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README.md CHANGED
@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6984
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- - Accuracy: 0.5149
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22
  ## Model description
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@@ -49,163 +49,241 @@ The following hyperparameters were used during training:
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50
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
51
  |:-------------:|:------:|:-----:|:---------------:|:--------:|
52
- | No log | 0 | 0 | 3.0628 | 0.0 |
53
- | 0.5974 | 0.0064 | 100 | 0.7177 | 0.5008 |
54
- | 0.5341 | 0.0128 | 200 | 0.7740 | 0.4994 |
55
- | 0.4853 | 0.0192 | 300 | 0.6970 | 0.4994 |
56
- | 0.6345 | 0.0256 | 400 | 0.7084 | 0.5522 |
57
- | 0.4794 | 0.032 | 500 | 0.7496 | 0.5008 |
58
- | 0.5218 | 0.0384 | 600 | 0.7013 | 0.5006 |
59
- | 0.5271 | 0.0448 | 700 | 0.7223 | 0.4994 |
60
- | 0.6744 | 0.0512 | 800 | 0.6951 | 0.5008 |
61
- | 0.6895 | 0.0576 | 900 | 0.7071 | 0.5008 |
62
- | 0.6529 | 0.064 | 1000 | 0.6953 | 0.4992 |
63
- | 0.6682 | 0.0704 | 1100 | 0.6988 | 0.5008 |
64
- | 0.9596 | 0.0768 | 1200 | 0.7253 | 0.4992 |
65
- | 0.5184 | 0.0832 | 1300 | 0.6986 | 0.4992 |
66
- | 1.0007 | 0.0896 | 1400 | 0.6968 | 0.4798 |
67
- | 0.6532 | 0.096 | 1500 | 0.7049 | 0.4992 |
68
- | 0.6375 | 0.1024 | 1600 | 0.7030 | 0.4992 |
69
- | 0.5504 | 0.1088 | 1700 | 0.7063 | 0.5008 |
70
- | 0.6267 | 0.1152 | 1800 | 0.7091 | 0.4992 |
71
- | 0.6315 | 0.1216 | 1900 | 0.6948 | 0.4992 |
72
- | 0.6739 | 0.128 | 2000 | 0.6976 | 0.4992 |
73
- | 0.6723 | 0.1344 | 2100 | 0.6992 | 0.5008 |
74
- | 0.6359 | 0.1408 | 2200 | 0.6945 | 0.5006 |
75
- | 0.6679 | 0.1472 | 2300 | 0.6963 | 0.4992 |
76
- | 0.6552 | 0.1536 | 2400 | 0.6961 | 0.4992 |
77
- | 0.5607 | 0.16 | 2500 | 0.7055 | 0.4992 |
78
- | 0.6788 | 0.1664 | 2600 | 0.6937 | 0.5008 |
79
- | 0.6415 | 0.1728 | 2700 | 0.7220 | 0.5008 |
80
- | 0.6445 | 0.1792 | 2800 | 0.6972 | 0.5008 |
81
- | 0.6322 | 0.1856 | 2900 | 0.6935 | 0.4912 |
82
- | 0.676 | 0.192 | 3000 | 0.6961 | 0.5008 |
83
- | 0.6744 | 0.1984 | 3100 | 0.6951 | 0.4992 |
84
- | 0.6803 | 0.2048 | 3200 | 0.6953 | 0.4992 |
85
- | 0.6607 | 0.2112 | 3300 | 0.6936 | 0.4992 |
86
- | 0.6363 | 0.2176 | 3400 | 0.6940 | 0.5008 |
87
- | 0.6888 | 0.224 | 3500 | 0.6939 | 0.5008 |
88
- | 0.6612 | 0.2304 | 3600 | 0.6975 | 0.5008 |
89
- | 0.6747 | 0.2368 | 3700 | 0.6971 | 0.5008 |
90
- | 0.6193 | 0.2432 | 3800 | 0.6949 | 0.5008 |
91
- | 0.6553 | 0.2496 | 3900 | 0.6932 | 0.5008 |
92
- | 0.6705 | 0.256 | 4000 | 0.7092 | 0.4992 |
93
- | 0.6693 | 0.2624 | 4100 | 0.6950 | 0.4992 |
94
- | 0.6606 | 0.2688 | 4200 | 0.6933 | 0.5007 |
95
- | 0.6811 | 0.2752 | 4300 | 0.6937 | 0.4992 |
96
- | 0.6288 | 0.2816 | 4400 | 0.6936 | 0.4992 |
97
- | 0.6844 | 0.288 | 4500 | 0.6932 | 0.5008 |
98
- | 0.6956 | 0.2944 | 4600 | 0.6934 | 0.4992 |
99
- | 0.6442 | 0.3008 | 4700 | 0.6938 | 0.5008 |
100
- | 0.6369 | 0.3072 | 4800 | 0.7019 | 0.5008 |
101
- | 0.666 | 0.3136 | 4900 | 0.6943 | 0.4992 |
102
- | 0.6734 | 0.32 | 5000 | 0.6958 | 0.4992 |
103
- | 0.6682 | 0.3264 | 5100 | 0.6941 | 0.4992 |
104
- | 0.6368 | 0.3328 | 5200 | 0.6979 | 0.4992 |
105
- | 0.6813 | 0.3392 | 5300 | 0.6966 | 0.4992 |
106
- | 0.6588 | 0.3456 | 5400 | 0.6936 | 0.4992 |
107
- | 0.6737 | 0.352 | 5500 | 0.6950 | 0.4992 |
108
- | 0.6444 | 0.3584 | 5600 | 0.6932 | 0.4992 |
109
- | 0.6535 | 0.3648 | 5700 | 0.6956 | 0.5008 |
110
- | 0.6403 | 0.3712 | 5800 | 0.6951 | 0.5008 |
111
- | 0.6727 | 0.3776 | 5900 | 0.6941 | 0.5008 |
112
- | 0.6541 | 0.384 | 6000 | 0.6936 | 0.5008 |
113
- | 0.6388 | 0.3904 | 6100 | 0.6932 | 0.5008 |
114
- | 0.6405 | 0.3968 | 6200 | 0.6935 | 0.5008 |
115
- | 0.6365 | 0.4032 | 6300 | 0.6969 | 0.5008 |
116
- | 0.6709 | 0.4096 | 6400 | 0.6932 | 0.5008 |
117
- | 0.6841 | 0.416 | 6500 | 0.6941 | 0.5008 |
118
- | 0.6325 | 0.4224 | 6600 | 0.6936 | 0.4992 |
119
- | 0.6568 | 0.4288 | 6700 | 0.6937 | 0.5008 |
120
- | 0.674 | 0.4352 | 6800 | 0.6948 | 0.5008 |
121
- | 0.6663 | 0.4416 | 6900 | 0.6933 | 0.4992 |
122
- | 0.6495 | 0.448 | 7000 | 0.6959 | 0.5008 |
123
- | 0.6462 | 0.4544 | 7100 | 0.6932 | 0.5008 |
124
- | 0.6513 | 0.4608 | 7200 | 0.6932 | 0.5008 |
125
- | 0.6535 | 0.4672 | 7300 | 0.6932 | 0.5008 |
126
- | 0.6447 | 0.4736 | 7400 | 0.6934 | 0.5008 |
127
- | 0.647 | 0.48 | 7500 | 0.6932 | 0.5008 |
128
- | 0.6465 | 0.4864 | 7600 | 0.6935 | 0.5008 |
129
- | 0.6948 | 0.4928 | 7700 | 0.6958 | 0.5008 |
130
- | 0.6233 | 0.4992 | 7800 | 0.6932 | 0.5008 |
131
- | 0.6552 | 0.5056 | 7900 | 0.6932 | 0.5008 |
132
- | 0.657 | 0.512 | 8000 | 0.6936 | 0.5008 |
133
- | 0.6647 | 0.5184 | 8100 | 0.6942 | 0.5008 |
134
- | 0.6184 | 0.5248 | 8200 | 0.6934 | 0.5008 |
135
- | 0.6661 | 0.5312 | 8300 | 0.6944 | 0.5008 |
136
- | 0.651 | 0.5376 | 8400 | 0.6932 | 0.5008 |
137
- | 0.6506 | 0.544 | 8500 | 0.6935 | 0.4992 |
138
- | 0.6391 | 0.5504 | 8600 | 0.6943 | 0.5008 |
139
- | 0.6874 | 0.5568 | 8700 | 0.6932 | 0.5008 |
140
- | 0.6795 | 0.5632 | 8800 | 0.6938 | 0.5008 |
141
- | 0.6357 | 0.5696 | 8900 | 0.6943 | 0.5008 |
142
- | 0.6689 | 0.576 | 9000 | 0.6942 | 0.5008 |
143
- | 0.6583 | 0.5824 | 9100 | 0.6933 | 0.5008 |
144
- | 0.6407 | 0.5888 | 9200 | 0.6953 | 0.5008 |
145
- | 0.6499 | 0.5952 | 9300 | 0.6935 | 0.4992 |
146
- | 0.6356 | 0.6016 | 9400 | 0.6936 | 0.5008 |
147
- | 0.6728 | 0.608 | 9500 | 0.6932 | 0.4991 |
148
- | 0.6602 | 0.6144 | 9600 | 0.6933 | 0.4991 |
149
- | 0.6716 | 0.6208 | 9700 | 0.6977 | 0.5008 |
150
- | 0.6437 | 0.6272 | 9800 | 0.6939 | 0.5008 |
151
- | 0.6528 | 0.6336 | 9900 | 0.6939 | 0.5008 |
152
- | 0.6381 | 0.64 | 10000 | 0.6932 | 0.4992 |
153
- | 0.6581 | 0.6464 | 10100 | 0.6933 | 0.4992 |
154
- | 0.6632 | 0.6528 | 10200 | 0.6932 | 0.5008 |
155
- | 0.6365 | 0.6592 | 10300 | 0.6932 | 0.4991 |
156
- | 0.6511 | 0.6656 | 10400 | 0.6936 | 0.5008 |
157
- | 0.6821 | 0.672 | 10500 | 0.6932 | 0.4991 |
158
- | 0.6571 | 0.6784 | 10600 | 0.6935 | 0.4991 |
159
- | 0.641 | 0.6848 | 10700 | 0.6933 | 0.5008 |
160
- | 0.6506 | 0.6912 | 10800 | 0.6936 | 0.5008 |
161
- | 0.6464 | 0.6976 | 10900 | 0.6932 | 0.4991 |
162
- | 0.6471 | 0.704 | 11000 | 0.6932 | 0.5008 |
163
- | 0.6262 | 0.7104 | 11100 | 0.6932 | 0.5152 |
164
- | 0.646 | 0.7168 | 11200 | 0.6944 | 0.5008 |
165
- | 0.594 | 0.7232 | 11300 | 0.6935 | 0.5022 |
166
- | 0.5642 | 0.7296 | 11400 | 0.6947 | 0.4954 |
167
- | 0.5901 | 0.736 | 11500 | 0.6944 | 0.5085 |
168
- | 0.9112 | 0.7424 | 11600 | 0.7075 | 0.5017 |
169
- | 0.5567 | 0.7488 | 11700 | 0.6946 | 0.4965 |
170
- | 0.5195 | 0.7552 | 11800 | 0.6943 | 0.5022 |
171
- | 0.5034 | 0.7616 | 11900 | 0.6964 | 0.5008 |
172
- | 0.5033 | 0.768 | 12000 | 0.7115 | 0.5008 |
173
- | 0.5441 | 0.7744 | 12100 | 0.6943 | 0.496 |
174
- | 0.5013 | 0.7808 | 12200 | 0.6968 | 0.4758 |
175
- | 0.4982 | 0.7872 | 12300 | 0.6938 | 0.5027 |
176
- | 0.6277 | 0.7936 | 12400 | 0.7019 | 0.5008 |
177
- | 0.6034 | 0.8 | 12500 | 0.6933 | 0.5084 |
178
- | 0.5511 | 0.8064 | 12600 | 0.6950 | 0.5 |
179
- | 0.5032 | 0.8128 | 12700 | 0.6943 | 0.5071 |
180
- | 0.4484 | 0.8192 | 12800 | 0.7013 | 0.5015 |
181
- | 0.5145 | 0.8256 | 12900 | 0.7024 | 0.499 |
182
- | 0.5242 | 0.832 | 13000 | 0.6947 | 0.4996 |
183
- | 0.4379 | 0.8384 | 13100 | 0.6966 | 0.5012 |
184
- | 0.5915 | 0.8448 | 13200 | 0.6950 | 0.5031 |
185
- | 0.4966 | 0.8512 | 13300 | 0.6937 | 0.5086 |
186
- | 0.5188 | 0.8576 | 13400 | 0.7033 | 0.5061 |
187
- | 0.5425 | 0.864 | 13500 | 0.7071 | 0.4982 |
188
- | 0.5383 | 0.8704 | 13600 | 0.6951 | 0.5073 |
189
- | 0.4726 | 0.8768 | 13700 | 0.6917 | 0.5065 |
190
- | 0.4715 | 0.8832 | 13800 | 0.6955 | 0.5141 |
191
- | 0.3859 | 0.8896 | 13900 | 0.6962 | 0.5106 |
192
- | 0.3897 | 0.896 | 14000 | 0.6932 | 0.509 |
193
- | 0.3483 | 0.9024 | 14100 | 0.6913 | 0.5058 |
194
- | 0.4255 | 0.9088 | 14200 | 0.6967 | 0.5114 |
195
- | 0.3708 | 0.9152 | 14300 | 0.7110 | 0.5118 |
196
- | 0.4321 | 0.9216 | 14400 | 0.6898 | 0.5143 |
197
- | 0.3683 | 0.928 | 14500 | 0.7010 | 0.5133 |
198
- | 0.3511 | 0.9344 | 14600 | 0.7037 | 0.5116 |
199
- | 0.3547 | 0.9408 | 14700 | 0.6956 | 0.5201 |
200
- | 0.3052 | 0.9472 | 14800 | 0.6934 | 0.5113 |
201
- | 0.3722 | 0.9536 | 14900 | 0.6971 | 0.5198 |
202
- | 0.3727 | 0.96 | 15000 | 0.6972 | 0.5207 |
203
- | 0.3045 | 0.9664 | 15100 | 0.6998 | 0.521 |
204
- | 0.3515 | 0.9728 | 15200 | 0.6997 | 0.5152 |
205
- | 0.3888 | 0.9792 | 15300 | 0.6976 | 0.5166 |
206
- | 0.3267 | 0.9856 | 15400 | 0.6981 | 0.516 |
207
- | 0.3438 | 0.992 | 15500 | 0.6984 | 0.5156 |
208
- | 0.4046 | 0.9984 | 15600 | 0.6984 | 0.5149 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
209
 
210
 
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  ### Framework versions
 
16
 
17
  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
18
  It achieves the following results on the evaluation set:
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+ - Loss: 0.6932
20
+ - Accuracy: 0.503
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22
  ## Model description
23
 
 
49
 
50
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
51
  |:-------------:|:------:|:-----:|:---------------:|:--------:|
52
+ | No log | 0 | 0 | 3.1802 | 0.0 |
53
+ | 0.6348 | 0.0043 | 100 | 0.7304 | 0.4995 |
54
+ | 0.496 | 0.0085 | 200 | 0.7047 | 0.495 |
55
+ | 0.4587 | 0.0128 | 300 | 0.7099 | 0.503 |
56
+ | 0.5157 | 0.0171 | 400 | 0.6995 | 0.5008 |
57
+ | 0.4757 | 0.0213 | 500 | 0.7055 | 0.4977 |
58
+ | 0.4381 | 0.0256 | 600 | 0.7905 | 0.4994 |
59
+ | 0.4112 | 0.0299 | 700 | 0.7068 | 0.5029 |
60
+ | 0.4687 | 0.0341 | 800 | 0.6958 | 0.4993 |
61
+ | 0.6409 | 0.0384 | 900 | 0.6983 | 0.4973 |
62
+ | 0.4482 | 0.0427 | 1000 | 0.6928 | 0.4994 |
63
+ | 0.4199 | 0.0469 | 1100 | 0.6920 | 0.4829 |
64
+ | 0.6614 | 0.0512 | 1200 | 0.6997 | 0.4996 |
65
+ | 0.5764 | 0.0555 | 1300 | 0.6960 | 0.4987 |
66
+ | 0.6243 | 0.0597 | 1400 | 0.6934 | 0.5001 |
67
+ | 0.67 | 0.0640 | 1500 | 0.7071 | 0.4992 |
68
+ | 0.6491 | 0.0683 | 1600 | 0.6960 | 0.5008 |
69
+ | 0.6874 | 0.0725 | 1700 | 0.6911 | 0.4871 |
70
+ | 0.6815 | 0.0768 | 1800 | 0.7413 | 0.4992 |
71
+ | 0.6497 | 0.0811 | 1900 | 0.6955 | 0.4992 |
72
+ | 0.6686 | 0.0853 | 2000 | 0.6960 | 0.5008 |
73
+ | 0.6624 | 0.0896 | 2100 | 0.6995 | 0.4992 |
74
+ | 0.6589 | 0.0939 | 2200 | 0.7005 | 0.4992 |
75
+ | 0.6788 | 0.0981 | 2300 | 0.6958 | 0.4992 |
76
+ | 0.6665 | 0.1024 | 2400 | 0.6953 | 0.4992 |
77
+ | 0.6473 | 0.1067 | 2500 | 0.7014 | 0.4992 |
78
+ | 0.682 | 0.1109 | 2600 | 0.6971 | 0.4992 |
79
+ | 0.6642 | 0.1152 | 2700 | 0.7000 | 0.5008 |
80
+ | 0.6411 | 0.1195 | 2800 | 0.6955 | 0.4992 |
81
+ | 0.6611 | 0.1237 | 2900 | 0.6978 | 0.4992 |
82
+ | 0.6501 | 0.1280 | 3000 | 0.6939 | 0.4992 |
83
+ | 0.6535 | 0.1323 | 3100 | 0.6963 | 0.4992 |
84
+ | 0.6507 | 0.1365 | 3200 | 0.6933 | 0.4992 |
85
+ | 0.6675 | 0.1408 | 3300 | 0.6943 | 0.4992 |
86
+ | 0.6466 | 0.1451 | 3400 | 0.6935 | 0.5008 |
87
+ | 0.6515 | 0.1493 | 3500 | 0.7037 | 0.4992 |
88
+ | 0.6748 | 0.1536 | 3600 | 0.6942 | 0.4992 |
89
+ | 0.6409 | 0.1579 | 3700 | 0.6934 | 0.5008 |
90
+ | 0.6628 | 0.1621 | 3800 | 0.6968 | 0.4992 |
91
+ | 0.6386 | 0.1664 | 3900 | 0.6946 | 0.4992 |
92
+ | 0.6525 | 0.1707 | 4000 | 0.6954 | 0.4992 |
93
+ | 0.6857 | 0.1749 | 4100 | 0.6946 | 0.4992 |
94
+ | 0.65 | 0.1792 | 4200 | 0.6937 | 0.5008 |
95
+ | 0.6695 | 0.1835 | 4300 | 0.6942 | 0.5008 |
96
+ | 0.6676 | 0.1877 | 4400 | 0.6941 | 0.5008 |
97
+ | 0.6591 | 0.1920 | 4500 | 0.6972 | 0.5008 |
98
+ | 0.6537 | 0.1963 | 4600 | 0.6932 | 0.4992 |
99
+ | 0.6771 | 0.2005 | 4700 | 0.6935 | 0.5008 |
100
+ | 0.5977 | 0.2048 | 4800 | 0.6938 | 0.4992 |
101
+ | 0.6751 | 0.2091 | 4900 | 0.6938 | 0.5008 |
102
+ | 0.6611 | 0.2133 | 5000 | 0.6962 | 0.5008 |
103
+ | 0.6913 | 0.2176 | 5100 | 0.6932 | 0.4992 |
104
+ | 0.6444 | 0.2219 | 5200 | 0.6935 | 0.5008 |
105
+ | 0.6526 | 0.2261 | 5300 | 0.6939 | 0.4992 |
106
+ | 0.6554 | 0.2304 | 5400 | 0.6966 | 0.4992 |
107
+ | 0.6638 | 0.2347 | 5500 | 0.6956 | 0.5008 |
108
+ | 0.673 | 0.2389 | 5600 | 0.6933 | 0.5008 |
109
+ | 0.6423 | 0.2432 | 5700 | 0.6933 | 0.5008 |
110
+ | 0.672 | 0.2475 | 5800 | 0.6947 | 0.4992 |
111
+ | 0.6539 | 0.2517 | 5900 | 0.6956 | 0.4992 |
112
+ | 0.6319 | 0.2560 | 6000 | 0.6957 | 0.4992 |
113
+ | 0.6613 | 0.2603 | 6100 | 0.6934 | 0.497 |
114
+ | 0.6808 | 0.2645 | 6200 | 0.6996 | 0.5008 |
115
+ | 0.6866 | 0.2688 | 6300 | 0.6952 | 0.5008 |
116
+ | 0.6544 | 0.2731 | 6400 | 0.6936 | 0.4992 |
117
+ | 0.6663 | 0.2773 | 6500 | 0.6933 | 0.4992 |
118
+ | 0.6594 | 0.2816 | 6600 | 0.6938 | 0.4992 |
119
+ | 0.6618 | 0.2859 | 6700 | 0.6959 | 0.4992 |
120
+ | 0.6683 | 0.2901 | 6800 | 0.6939 | 0.4992 |
121
+ | 0.6371 | 0.2944 | 6900 | 0.6932 | 0.5008 |
122
+ | 0.6405 | 0.2987 | 7000 | 0.6947 | 0.4992 |
123
+ | 0.6831 | 0.3029 | 7100 | 0.6934 | 0.5008 |
124
+ | 0.6585 | 0.3072 | 7200 | 0.6934 | 0.4992 |
125
+ | 0.665 | 0.3115 | 7300 | 0.6942 | 0.5008 |
126
+ | 0.6593 | 0.3157 | 7400 | 0.6940 | 0.4992 |
127
+ | 0.6699 | 0.3200 | 7500 | 0.6956 | 0.5008 |
128
+ | 0.6724 | 0.3243 | 7600 | 0.6934 | 0.497 |
129
+ | 0.6669 | 0.3285 | 7700 | 0.6934 | 0.497 |
130
+ | 0.6518 | 0.3328 | 7800 | 0.6936 | 0.4992 |
131
+ | 0.676 | 0.3371 | 7900 | 0.6939 | 0.497 |
132
+ | 0.6865 | 0.3413 | 8000 | 0.6968 | 0.4992 |
133
+ | 0.676 | 0.3456 | 8100 | 0.6947 | 0.4992 |
134
+ | 0.6695 | 0.3499 | 8200 | 0.6933 | 0.5008 |
135
+ | 0.6756 | 0.3541 | 8300 | 0.6934 | 0.5008 |
136
+ | 0.6601 | 0.3584 | 8400 | 0.6933 | 0.497 |
137
+ | 0.627 | 0.3627 | 8500 | 0.6936 | 0.4992 |
138
+ | 0.6727 | 0.3669 | 8600 | 0.6936 | 0.497 |
139
+ | 0.6514 | 0.3712 | 8700 | 0.6939 | 0.5008 |
140
+ | 0.67 | 0.3755 | 8800 | 0.6943 | 0.4992 |
141
+ | 0.6805 | 0.3797 | 8900 | 0.6945 | 0.4992 |
142
+ | 0.6675 | 0.3840 | 9000 | 0.6937 | 0.497 |
143
+ | 0.6522 | 0.3883 | 9100 | 0.6937 | 0.497 |
144
+ | 0.6502 | 0.3925 | 9200 | 0.6935 | 0.5008 |
145
+ | 0.6392 | 0.3968 | 9300 | 0.6940 | 0.4992 |
146
+ | 0.6593 | 0.4011 | 9400 | 0.6935 | 0.497 |
147
+ | 0.6567 | 0.4053 | 9500 | 0.6937 | 0.4992 |
148
+ | 0.6888 | 0.4096 | 9600 | 0.6938 | 0.497 |
149
+ | 0.6795 | 0.4139 | 9700 | 0.6954 | 0.4992 |
150
+ | 0.6627 | 0.4181 | 9800 | 0.6940 | 0.497 |
151
+ | 0.6549 | 0.4224 | 9900 | 0.6936 | 0.497 |
152
+ | 0.6688 | 0.4267 | 10000 | 0.6959 | 0.4992 |
153
+ | 0.6685 | 0.4309 | 10100 | 0.6936 | 0.497 |
154
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263
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264
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266
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