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tona3738/ChungliaoSA_MizoBERT_finetuned

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  1. README.md +93 -67
  2. config.json +9 -11
  3. model.safetensors +2 -2
README.md CHANGED
@@ -1,6 +1,6 @@
1
  ---
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- license: mit
3
- base_model: xlm-roberta-base
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  tags:
5
  - generated_from_trainer
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  metrics:
@@ -18,13 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # results
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21
- This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
23
- - Loss: 2.1152
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- - Accuracy: 0.61
25
- - F1: 0.61
26
- - Precision: 0.61
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- - Recall: 0.61
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  ## Model description
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@@ -56,65 +56,91 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
58
  |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
59
- | 0.1882 | 0.0585 | 10 | 0.1886 | 0.9391 | 0.9391 | 0.9391 | 0.9391 |
60
- | 0.1568 | 0.1170 | 20 | 0.1963 | 0.9391 | 0.9391 | 0.9391 | 0.9391 |
61
- | 0.1994 | 0.1754 | 30 | 0.2035 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
62
- | 0.1416 | 0.2339 | 40 | 0.2171 | 0.9359 | 0.9359 | 0.9359 | 0.9359 |
63
- | 0.2141 | 0.2924 | 50 | 0.2720 | 0.9328 | 0.9328 | 0.9328 | 0.9328 |
64
- | 0.0969 | 0.3509 | 60 | 0.2458 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
65
- | 0.2652 | 0.4094 | 70 | 0.2554 | 0.9391 | 0.9391 | 0.9391 | 0.9391 |
66
- | 0.085 | 0.4678 | 80 | 0.2936 | 0.9344 | 0.9344 | 0.9344 | 0.9344 |
67
- | 0.1397 | 0.5263 | 90 | 0.2649 | 0.9391 | 0.9391 | 0.9391 | 0.9391 |
68
- | 0.2762 | 0.5848 | 100 | 0.3024 | 0.9297 | 0.9297 | 0.9297 | 0.9297 |
69
- | 0.1788 | 0.6433 | 110 | 0.2307 | 0.9391 | 0.9391 | 0.9391 | 0.9391 |
70
- | 0.1667 | 0.7018 | 120 | 0.2133 | 0.9391 | 0.9391 | 0.9391 | 0.9391 |
71
- | 0.2061 | 0.7602 | 130 | 0.2254 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
72
- | 0.1521 | 0.8187 | 140 | 0.2279 | 0.9344 | 0.9344 | 0.9344 | 0.9344 |
73
- | 0.0968 | 0.8772 | 150 | 0.2560 | 0.9391 | 0.9391 | 0.9391 | 0.9391 |
74
- | 0.1564 | 0.9357 | 160 | 0.2553 | 0.9406 | 0.9406 | 0.9406 | 0.9406 |
75
- | 0.1431 | 0.9942 | 170 | 0.2700 | 0.9281 | 0.9281 | 0.9281 | 0.9281 |
76
- | 0.1686 | 1.0526 | 180 | 0.3400 | 0.9313 | 0.9313 | 0.9313 | 0.9313 |
77
- | 0.1074 | 1.1111 | 190 | 0.2661 | 0.9328 | 0.9328 | 0.9328 | 0.9328 |
78
- | 0.1612 | 1.1696 | 200 | 0.2847 | 0.9359 | 0.9359 | 0.9359 | 0.9359 |
79
- | 0.2118 | 1.2281 | 210 | 0.4392 | 0.9078 | 0.9078 | 0.9078 | 0.9078 |
80
- | 0.2551 | 1.2865 | 220 | 0.3470 | 0.9359 | 0.9359 | 0.9359 | 0.9359 |
81
- | 0.1858 | 1.3450 | 230 | 0.3350 | 0.9281 | 0.9281 | 0.9281 | 0.9281 |
82
- | 0.1507 | 1.4035 | 240 | 0.2992 | 0.9328 | 0.9328 | 0.9328 | 0.9328 |
83
- | 0.2389 | 1.4620 | 250 | 0.2510 | 0.9391 | 0.9391 | 0.9391 | 0.9391 |
84
- | 0.2761 | 1.5205 | 260 | 0.2246 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
85
- | 0.1357 | 1.5789 | 270 | 0.2733 | 0.9328 | 0.9328 | 0.9328 | 0.9328 |
86
- | 0.1481 | 1.6374 | 280 | 0.2734 | 0.9453 | 0.9453 | 0.9453 | 0.9453 |
87
- | 0.3975 | 1.6959 | 290 | 0.5704 | 0.8688 | 0.8688 | 0.8688 | 0.8688 |
88
- | 0.2902 | 1.7544 | 300 | 0.5804 | 0.8969 | 0.8969 | 0.8969 | 0.8969 |
89
- | 0.2816 | 1.8129 | 310 | 0.3855 | 0.9328 | 0.9328 | 0.9328 | 0.9328 |
90
- | 0.1115 | 1.8713 | 320 | 0.3323 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
91
- | 0.146 | 1.9298 | 330 | 0.3517 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
92
- | 0.1917 | 1.9883 | 340 | 0.2245 | 0.9453 | 0.9453 | 0.9453 | 0.9453 |
93
- | 0.1237 | 2.0468 | 350 | 0.2687 | 0.9484 | 0.9484 | 0.9484 | 0.9484 |
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- | 0.1781 | 2.1053 | 360 | 0.2380 | 0.9469 | 0.9469 | 0.9469 | 0.9469 |
95
- | 0.0625 | 2.1637 | 370 | 0.2448 | 0.9516 | 0.9516 | 0.9516 | 0.9516 |
96
- | 0.1892 | 2.2222 | 380 | 0.2868 | 0.9359 | 0.9359 | 0.9359 | 0.9359 |
97
- | 0.0542 | 2.2807 | 390 | 0.2990 | 0.9437 | 0.9437 | 0.9437 | 0.9437 |
98
- | 0.259 | 2.3392 | 400 | 0.2570 | 0.9469 | 0.9469 | 0.9469 | 0.9469 |
99
- | 0.2048 | 2.3977 | 410 | 0.2399 | 0.9437 | 0.9437 | 0.9437 | 0.9437 |
100
- | 0.0834 | 2.4561 | 420 | 0.5115 | 0.9266 | 0.9266 | 0.9266 | 0.9266 |
101
- | 0.3759 | 2.5146 | 430 | 0.3691 | 0.8922 | 0.8922 | 0.8922 | 0.8922 |
102
- | 0.2228 | 2.5731 | 440 | 0.2967 | 0.9062 | 0.9062 | 0.9062 | 0.9062 |
103
- | 0.093 | 2.6316 | 450 | 0.2749 | 0.9453 | 0.9453 | 0.9453 | 0.9453 |
104
- | 0.2818 | 2.6901 | 460 | 0.4297 | 0.925 | 0.925 | 0.925 | 0.925 |
105
- | 0.0214 | 2.7485 | 470 | 0.3587 | 0.9484 | 0.9484 | 0.9484 | 0.9484 |
106
- | 0.075 | 2.8070 | 480 | 0.4247 | 0.9266 | 0.9266 | 0.9266 | 0.9266 |
107
- | 0.3637 | 2.8655 | 490 | 0.8781 | 0.8703 | 0.8703 | 0.8703 | 0.8703 |
108
- | 0.4704 | 2.9240 | 500 | 0.3136 | 0.9484 | 0.9484 | 0.9484 | 0.9484 |
109
- | 0.6137 | 2.9825 | 510 | 0.2817 | 0.9156 | 0.9156 | 0.9156 | 0.9156 |
110
- | 0.1679 | 3.0409 | 520 | 0.3007 | 0.9484 | 0.9484 | 0.9484 | 0.9484 |
111
- | 0.1285 | 3.0994 | 530 | 0.4259 | 0.9406 | 0.9406 | 0.9406 | 0.9406 |
112
- | 0.2149 | 3.1579 | 540 | 0.4744 | 0.9187 | 0.9187 | 0.9187 | 0.9187 |
113
- | 0.3003 | 3.2164 | 550 | 0.1967 | 0.9531 | 0.9531 | 0.9531 | 0.9531 |
114
- | 0.1603 | 3.2749 | 560 | 0.1874 | 0.9547 | 0.9547 | 0.9547 | 0.9547 |
115
- | 0.1173 | 3.3333 | 570 | 0.3717 | 0.9328 | 0.9328 | 0.9328 | 0.9328 |
116
- | 0.1035 | 3.3918 | 580 | 0.3302 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
117
- | 0.2405 | 3.4503 | 590 | 0.4261 | 0.9313 | 0.9313 | 0.9313 | 0.9313 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
1
  ---
2
+ license: apache-2.0
3
+ base_model: robzchhangte/MizBERT
4
  tags:
5
  - generated_from_trainer
6
  metrics:
 
18
 
19
  # results
20
 
21
+ This model is a fine-tuned version of [robzchhangte/MizBERT](https://huggingface.co/robzchhangte/MizBERT) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 1.3926
24
+ - Accuracy: 0.725
25
+ - F1: 0.7250
26
+ - Precision: 0.725
27
+ - Recall: 0.725
28
 
29
  ## Model description
30
 
 
56
 
57
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
58
  |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
59
+ | 0.7055 | 0.0585 | 10 | 0.7187 | 0.4938 | 0.4938 | 0.4938 | 0.4938 |
60
+ | 0.7246 | 0.1170 | 20 | 0.7049 | 0.4938 | 0.4938 | 0.4938 | 0.4938 |
61
+ | 0.694 | 0.1754 | 30 | 0.6871 | 0.5047 | 0.5047 | 0.5047 | 0.5047 |
62
+ | 0.6795 | 0.2339 | 40 | 0.6711 | 0.5719 | 0.5719 | 0.5719 | 0.5719 |
63
+ | 0.6724 | 0.2924 | 50 | 0.6462 | 0.775 | 0.775 | 0.775 | 0.775 |
64
+ | 0.6381 | 0.3509 | 60 | 0.6175 | 0.6891 | 0.6891 | 0.6891 | 0.6891 |
65
+ | 0.6196 | 0.4094 | 70 | 0.5848 | 0.6703 | 0.6703 | 0.6703 | 0.6703 |
66
+ | 0.5953 | 0.4678 | 80 | 0.5209 | 0.8891 | 0.8891 | 0.8891 | 0.8891 |
67
+ | 0.541 | 0.5263 | 90 | 0.4970 | 0.7766 | 0.7766 | 0.7766 | 0.7766 |
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+ | 0.4702 | 0.5848 | 100 | 0.4052 | 0.8406 | 0.8406 | 0.8406 | 0.8406 |
69
+ | 0.3675 | 0.6433 | 110 | 0.2772 | 0.925 | 0.925 | 0.925 | 0.925 |
70
+ | 0.2899 | 0.7018 | 120 | 0.2200 | 0.9219 | 0.9219 | 0.9219 | 0.9219 |
71
+ | 0.1951 | 0.7602 | 130 | 0.1902 | 0.9297 | 0.9297 | 0.9297 | 0.9297 |
72
+ | 0.1448 | 0.8187 | 140 | 0.1830 | 0.9359 | 0.9359 | 0.9359 | 0.9359 |
73
+ | 0.3086 | 0.8772 | 150 | 0.1675 | 0.9453 | 0.9453 | 0.9453 | 0.9453 |
74
+ | 0.1802 | 0.9357 | 160 | 0.1632 | 0.9391 | 0.9391 | 0.9391 | 0.9391 |
75
+ | 0.1315 | 0.9942 | 170 | 0.1461 | 0.9516 | 0.9516 | 0.9516 | 0.9516 |
76
+ | 0.0848 | 1.0526 | 180 | 0.1691 | 0.9406 | 0.9406 | 0.9406 | 0.9406 |
77
+ | 0.1519 | 1.1111 | 190 | 0.1660 | 0.9453 | 0.9453 | 0.9453 | 0.9453 |
78
+ | 0.1363 | 1.1696 | 200 | 0.1355 | 0.9563 | 0.9563 | 0.9563 | 0.9563 |
79
+ | 0.1734 | 1.2281 | 210 | 0.1341 | 0.9531 | 0.9531 | 0.9531 | 0.9531 |
80
+ | 0.1234 | 1.2865 | 220 | 0.1225 | 0.9547 | 0.9547 | 0.9547 | 0.9547 |
81
+ | 0.1877 | 1.3450 | 230 | 0.2228 | 0.9219 | 0.9219 | 0.9219 | 0.9219 |
82
+ | 0.125 | 1.4035 | 240 | 0.2108 | 0.9359 | 0.9359 | 0.9359 | 0.9359 |
83
+ | 0.169 | 1.4620 | 250 | 0.1416 | 0.9563 | 0.9563 | 0.9563 | 0.9563 |
84
+ | 0.0852 | 1.5205 | 260 | 0.1722 | 0.9516 | 0.9516 | 0.9516 | 0.9516 |
85
+ | 0.1255 | 1.5789 | 270 | 0.1551 | 0.9609 | 0.9609 | 0.9609 | 0.9609 |
86
+ | 0.1869 | 1.6374 | 280 | 0.1823 | 0.9469 | 0.9469 | 0.9469 | 0.9469 |
87
+ | 0.1286 | 1.6959 | 290 | 0.1334 | 0.9641 | 0.9641 | 0.9641 | 0.9641 |
88
+ | 0.1508 | 1.7544 | 300 | 0.1064 | 0.9703 | 0.9703 | 0.9703 | 0.9703 |
89
+ | 0.1334 | 1.8129 | 310 | 0.0977 | 0.9703 | 0.9703 | 0.9703 | 0.9703 |
90
+ | 0.0937 | 1.8713 | 320 | 0.1685 | 0.9453 | 0.9453 | 0.9453 | 0.9453 |
91
+ | 0.1036 | 1.9298 | 330 | 0.1624 | 0.95 | 0.9500 | 0.95 | 0.95 |
92
+ | 0.084 | 1.9883 | 340 | 0.1674 | 0.9578 | 0.9578 | 0.9578 | 0.9578 |
93
+ | 0.0941 | 2.0468 | 350 | 0.1388 | 0.9672 | 0.9672 | 0.9672 | 0.9672 |
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+ | 0.0322 | 2.1053 | 360 | 0.1474 | 0.9609 | 0.9609 | 0.9609 | 0.9609 |
95
+ | 0.0538 | 2.1637 | 370 | 0.1744 | 0.9641 | 0.9641 | 0.9641 | 0.9641 |
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+ | 0.122 | 2.2222 | 380 | 0.1500 | 0.9625 | 0.9625 | 0.9625 | 0.9625 |
97
+ | 0.0953 | 2.2807 | 390 | 0.1452 | 0.9703 | 0.9703 | 0.9703 | 0.9703 |
98
+ | 0.0173 | 2.3392 | 400 | 0.1335 | 0.9703 | 0.9703 | 0.9703 | 0.9703 |
99
+ | 0.1145 | 2.3977 | 410 | 0.1010 | 0.9734 | 0.9734 | 0.9734 | 0.9734 |
100
+ | 0.0869 | 2.4561 | 420 | 0.2181 | 0.9453 | 0.9453 | 0.9453 | 0.9453 |
101
+ | 0.133 | 2.5146 | 430 | 0.1249 | 0.9594 | 0.9594 | 0.9594 | 0.9594 |
102
+ | 0.0906 | 2.5731 | 440 | 0.1398 | 0.9625 | 0.9625 | 0.9625 | 0.9625 |
103
+ | 0.0219 | 2.6316 | 450 | 0.1419 | 0.9656 | 0.9656 | 0.9656 | 0.9656 |
104
+ | 0.019 | 2.6901 | 460 | 0.2162 | 0.9594 | 0.9594 | 0.9594 | 0.9594 |
105
+ | 0.0504 | 2.7485 | 470 | 0.1927 | 0.9625 | 0.9625 | 0.9625 | 0.9625 |
106
+ | 0.0321 | 2.8070 | 480 | 0.1142 | 0.975 | 0.975 | 0.975 | 0.975 |
107
+ | 0.1058 | 2.8655 | 490 | 0.3005 | 0.9359 | 0.9359 | 0.9359 | 0.9359 |
108
+ | 0.0666 | 2.9240 | 500 | 0.2410 | 0.9563 | 0.9563 | 0.9563 | 0.9563 |
109
+ | 0.0383 | 2.9825 | 510 | 0.1155 | 0.9688 | 0.9688 | 0.9688 | 0.9688 |
110
+ | 0.022 | 3.0409 | 520 | 0.0930 | 0.9797 | 0.9797 | 0.9797 | 0.9797 |
111
+ | 0.1328 | 3.0994 | 530 | 0.1120 | 0.975 | 0.975 | 0.975 | 0.975 |
112
+ | 0.0765 | 3.1579 | 540 | 0.0973 | 0.9766 | 0.9766 | 0.9766 | 0.9766 |
113
+ | 0.0316 | 3.2164 | 550 | 0.0939 | 0.9797 | 0.9797 | 0.9797 | 0.9797 |
114
+ | 0.0646 | 3.2749 | 560 | 0.0965 | 0.9812 | 0.9812 | 0.9812 | 0.9812 |
115
+ | 0.0478 | 3.3333 | 570 | 0.0726 | 0.9828 | 0.9828 | 0.9828 | 0.9828 |
116
+ | 0.0007 | 3.3918 | 580 | 0.0772 | 0.9828 | 0.9828 | 0.9828 | 0.9828 |
117
+ | 0.0444 | 3.4503 | 590 | 0.0769 | 0.9859 | 0.9859 | 0.9859 | 0.9859 |
118
+ | 0.0378 | 3.5088 | 600 | 0.0773 | 0.9828 | 0.9828 | 0.9828 | 0.9828 |
119
+ | 0.0058 | 3.5673 | 610 | 0.0932 | 0.9812 | 0.9812 | 0.9812 | 0.9812 |
120
+ | 0.0282 | 3.6257 | 620 | 0.1314 | 0.9703 | 0.9703 | 0.9703 | 0.9703 |
121
+ | 0.0034 | 3.6842 | 630 | 0.1199 | 0.9719 | 0.9719 | 0.9719 | 0.9719 |
122
+ | 0.0485 | 3.7427 | 640 | 0.1164 | 0.9781 | 0.9781 | 0.9781 | 0.9781 |
123
+ | 0.1038 | 3.8012 | 650 | 0.1432 | 0.9781 | 0.9781 | 0.9781 | 0.9781 |
124
+ | 0.0024 | 3.8596 | 660 | 0.1072 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
125
+ | 0.0143 | 3.9181 | 670 | 0.1090 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
126
+ | 0.0504 | 3.9766 | 680 | 0.1029 | 0.9828 | 0.9828 | 0.9828 | 0.9828 |
127
+ | 0.0234 | 4.0351 | 690 | 0.0889 | 0.9812 | 0.9812 | 0.9812 | 0.9812 |
128
+ | 0.0005 | 4.0936 | 700 | 0.0847 | 0.9812 | 0.9812 | 0.9812 | 0.9812 |
129
+ | 0.0275 | 4.1520 | 710 | 0.0694 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
130
+ | 0.0166 | 4.2105 | 720 | 0.0758 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
131
+ | 0.0007 | 4.2690 | 730 | 0.0875 | 0.9828 | 0.9828 | 0.9828 | 0.9828 |
132
+ | 0.0004 | 4.3275 | 740 | 0.0968 | 0.9828 | 0.9828 | 0.9828 | 0.9828 |
133
+ | 0.0004 | 4.3860 | 750 | 0.0939 | 0.9812 | 0.9812 | 0.9812 | 0.9812 |
134
+ | 0.0006 | 4.4444 | 760 | 0.0912 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
135
+ | 0.0453 | 4.5029 | 770 | 0.0876 | 0.9828 | 0.9828 | 0.9828 | 0.9828 |
136
+ | 0.0037 | 4.5614 | 780 | 0.0921 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
137
+ | 0.0459 | 4.6199 | 790 | 0.1005 | 0.9828 | 0.9828 | 0.9828 | 0.9828 |
138
+ | 0.0027 | 4.6784 | 800 | 0.1031 | 0.9828 | 0.9828 | 0.9828 | 0.9828 |
139
+ | 0.0203 | 4.7368 | 810 | 0.0974 | 0.9828 | 0.9828 | 0.9828 | 0.9828 |
140
+ | 0.02 | 4.7953 | 820 | 0.0847 | 0.9859 | 0.9859 | 0.9859 | 0.9859 |
141
+ | 0.0355 | 4.8538 | 830 | 0.0871 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
142
+ | 0.0021 | 4.9123 | 840 | 0.0881 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
143
+ | 0.0011 | 4.9708 | 850 | 0.0882 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
144
 
145
 
146
  ### Framework versions
config.json CHANGED
@@ -1,29 +1,27 @@
1
  {
2
- "_name_or_path": "xlm-roberta-base",
3
  "architectures": [
4
- "XLMRobertaForSequenceClassification"
5
  ],
6
  "attention_probs_dropout_prob": 0.1,
7
- "bos_token_id": 0,
8
  "classifier_dropout": null,
9
- "eos_token_id": 2,
10
  "hidden_act": "gelu",
11
  "hidden_dropout_prob": 0.1,
12
  "hidden_size": 768,
13
  "initializer_range": 0.02,
14
  "intermediate_size": 3072,
15
- "layer_norm_eps": 1e-05,
16
- "max_position_embeddings": 514,
17
- "model_type": "xlm-roberta",
18
  "num_attention_heads": 12,
19
  "num_hidden_layers": 12,
20
- "output_past": true,
21
- "pad_token_id": 1,
22
  "position_embedding_type": "absolute",
23
  "problem_type": "single_label_classification",
24
  "torch_dtype": "float32",
25
  "transformers_version": "4.42.4",
26
- "type_vocab_size": 1,
27
  "use_cache": true,
28
- "vocab_size": 250002
29
  }
 
1
  {
2
+ "_name_or_path": "robzchhangte/MizBERT",
3
  "architectures": [
4
+ "BertForSequenceClassification"
5
  ],
6
  "attention_probs_dropout_prob": 0.1,
 
7
  "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
  "hidden_act": "gelu",
10
  "hidden_dropout_prob": 0.1,
11
  "hidden_size": 768,
12
  "initializer_range": 0.02,
13
  "intermediate_size": 3072,
14
+ "layer_norm_eps": 1e-12,
15
+ "max_position_embeddings": 512,
16
+ "model_type": "bert",
17
  "num_attention_heads": 12,
18
  "num_hidden_layers": 12,
19
+ "pad_token_id": 0,
 
20
  "position_embedding_type": "absolute",
21
  "problem_type": "single_label_classification",
22
  "torch_dtype": "float32",
23
  "transformers_version": "4.42.4",
24
+ "type_vocab_size": 2,
25
  "use_cache": true,
26
+ "vocab_size": 30522
27
  }
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
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- oid sha256:99e339eacbfc46f83b80139b5d326803f3a8fc4515e29acff80cdaa9967fcf9e
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- size 1112205008
 
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