | 10.0 | 2000 | 0.5097 |
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+ | 0.5507 | 11.25 | 2250 | 0.5111 |
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+ | 0.5396 | 12.5 | 2500 | 0.5090 |
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+ | 0.5396 | 13.75 | 2750 | 0.5072 |
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+ | 0.5348 | 15.0 | 3000 | 0.5099 |
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+ | 0.5348 | 16.25 | 3250 | 0.5098 |
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+ | 0.5269 | 17.5 | 3500 | 0.5094 |
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+ | 0.5269 | 18.75 | 3750 | 0.5081 |
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+ | 0.5209 | 20.0 | 4000 | 0.5088 |
68
+ | 0.5209 | 21.25 | 4250 | 0.5100 |
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+ | 0.5176 | 22.5 | 4500 | 0.5084 |
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+ | 0.5176 | 23.75 | 4750 | 0.5107 |
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+ | 0.5129 | 25.0 | 5000 | 0.5131 |
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+ | 0.5129 | 26.25 | 5250 | 0.5205 |
73
+ | 0.5081 | 27.5 | 5500 | 0.5174 |
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+ | 0.5081 | 28.75 | 5750 | 0.5131 |
75
+ | 0.5033 | 30.0 | 6000 | 0.5127 |
76
+ | 0.5033 | 31.25 | 6250 | 0.5272 |
77
+ | 0.505 | 32.5 | 6500 | 0.5208 |
78
+ | 0.505 | 33.75 | 6750 | 0.5263 |
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+ | 0.4933 | 35.0 | 7000 | 0.5257 |
80
+ | 0.4933 | 36.25 | 7250 | 0.5270 |
81
+ | 0.4929 | 37.5 | 7500 | 0.5240 |
82
+ | 0.4929 | 38.75 | 7750 | 0.5272 |
83
+ | 0.4942 | 40.0 | 8000 | 0.5266 |
84
+ | 0.4942 | 41.25 | 8250 | 0.5364 |
85
+ | 0.4883 | 42.5 | 8500 | 0.5339 |
86
+ | 0.4883 | 43.75 | 8750 | 0.5313 |
87
+ | 0.4874 | 45.0 | 9000 | 0.5335 |
88
+ | 0.4874 | 46.25 | 9250 | 0.5300 |
89
+ | 0.4849 | 47.5 | 9500 | 0.5357 |
90
+ | 0.4849 | 48.75 | 9750 | 0.5361 |
91
+ | 0.483 | 50.0 | 10000 | 0.5306 |
92
+ | 0.483 | 51.25 | 10250 | 0.5330 |
93
+ | 0.4812 | 52.5 | 10500 | 0.5234 |
94
+ | 0.4812 | 53.75 | 10750 | 0.5248 |
95
+ | 0.484 | 55.0 | 11000 | 0.5364 |
96
+ | 0.484 | 56.25 | 11250 | 0.5381 |
97
+ | 0.4786 | 57.5 | 11500 | 0.5340 |
98
+ | 0.4786 | 58.75 | 11750 | 0.5385 |
99
+ | 0.4794 | 60.0 | 12000 | 0.5365 |
100
+ | 0.4794 | 61.25 | 12250 | 0.5411 |
101
+ | 0.4719 | 62.5 | 12500 | 0.5358 |
102
+ | 0.4719 | 63.75 | 12750 | 0.5377 |
103
+ | 0.479 | 65.0 | 13000 | 0.5378 |
104
+ | 0.479 | 66.25 | 13250 | 0.5426 |
105
+ | 0.474 | 67.5 | 13500 | 0.5370 |
106
+ | 0.474 | 68.75 | 13750 | 0.5402 |
107
+ | 0.473 | 70.0 | 14000 | 0.5400 |
108
+ | 0.473 | 71.25 | 14250 | 0.5453 |
109
+ | 0.4717 | 72.5 | 14500 | 0.5453 |
110
+ | 0.4717 | 73.75 | 14750 | 0.5419 |
111
+ | 0.4663 | 75.0 | 15000 | 0.5407 |
112
+ | 0.4663 | 76.25 | 15250 | 0.5427 |
113
+ | 0.4631 | 77.5 | 15500 | 0.5408 |
114
+ | 0.4631 | 78.75 | 15750 | 0.5408 |
115
+ | 0.4665 | 80.0 | 16000 | 0.5400 |
116
+ | 0.4665 | 81.25 | 16250 | 0.5486 |
117
+ | 0.4658 | 82.5 | 16500 | 0.5429 |
118
+ | 0.4658 | 83.75 | 16750 | 0.5395 |
119
+ | 0.4657 | 85.0 | 17000 | 0.5361 |
120
+ | 0.4657 | 86.25 | 17250 | 0.5415 |
121
+ | 0.4647 | 87.5 | 17500 | 0.5464 |
122
+ | 0.4647 | 88.75 | 17750 | 0.5428 |
123
+ | 0.4646 | 90.0 | 18000 | 0.5412 |
124
+ | 0.4646 | 91.25 | 18250 | 0.5478 |
125
+ | 0.4649 | 92.5 | 18500 | 0.5479 |
126
+ | 0.4649 | 93.75 | 18750 | 0.5463 |
127
+ | 0.4622 | 95.0 | 19000 | 0.5447 |
128
+ | 0.4622 | 96.25 | 19250 | 0.5440 |
129
+ | 0.4598 | 97.5 | 19500 | 0.5524 |
130
+ | 0.4598 | 98.75 | 19750 | 0.5518 |
131
+ | 0.461 | 100.0 | 20000 | 0.5470 |
132
+ | 0.461 | 101.25 | 20250 | 0.5507 |
133
+ | 0.4608 | 102.5 | 20500 | 0.5486 |
134
+ | 0.4608 | 103.75 | 20750 | 0.5481 |
135
+ | 0.4565 | 105.0 | 21000 | 0.5509 |
136
+ | 0.4565 | 106.25 | 21250 | 0.5532 |
137
+ | 0.4561 | 107.5 | 21500 | 0.5488 |
138
+ | 0.4561 | 108.75 | 21750 | 0.5448 |
139
+ | 0.4577 | 110.0 | 22000 | 0.5492 |
140
+ | 0.4577 | 111.25 | 22250 | 0.5539 |
141
+ | 0.4545 | 112.5 | 22500 | 0.5497 |
142
+ | 0.4545 | 113.75 | 22750 | 0.5536 |
143
+ | 0.4548 | 115.0 | 23000 | 0.5497 |
144
+ | 0.4548 | 116.25 | 23250 | 0.5520 |
145
+ | 0.4555 | 117.5 | 23500 | 0.5445 |
146
+ | 0.4555 | 118.75 | 23750 | 0.5518 |
147
+ | 0.456 | 120.0 | 24000 | 0.5520 |
148
+ | 0.456 | 121.25 | 24250 | 0.5512 |
149
+ | 0.4526 | 122.5 | 24500 | 0.5516 |
150
+ | 0.4526 | 123.75 | 24750 | 0.5534 |
151
+ | 0.4528 | 125.0 | 25000 | 0.5524 |
152
+ | 0.4528 | 126.25 | 25250 | 0.5512 |
153
+ | 0.4506 | 127.5 | 25500 | 0.5530 |
154
+ | 0.4506 | 128.75 | 25750 | 0.5534 |
155
+ | 0.4512 | 130.0 | 26000 | 0.5528 |
156
+ | 0.4512 | 131.25 | 26250 | 0.5524 |
157
+ | 0.4504 | 132.5 | 26500 | 0.5569 |
158
+ | 0.4504 | 133.75 | 26750 | 0.5489 |
159
+ | 0.4472 | 135.0 | 27000 | 0.5530 |
160
+ | 0.4472 | 136.25 | 27250 | 0.5571 |
161
+ | 0.447 | 137.5 | 27500 | 0.5566 |
162
+ | 0.447 | 138.75 | 27750 | 0.5562 |
163
+ | 0.4465 | 140.0 | 28000 | 0.5546 |
164
+ | 0.4465 | 141.25 | 28250 | 0.5579 |
165
+ | 0.4455 | 142.5 | 28500 | 0.5557 |
166
+ | 0.4455 | 143.75 | 28750 | 0.5533 |
167
+ | 0.4487 | 145.0 | 29000 | 0.5528 |
168
+ | 0.4487 | 146.25 | 29250 | 0.5576 |
169
+ | 0.445 | 147.5 | 29500 | 0.5574 |
170
+ | 0.445 | 148.75 | 29750 | 0.5593 |
171
+ | 0.4455 | 150.0 | 30000 | 0.5579 |
172
+ | 0.4455 | 151.25 | 30250 | 0.5539 |
173
+ | 0.4467 | 152.5 | 30500 | 0.5551 |
174
+ | 0.4467 | 153.75 | 30750 | 0.5654 |
175
+ | 0.4448 | 155.0 | 31000 | 0.5555 |
176
+ | 0.4448 | 156.25 | 31250 | 0.5602 |
177
+ | 0.4438 | 157.5 | 31500 | 0.5595 |
178
+ | 0.4438 | 158.75 | 31750 | 0.5575 |
179
+ | 0.4426 | 160.0 | 32000 | 0.5592 |
180
+ | 0.4426 | 161.25 | 32250 | 0.5618 |
181
+ | 0.4451 | 162.5 | 32500 | 0.5628 |
182
+ | 0.4451 | 163.75 | 32750 | 0.5623 |
183
+ | 0.4406 | 165.0 | 33000 | 0.5583 |
184
+ | 0.4406 | 166.25 | 33250 | 0.5575 |
185
+ | 0.443 | 167.5 | 33500 | 0.5580 |
186
+ | 0.443 | 168.75 | 33750 | 0.5606 |
187
+ | 0.4423 | 170.0 | 34000 | 0.5575 |
188
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189
+ | 0.4379 | 172.5 | 34500 | 0.5660 |
190
+ | 0.4379 | 173.75 | 34750 | 0.5600 |
191
+ | 0.4424 | 175.0 | 35000 | 0.5624 |
192
+ | 0.4424 | 176.25 | 35250 | 0.5656 |
193
+ | 0.4414 | 177.5 | 35500 | 0.5653 |
194
+ | 0.4414 | 178.75 | 35750 | 0.5645 |
195
+ | 0.4401 | 180.0 | 36000 | 0.5608 |
196
+ | 0.4401 | 181.25 | 36250 | 0.5639 |
197
+ | 0.4374 | 182.5 | 36500 | 0.5659 |
198
+ | 0.4374 | 183.75 | 36750 | 0.5655 |
199
+ | 0.443 | 185.0 | 37000 | 0.5660 |
200
+ | 0.443 | 186.25 | 37250 | 0.5664 |
201
+ | 0.4406 | 187.5 | 37500 | 0.5676 |
202
+ | 0.4406 | 188.75 | 37750 | 0.5631 |
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
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210
+ | 0.4403 | 198.75 | 39750 | 0.5659 |
211
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212
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213
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214
+ | 0.4373 | 203.75 | 40750 | 0.5695 |
215
+ | 0.4362 | 205.0 | 41000 | 0.5658 |
216
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217
+ | 0.4354 | 207.5 | 41500 | 0.5665 |
218
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219
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220
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221
+ | 0.4334 | 212.5 | 42500 | 0.5690 |
222
+ | 0.4334 | 213.75 | 42750 | 0.5645 |
223
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224
+ | 0.436 | 216.25 | 43250 | 0.5696 |
225
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226
+ | 0.4373 | 218.75 | 43750 | 0.5698 |
227
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228
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229
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230
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231
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232
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233
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234
+ | 0.4319 | 228.75 | 45750 | 0.5709 |
235
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236
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237
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238
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239
+ | 0.4299 | 235.0 | 47000 | 0.5659 |
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
+ | 0.4348 | 243.75 | 48750 | 0.5762 |
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
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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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
+ | 0.4261 | 312.5 | 62500 | 0.5791 |
302
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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346
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347
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348
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349
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350
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351
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352
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353
+ | 0.4196 | 377.5 | 75500 | 0.5894 |
354
+ | 0.4196 | 378.75 | 75750 | 0.5866 |
355
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356
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357
+ | 0.4207 | 382.5 | 76500 | 0.5894 |
358
+ | 0.4207 | 383.75 | 76750 | 0.5880 |
359
+ | 0.423 | 385.0 | 77000 | 0.5864 |
360
+ | 0.423 | 386.25 | 77250 | 0.5896 |
361
+ | 0.4213 | 387.5 | 77500 | 0.5909 |
362
+ | 0.4213 | 388.75 | 77750 | 0.5886 |
363
+ | 0.4211 | 390.0 | 78000 | 0.5906 |
364
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365
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366
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367
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368
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369
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370
+ | 0.4211 | 398.75 | 79750 | 0.5902 |
371
+ | 0.4243 | 400.0 | 80000 | 0.5888 |
372
 
373
 
374
  ### Framework versions
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