kaixkhazaki commited on
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Pushing of the best model checkpoint

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README.md CHANGED
@@ -4,7 +4,6 @@ license: mit
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  base_model: dbmdz/distilbert-base-turkish-cased
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  tags:
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  - generated_from_trainer
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- - turkish
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  metrics:
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  - accuracy
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  - f1
@@ -13,9 +12,6 @@ metrics:
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  model-index:
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  - name: turkish-zeroshot-distilbert
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  results: []
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- datasets:
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- - facebook/xnli
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- pipeline_tag: zero-shot-classification
19
  ---
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21
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -25,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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26
  This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on an unknown dataset.
27
  It achieves the following results on the evaluation set:
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- - Loss: 0.6942
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- - Accuracy: 0.7137
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- - F1: 0.7148
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- - Precision: 0.7188
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- - Recall: 0.7137
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34
  ## Model description
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@@ -61,77 +57,127 @@ The following hyperparameters were used during training:
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62
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
63
  |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
64
- | 1.0977 | 0.0326 | 200 | 1.0947 | 0.3546 | 0.2595 | 0.3604 | 0.3546 |
65
- | 0.986 | 0.0652 | 400 | 0.9576 | 0.5482 | 0.5453 | 0.5749 | 0.5482 |
66
- | 0.9344 | 0.0978 | 600 | 0.8824 | 0.6044 | 0.6044 | 0.6050 | 0.6044 |
67
- | 0.8965 | 0.1304 | 800 | 0.8571 | 0.6040 | 0.6042 | 0.6059 | 0.6040 |
68
- | 0.9036 | 0.1630 | 1000 | 0.8388 | 0.6273 | 0.6278 | 0.6289 | 0.6273 |
69
- | 0.8795 | 0.1956 | 1200 | 0.8158 | 0.6329 | 0.6334 | 0.6476 | 0.6329 |
70
- | 0.8891 | 0.2282 | 1400 | 0.8250 | 0.6217 | 0.6212 | 0.6360 | 0.6217 |
71
- | 0.8661 | 0.2608 | 1600 | 0.8230 | 0.6257 | 0.6259 | 0.6262 | 0.6257 |
72
- | 0.8444 | 0.2934 | 1800 | 0.8146 | 0.6169 | 0.6162 | 0.6434 | 0.6169 |
73
- | 0.8163 | 0.3259 | 2000 | 0.8073 | 0.6337 | 0.6331 | 0.6398 | 0.6337 |
74
- | 0.855 | 0.3585 | 2200 | 0.8061 | 0.6446 | 0.6426 | 0.6591 | 0.6446 |
75
- | 0.8418 | 0.3911 | 2400 | 0.8080 | 0.6430 | 0.6419 | 0.6632 | 0.6430 |
76
- | 0.8141 | 0.4237 | 2600 | 0.7773 | 0.6526 | 0.6517 | 0.6631 | 0.6526 |
77
- | 0.8159 | 0.4563 | 2800 | 0.7642 | 0.6715 | 0.6702 | 0.6739 | 0.6715 |
78
- | 0.8247 | 0.4889 | 3000 | 0.7589 | 0.6578 | 0.6585 | 0.6653 | 0.6578 |
79
- | 0.8224 | 0.5215 | 3200 | 0.7727 | 0.6635 | 0.6631 | 0.6859 | 0.6635 |
80
- | 0.8163 | 0.5541 | 3400 | 0.7443 | 0.6819 | 0.6822 | 0.6841 | 0.6819 |
81
- | 0.792 | 0.5867 | 3600 | 0.7537 | 0.6663 | 0.6669 | 0.6741 | 0.6663 |
82
- | 0.7892 | 0.6193 | 3800 | 0.7494 | 0.6711 | 0.6711 | 0.6791 | 0.6711 |
83
- | 0.8136 | 0.6519 | 4000 | 0.7463 | 0.6739 | 0.6740 | 0.6855 | 0.6739 |
84
- | 0.7988 | 0.6845 | 4200 | 0.7367 | 0.6811 | 0.6818 | 0.6868 | 0.6811 |
85
- | 0.7905 | 0.7171 | 4400 | 0.7454 | 0.6767 | 0.6767 | 0.6812 | 0.6767 |
86
- | 0.7746 | 0.7497 | 4600 | 0.7533 | 0.6779 | 0.6774 | 0.6831 | 0.6779 |
87
- | 0.7778 | 0.7823 | 4800 | 0.7317 | 0.6791 | 0.6792 | 0.6879 | 0.6791 |
88
- | 0.7709 | 0.8149 | 5000 | 0.7304 | 0.6831 | 0.6834 | 0.6987 | 0.6831 |
89
- | 0.7479 | 0.8475 | 5200 | 0.7320 | 0.6759 | 0.6768 | 0.6889 | 0.6759 |
90
- | 0.7907 | 0.8801 | 5400 | 0.7156 | 0.6924 | 0.6931 | 0.6958 | 0.6924 |
91
- | 0.7587 | 0.9126 | 5600 | 0.7175 | 0.6952 | 0.6952 | 0.6983 | 0.6952 |
92
- | 0.7635 | 0.9452 | 5800 | 0.7206 | 0.6763 | 0.6767 | 0.6944 | 0.6763 |
93
- | 0.7437 | 0.9778 | 6000 | 0.7220 | 0.6843 | 0.6849 | 0.6999 | 0.6843 |
94
- | 0.7029 | 1.0104 | 6200 | 0.7448 | 0.6803 | 0.6792 | 0.6961 | 0.6803 |
95
- | 0.6853 | 1.0430 | 6400 | 0.7167 | 0.6896 | 0.6895 | 0.6963 | 0.6896 |
96
- | 0.7019 | 1.0756 | 6600 | 0.7333 | 0.6928 | 0.6931 | 0.7095 | 0.6928 |
97
- | 0.7006 | 1.1082 | 6800 | 0.7178 | 0.6960 | 0.6963 | 0.7043 | 0.6960 |
98
- | 0.7054 | 1.1408 | 7000 | 0.7078 | 0.6980 | 0.6986 | 0.7059 | 0.6980 |
99
- | 0.7125 | 1.1734 | 7200 | 0.7094 | 0.7016 | 0.7021 | 0.7114 | 0.7016 |
100
- | 0.6992 | 1.2060 | 7400 | 0.7339 | 0.6936 | 0.6924 | 0.7077 | 0.6936 |
101
- | 0.6989 | 1.2386 | 7600 | 0.7008 | 0.6980 | 0.6993 | 0.7065 | 0.6980 |
102
- | 0.7084 | 1.2712 | 7800 | 0.7040 | 0.7064 | 0.7066 | 0.7123 | 0.7064 |
103
- | 0.6951 | 1.3038 | 8000 | 0.7038 | 0.7064 | 0.7070 | 0.7154 | 0.7064 |
104
- | 0.6809 | 1.3364 | 8200 | 0.7146 | 0.7040 | 0.7043 | 0.7186 | 0.7040 |
105
- | 0.7038 | 1.3690 | 8400 | 0.6909 | 0.7120 | 0.7129 | 0.7194 | 0.7120 |
106
- | 0.7045 | 1.4016 | 8600 | 0.7248 | 0.6863 | 0.6852 | 0.7051 | 0.6863 |
107
- | 0.693 | 1.4342 | 8800 | 0.7133 | 0.6952 | 0.6952 | 0.7104 | 0.6952 |
108
- | 0.6912 | 1.4668 | 9000 | 0.7002 | 0.7024 | 0.7036 | 0.7145 | 0.7024 |
109
- | 0.6622 | 1.4993 | 9200 | 0.6899 | 0.7068 | 0.7079 | 0.7128 | 0.7068 |
110
- | 0.6986 | 1.5319 | 9400 | 0.6816 | 0.7129 | 0.7128 | 0.7137 | 0.7129 |
111
- | 0.6812 | 1.5645 | 9600 | 0.6879 | 0.7036 | 0.7041 | 0.7067 | 0.7036 |
112
- | 0.7011 | 1.5971 | 9800 | 0.6907 | 0.7032 | 0.7037 | 0.7098 | 0.7032 |
113
- | 0.6957 | 1.6297 | 10000 | 0.7047 | 0.7032 | 0.7040 | 0.7105 | 0.7032 |
114
- | 0.6978 | 1.6623 | 10200 | 0.6784 | 0.7137 | 0.7144 | 0.7169 | 0.7137 |
115
- | 0.6804 | 1.6949 | 10400 | 0.6965 | 0.7028 | 0.7036 | 0.7164 | 0.7028 |
116
- | 0.6673 | 1.7275 | 10600 | 0.7054 | 0.7036 | 0.7047 | 0.7162 | 0.7036 |
117
- | 0.6929 | 1.7601 | 10800 | 0.6933 | 0.7040 | 0.7050 | 0.7130 | 0.7040 |
118
- | 0.6714 | 1.7927 | 11000 | 0.6960 | 0.7020 | 0.7022 | 0.7113 | 0.7020 |
119
- | 0.6887 | 1.8253 | 11200 | 0.7028 | 0.7020 | 0.7023 | 0.7145 | 0.7020 |
120
- | 0.6894 | 1.8579 | 11400 | 0.7022 | 0.6996 | 0.6998 | 0.7154 | 0.6996 |
121
- | 0.6793 | 1.8905 | 11600 | 0.6930 | 0.7092 | 0.7100 | 0.7195 | 0.7092 |
122
- | 0.6767 | 1.9231 | 11800 | 0.6863 | 0.7173 | 0.7177 | 0.7245 | 0.7173 |
123
- | 0.695 | 1.9557 | 12000 | 0.6748 | 0.7161 | 0.7165 | 0.7241 | 0.7161 |
124
- | 0.6951 | 1.9883 | 12200 | 0.6874 | 0.7028 | 0.7034 | 0.7131 | 0.7028 |
125
- | 0.5979 | 2.0209 | 12400 | 0.6976 | 0.7116 | 0.7111 | 0.7122 | 0.7116 |
126
- | 0.5878 | 2.0535 | 12600 | 0.7078 | 0.7064 | 0.7070 | 0.7100 | 0.7064 |
127
- | 0.5826 | 2.0860 | 12800 | 0.7238 | 0.7036 | 0.7050 | 0.7155 | 0.7036 |
128
- | 0.5927 | 2.1186 | 13000 | 0.7100 | 0.7068 | 0.7075 | 0.7141 | 0.7068 |
129
- | 0.5976 | 2.1512 | 13200 | 0.7132 | 0.7056 | 0.7066 | 0.7175 | 0.7056 |
130
- | 0.5995 | 2.1838 | 13400 | 0.7001 | 0.7129 | 0.7137 | 0.7174 | 0.7129 |
131
- | 0.5898 | 2.2164 | 13600 | 0.7011 | 0.7145 | 0.7155 | 0.7202 | 0.7145 |
132
- | 0.5952 | 2.2490 | 13800 | 0.7345 | 0.7024 | 0.7028 | 0.7147 | 0.7024 |
133
- | 0.5886 | 2.2816 | 14000 | 0.7008 | 0.7088 | 0.7088 | 0.7144 | 0.7088 |
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- | 0.5761 | 2.3142 | 14200 | 0.6942 | 0.7137 | 0.7148 | 0.7188 | 0.7137 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
@@ -139,4 +185,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.48.0.dev0
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  - Pytorch 2.4.1+cu121
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  - Datasets 3.1.0
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- - Tokenizers 0.21.0
 
4
  base_model: dbmdz/distilbert-base-turkish-cased
5
  tags:
6
  - generated_from_trainer
 
7
  metrics:
8
  - accuracy
9
  - f1
 
12
  model-index:
13
  - name: turkish-zeroshot-distilbert
14
  results: []
 
 
 
15
  ---
16
 
17
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
21
 
22
  This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on an unknown dataset.
23
  It achieves the following results on the evaluation set:
24
+ - Loss: 0.7510
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+ - Accuracy: 0.7201
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+ - F1: 0.7207
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+ - Precision: 0.7290
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+ - Recall: 0.7201
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30
  ## Model description
31
 
 
57
 
58
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
59
  |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
60
+ | 1.0957 | 0.0326 | 200 | 1.0956 | 0.3506 | 0.2447 | 0.3575 | 0.3506 |
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+ | 1.0092 | 0.0652 | 400 | 0.9754 | 0.5305 | 0.5296 | 0.5476 | 0.5305 |
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+ | 0.9338 | 0.0978 | 600 | 0.8756 | 0.6080 | 0.6078 | 0.6098 | 0.6080 |
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+ | 0.8987 | 0.1304 | 800 | 0.8632 | 0.6112 | 0.6107 | 0.6133 | 0.6112 |
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+ | 0.9019 | 0.1630 | 1000 | 0.8275 | 0.6289 | 0.6291 | 0.6299 | 0.6289 |
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+ | 0.8854 | 0.1956 | 1200 | 0.8219 | 0.6185 | 0.6184 | 0.6439 | 0.6185 |
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+ | 0.8877 | 0.2282 | 1400 | 0.8108 | 0.6265 | 0.6249 | 0.6474 | 0.6265 |
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+ | 0.8653 | 0.2608 | 1600 | 0.8147 | 0.6317 | 0.6320 | 0.6346 | 0.6317 |
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+ | 0.8465 | 0.2934 | 1800 | 0.8109 | 0.6277 | 0.6269 | 0.6556 | 0.6277 |
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+ | 0.8205 | 0.3259 | 2000 | 0.7946 | 0.6430 | 0.6423 | 0.6480 | 0.6430 |
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+ | 0.8584 | 0.3585 | 2200 | 0.7998 | 0.6438 | 0.6414 | 0.6592 | 0.6438 |
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+ | 0.8393 | 0.3911 | 2400 | 0.7971 | 0.6534 | 0.6536 | 0.6719 | 0.6534 |
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+ | 0.8136 | 0.4237 | 2600 | 0.7695 | 0.6566 | 0.6562 | 0.6688 | 0.6566 |
73
+ | 0.8113 | 0.4563 | 2800 | 0.7614 | 0.6743 | 0.6739 | 0.6756 | 0.6743 |
74
+ | 0.8291 | 0.4889 | 3000 | 0.7589 | 0.6695 | 0.6704 | 0.6756 | 0.6695 |
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+ | 0.8274 | 0.5215 | 3200 | 0.7591 | 0.6699 | 0.6697 | 0.6916 | 0.6699 |
76
+ | 0.8165 | 0.5541 | 3400 | 0.7379 | 0.6791 | 0.6795 | 0.6828 | 0.6791 |
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+ | 0.7897 | 0.5867 | 3600 | 0.7467 | 0.6731 | 0.6734 | 0.6800 | 0.6731 |
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+ | 0.79 | 0.6193 | 3800 | 0.7473 | 0.6679 | 0.6676 | 0.6804 | 0.6679 |
79
+ | 0.8108 | 0.6519 | 4000 | 0.7380 | 0.6687 | 0.6696 | 0.6786 | 0.6687 |
80
+ | 0.797 | 0.6845 | 4200 | 0.7429 | 0.6783 | 0.6791 | 0.6889 | 0.6783 |
81
+ | 0.7893 | 0.7171 | 4400 | 0.7405 | 0.6743 | 0.6747 | 0.6777 | 0.6743 |
82
+ | 0.7653 | 0.7497 | 4600 | 0.7551 | 0.6711 | 0.6708 | 0.6789 | 0.6711 |
83
+ | 0.772 | 0.7823 | 4800 | 0.7270 | 0.6859 | 0.6861 | 0.6941 | 0.6859 |
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+ | 0.7686 | 0.8149 | 5000 | 0.7253 | 0.6819 | 0.6833 | 0.6909 | 0.6819 |
85
+ | 0.7431 | 0.8475 | 5200 | 0.7386 | 0.6731 | 0.6743 | 0.6926 | 0.6731 |
86
+ | 0.7968 | 0.8801 | 5400 | 0.7130 | 0.6936 | 0.6943 | 0.6978 | 0.6936 |
87
+ | 0.7584 | 0.9126 | 5600 | 0.7129 | 0.6960 | 0.6957 | 0.6973 | 0.6960 |
88
+ | 0.7629 | 0.9452 | 5800 | 0.7141 | 0.6827 | 0.6841 | 0.6983 | 0.6827 |
89
+ | 0.7477 | 0.9778 | 6000 | 0.7044 | 0.6920 | 0.6930 | 0.7029 | 0.6920 |
90
+ | 0.7043 | 1.0104 | 6200 | 0.7362 | 0.6880 | 0.6875 | 0.6996 | 0.6880 |
91
+ | 0.6868 | 1.0430 | 6400 | 0.7056 | 0.6972 | 0.6975 | 0.6999 | 0.6972 |
92
+ | 0.7048 | 1.0756 | 6600 | 0.7104 | 0.6896 | 0.6907 | 0.7016 | 0.6896 |
93
+ | 0.6965 | 1.1082 | 6800 | 0.7140 | 0.6988 | 0.6990 | 0.7021 | 0.6988 |
94
+ | 0.7043 | 1.1408 | 7000 | 0.7084 | 0.7028 | 0.7029 | 0.7109 | 0.7028 |
95
+ | 0.7111 | 1.1734 | 7200 | 0.6998 | 0.7008 | 0.7014 | 0.7066 | 0.7008 |
96
+ | 0.6994 | 1.2060 | 7400 | 0.7147 | 0.6964 | 0.6962 | 0.7043 | 0.6964 |
97
+ | 0.6992 | 1.2386 | 7600 | 0.6962 | 0.7028 | 0.7038 | 0.7129 | 0.7028 |
98
+ | 0.7161 | 1.2712 | 7800 | 0.7002 | 0.6964 | 0.6967 | 0.7039 | 0.6964 |
99
+ | 0.6935 | 1.3038 | 8000 | 0.7046 | 0.6972 | 0.6978 | 0.7079 | 0.6972 |
100
+ | 0.6858 | 1.3364 | 8200 | 0.7066 | 0.6996 | 0.7005 | 0.7144 | 0.6996 |
101
+ | 0.706 | 1.3690 | 8400 | 0.6956 | 0.7044 | 0.7053 | 0.7160 | 0.7044 |
102
+ | 0.7072 | 1.4016 | 8600 | 0.7158 | 0.6956 | 0.6953 | 0.7114 | 0.6956 |
103
+ | 0.6896 | 1.4342 | 8800 | 0.7090 | 0.6948 | 0.6952 | 0.7083 | 0.6948 |
104
+ | 0.6891 | 1.4668 | 9000 | 0.6936 | 0.6964 | 0.6977 | 0.7059 | 0.6964 |
105
+ | 0.6577 | 1.4993 | 9200 | 0.6926 | 0.7060 | 0.7072 | 0.7143 | 0.7060 |
106
+ | 0.6961 | 1.5319 | 9400 | 0.6792 | 0.7108 | 0.7106 | 0.7113 | 0.7108 |
107
+ | 0.6826 | 1.5645 | 9600 | 0.6843 | 0.7060 | 0.7066 | 0.7088 | 0.7060 |
108
+ | 0.695 | 1.5971 | 9800 | 0.6956 | 0.6896 | 0.6899 | 0.7013 | 0.6896 |
109
+ | 0.6904 | 1.6297 | 10000 | 0.7056 | 0.6948 | 0.6956 | 0.7030 | 0.6948 |
110
+ | 0.6982 | 1.6623 | 10200 | 0.6865 | 0.6988 | 0.6997 | 0.7041 | 0.6988 |
111
+ | 0.6723 | 1.6949 | 10400 | 0.7029 | 0.6932 | 0.6941 | 0.7105 | 0.6932 |
112
+ | 0.6658 | 1.7275 | 10600 | 0.6882 | 0.7060 | 0.7071 | 0.7122 | 0.7060 |
113
+ | 0.6929 | 1.7601 | 10800 | 0.6915 | 0.7028 | 0.7035 | 0.7139 | 0.7028 |
114
+ | 0.6742 | 1.7927 | 11000 | 0.6908 | 0.7044 | 0.7050 | 0.7171 | 0.7044 |
115
+ | 0.694 | 1.8253 | 11200 | 0.6960 | 0.7020 | 0.7021 | 0.7132 | 0.7020 |
116
+ | 0.6839 | 1.8579 | 11400 | 0.6894 | 0.7060 | 0.7069 | 0.7191 | 0.7060 |
117
+ | 0.682 | 1.8905 | 11600 | 0.6930 | 0.7020 | 0.7030 | 0.7161 | 0.7020 |
118
+ | 0.6806 | 1.9231 | 11800 | 0.6800 | 0.7112 | 0.7117 | 0.7182 | 0.7112 |
119
+ | 0.6936 | 1.9557 | 12000 | 0.6718 | 0.7076 | 0.7080 | 0.7143 | 0.7076 |
120
+ | 0.6917 | 1.9883 | 12200 | 0.6877 | 0.6972 | 0.6979 | 0.7088 | 0.6972 |
121
+ | 0.5941 | 2.0209 | 12400 | 0.6877 | 0.7161 | 0.7159 | 0.7168 | 0.7161 |
122
+ | 0.5729 | 2.0535 | 12600 | 0.7059 | 0.7120 | 0.7128 | 0.7165 | 0.7120 |
123
+ | 0.5849 | 2.0860 | 12800 | 0.7126 | 0.7084 | 0.7099 | 0.7181 | 0.7084 |
124
+ | 0.5937 | 2.1186 | 13000 | 0.6982 | 0.7137 | 0.7149 | 0.7220 | 0.7137 |
125
+ | 0.5975 | 2.1512 | 13200 | 0.7067 | 0.7048 | 0.7056 | 0.7143 | 0.7048 |
126
+ | 0.5877 | 2.1838 | 13400 | 0.7041 | 0.7088 | 0.7096 | 0.7124 | 0.7088 |
127
+ | 0.5801 | 2.2164 | 13600 | 0.7021 | 0.7185 | 0.7197 | 0.7249 | 0.7185 |
128
+ | 0.5897 | 2.2490 | 13800 | 0.7370 | 0.7012 | 0.7020 | 0.7160 | 0.7012 |
129
+ | 0.5986 | 2.2816 | 14000 | 0.6885 | 0.7173 | 0.7175 | 0.7211 | 0.7173 |
130
+ | 0.5702 | 2.3142 | 14200 | 0.6967 | 0.7201 | 0.7212 | 0.7251 | 0.7201 |
131
+ | 0.5885 | 2.3468 | 14400 | 0.6928 | 0.7084 | 0.7094 | 0.7173 | 0.7084 |
132
+ | 0.5955 | 2.3794 | 14600 | 0.6889 | 0.7165 | 0.7175 | 0.7222 | 0.7165 |
133
+ | 0.5981 | 2.4120 | 14800 | 0.6862 | 0.7193 | 0.7198 | 0.7258 | 0.7193 |
134
+ | 0.5974 | 2.4446 | 15000 | 0.6951 | 0.7165 | 0.7174 | 0.7244 | 0.7165 |
135
+ | 0.6057 | 2.4772 | 15200 | 0.6984 | 0.7108 | 0.7115 | 0.7199 | 0.7108 |
136
+ | 0.5939 | 2.5098 | 15400 | 0.7005 | 0.7169 | 0.7180 | 0.7248 | 0.7169 |
137
+ | 0.6026 | 2.5424 | 15600 | 0.7110 | 0.7120 | 0.7130 | 0.7213 | 0.7120 |
138
+ | 0.5794 | 2.5750 | 15800 | 0.7021 | 0.7213 | 0.7221 | 0.7285 | 0.7213 |
139
+ | 0.5743 | 2.6076 | 16000 | 0.6961 | 0.7157 | 0.7161 | 0.7222 | 0.7157 |
140
+ | 0.5987 | 2.6402 | 16200 | 0.6909 | 0.7201 | 0.7211 | 0.7258 | 0.7201 |
141
+ | 0.5741 | 2.6728 | 16400 | 0.7035 | 0.7084 | 0.7090 | 0.7163 | 0.7084 |
142
+ | 0.5628 | 2.7053 | 16600 | 0.7137 | 0.7068 | 0.7073 | 0.7210 | 0.7068 |
143
+ | 0.5632 | 2.7379 | 16800 | 0.7102 | 0.7084 | 0.7094 | 0.7270 | 0.7084 |
144
+ | 0.6049 | 2.7705 | 17000 | 0.6855 | 0.7181 | 0.7189 | 0.7274 | 0.7181 |
145
+ | 0.578 | 2.8031 | 17200 | 0.6946 | 0.7165 | 0.7172 | 0.7245 | 0.7165 |
146
+ | 0.5795 | 2.8357 | 17400 | 0.6919 | 0.7161 | 0.7169 | 0.7222 | 0.7161 |
147
+ | 0.5507 | 2.8683 | 17600 | 0.6898 | 0.7253 | 0.7260 | 0.7292 | 0.7253 |
148
+ | 0.5936 | 2.9009 | 17800 | 0.6892 | 0.7189 | 0.7197 | 0.7257 | 0.7189 |
149
+ | 0.5964 | 2.9335 | 18000 | 0.6826 | 0.7173 | 0.7182 | 0.7245 | 0.7173 |
150
+ | 0.5805 | 2.9661 | 18200 | 0.7005 | 0.7112 | 0.7124 | 0.7238 | 0.7112 |
151
+ | 0.6106 | 2.9987 | 18400 | 0.6886 | 0.7229 | 0.7236 | 0.7299 | 0.7229 |
152
+ | 0.4978 | 3.0313 | 18600 | 0.7325 | 0.7213 | 0.7218 | 0.7268 | 0.7213 |
153
+ | 0.5034 | 3.0639 | 18800 | 0.7586 | 0.7149 | 0.7158 | 0.7237 | 0.7149 |
154
+ | 0.4796 | 3.0965 | 19000 | 0.7483 | 0.7237 | 0.7242 | 0.7300 | 0.7237 |
155
+ | 0.5027 | 3.1291 | 19200 | 0.7195 | 0.7273 | 0.7282 | 0.7320 | 0.7273 |
156
+ | 0.4718 | 3.1617 | 19400 | 0.7576 | 0.7233 | 0.7239 | 0.7324 | 0.7233 |
157
+ | 0.4806 | 3.1943 | 19600 | 0.7427 | 0.7213 | 0.7219 | 0.7267 | 0.7213 |
158
+ | 0.4892 | 3.2269 | 19800 | 0.7586 | 0.7217 | 0.7222 | 0.7276 | 0.7217 |
159
+ | 0.4934 | 3.2595 | 20000 | 0.7593 | 0.7120 | 0.7128 | 0.7241 | 0.7120 |
160
+ | 0.4931 | 3.2920 | 20200 | 0.7459 | 0.7221 | 0.7228 | 0.7299 | 0.7221 |
161
+ | 0.4987 | 3.3246 | 20400 | 0.7301 | 0.7161 | 0.7168 | 0.7216 | 0.7161 |
162
+ | 0.4929 | 3.3572 | 20600 | 0.7499 | 0.7185 | 0.7193 | 0.7262 | 0.7185 |
163
+ | 0.4718 | 3.3898 | 20800 | 0.7398 | 0.7221 | 0.7228 | 0.7268 | 0.7221 |
164
+ | 0.4957 | 3.4224 | 21000 | 0.7343 | 0.7189 | 0.7197 | 0.7247 | 0.7189 |
165
+ | 0.496 | 3.4550 | 21200 | 0.7395 | 0.7141 | 0.7150 | 0.7231 | 0.7141 |
166
+ | 0.5113 | 3.4876 | 21400 | 0.7237 | 0.7213 | 0.7224 | 0.7287 | 0.7213 |
167
+ | 0.5009 | 3.5202 | 21600 | 0.7393 | 0.7205 | 0.7216 | 0.7276 | 0.7205 |
168
+ | 0.4793 | 3.5528 | 21800 | 0.7462 | 0.7217 | 0.7226 | 0.7278 | 0.7217 |
169
+ | 0.5007 | 3.5854 | 22000 | 0.7393 | 0.7229 | 0.7236 | 0.7284 | 0.7229 |
170
+ | 0.4836 | 3.6180 | 22200 | 0.7483 | 0.7173 | 0.7185 | 0.7275 | 0.7173 |
171
+ | 0.4885 | 3.6506 | 22400 | 0.7446 | 0.7201 | 0.7208 | 0.7285 | 0.7201 |
172
+ | 0.494 | 3.6832 | 22600 | 0.7368 | 0.7225 | 0.7235 | 0.7311 | 0.7225 |
173
+ | 0.476 | 3.7158 | 22800 | 0.7500 | 0.7165 | 0.7176 | 0.7278 | 0.7165 |
174
+ | 0.4787 | 3.7484 | 23000 | 0.7408 | 0.7201 | 0.7211 | 0.7281 | 0.7201 |
175
+ | 0.4983 | 3.7810 | 23200 | 0.7351 | 0.7181 | 0.7190 | 0.7265 | 0.7181 |
176
+ | 0.5081 | 3.8136 | 23400 | 0.7407 | 0.7197 | 0.7206 | 0.7287 | 0.7197 |
177
+ | 0.5209 | 3.8462 | 23600 | 0.7542 | 0.7137 | 0.7147 | 0.7248 | 0.7137 |
178
+ | 0.4924 | 3.8787 | 23800 | 0.7576 | 0.7169 | 0.7177 | 0.7280 | 0.7169 |
179
+ | 0.4939 | 3.9113 | 24000 | 0.7571 | 0.7161 | 0.7171 | 0.7258 | 0.7161 |
180
+ | 0.4792 | 3.9439 | 24200 | 0.7510 | 0.7201 | 0.7207 | 0.7290 | 0.7201 |
181
 
182
 
183
  ### Framework versions
 
185
  - Transformers 4.48.0.dev0
186
  - Pytorch 2.4.1+cu121
187
  - Datasets 3.1.0
188
+ - Tokenizers 0.21.0
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