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amaye15
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SwinV2-Base-Document-Classifier

Image Classification
Transformers
Safetensors
swinv2
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +212 -23
  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. training_args.bin +2 -2
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0271
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- - Accuracy: 0.9926
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- - F1: 0.9926
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- - Precision: 0.9926
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- - Recall: 0.9926
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  ## Model description
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@@ -47,30 +47,219 @@ The following hyperparameters were used during training:
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
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- - gradient_accumulation_steps: 4
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- - total_train_batch_size: 128
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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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- - training_steps: 800
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56
  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.0759 | 0.2 | 160 | 0.0482 | 0.9879 | 0.9878 | 0.9882 | 0.9877 |
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- | 0.0603 | 0.3 | 240 | 0.0323 | 0.9903 | 0.9902 | 0.9903 | 0.9902 |
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- | 0.0532 | 0.4 | 320 | 0.0391 | 0.9902 | 0.9902 | 0.9903 | 0.9901 |
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- | 0.0517 | 1.0 | 400 | 0.0558 | 0.9821 | 0.9818 | 0.9824 | 0.9819 |
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- | 0.046 | 1.1 | 480 | 0.0275 | 0.9917 | 0.9916 | 0.9917 | 0.9916 |
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- | 0.0355 | 1.2 | 560 | 0.0276 | 0.9923 | 0.9922 | 0.9923 | 0.9922 |
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- | 0.0365 | 1.3 | 640 | 0.0316 | 0.9922 | 0.9921 | 0.9922 | 0.9921 |
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- | 0.0342 | 1.4 | 720 | 0.0289 | 0.9923 | 0.9923 | 0.9923 | 0.9922 |
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- | 0.0509 | 2.01 | 800 | 0.0271 | 0.9926 | 0.9926 | 0.9926 | 0.9926 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.39.3
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- - Pytorch 2.3.1
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- - Datasets 2.19.2
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- - Tokenizers 0.15.2
 
20
 
21
  This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the None dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.0187
24
+ - Accuracy: 0.9964
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+ - F1: 0.9964
26
+ - Precision: 0.9964
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+ - Recall: 0.9964
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29
  ## Model description
30
 
 
47
  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
 
 
50
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - training_steps: 10000
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  ### Training results
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56
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
57
+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.4373 | 0.005 | 50 | 0.0894 | 0.9683 | 0.9683 | 0.9698 | 0.9683 |
59
+ | 0.1053 | 0.01 | 100 | 0.0463 | 0.9846 | 0.9846 | 0.9850 | 0.9846 |
60
+ | 0.0675 | 0.015 | 150 | 0.0604 | 0.9836 | 0.9836 | 0.9839 | 0.9836 |
61
+ | 0.0722 | 0.02 | 200 | 0.0336 | 0.9906 | 0.9906 | 0.9906 | 0.9906 |
62
+ | 0.079 | 0.025 | 250 | 0.0337 | 0.9908 | 0.9908 | 0.9908 | 0.9908 |
63
+ | 0.0707 | 0.03 | 300 | 0.0402 | 0.9886 | 0.9886 | 0.9887 | 0.9886 |
64
+ | 0.0565 | 0.035 | 350 | 0.0369 | 0.9918 | 0.9918 | 0.9918 | 0.9918 |
65
+ | 0.0406 | 0.04 | 400 | 0.0392 | 0.9914 | 0.9914 | 0.9916 | 0.9914 |
66
+ | 0.0436 | 0.045 | 450 | 0.0298 | 0.9936 | 0.9936 | 0.9936 | 0.9936 |
67
+ | 0.0288 | 0.05 | 500 | 0.0310 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
68
+ | 0.0507 | 0.055 | 550 | 0.0437 | 0.9908 | 0.9908 | 0.9909 | 0.9908 |
69
+ | 0.0447 | 0.06 | 600 | 0.0299 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
70
+ | 0.0414 | 0.065 | 650 | 0.0338 | 0.9940 | 0.9940 | 0.9940 | 0.994 |
71
+ | 0.0275 | 0.07 | 700 | 0.0338 | 0.9934 | 0.9934 | 0.9934 | 0.9934 |
72
+ | 0.0384 | 0.075 | 750 | 0.0339 | 0.9942 | 0.9942 | 0.9942 | 0.9942 |
73
+ | 0.0319 | 0.08 | 800 | 0.0301 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
74
+ | 0.0637 | 0.085 | 850 | 0.0407 | 0.9934 | 0.9934 | 0.9935 | 0.9934 |
75
+ | 0.0333 | 0.09 | 900 | 0.0282 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
76
+ | 0.0436 | 0.095 | 950 | 0.0261 | 0.9948 | 0.9948 | 0.9948 | 0.9948 |
77
+ | 0.0295 | 0.1 | 1000 | 0.0475 | 0.9918 | 0.9918 | 0.9920 | 0.9918 |
78
+ | 0.06 | 0.105 | 1050 | 0.0516 | 0.9908 | 0.9908 | 0.9910 | 0.9908 |
79
+ | 0.0553 | 0.11 | 1100 | 0.0234 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
80
+ | 0.0492 | 0.115 | 1150 | 0.0371 | 0.9924 | 0.9924 | 0.9924 | 0.9924 |
81
+ | 0.0374 | 0.12 | 1200 | 0.0268 | 0.9938 | 0.9938 | 0.9939 | 0.9938 |
82
+ | 0.0477 | 0.125 | 1250 | 0.0227 | 0.9944 | 0.9944 | 0.9944 | 0.9944 |
83
+ | 0.0399 | 0.13 | 1300 | 0.0272 | 0.9942 | 0.9942 | 0.9942 | 0.9942 |
84
+ | 0.0357 | 0.135 | 1350 | 0.0233 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
85
+ | 0.0109 | 0.14 | 1400 | 0.0253 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
86
+ | 0.0384 | 0.145 | 1450 | 0.0271 | 0.9940 | 0.9940 | 0.9940 | 0.9940 |
87
+ | 0.031 | 0.15 | 1500 | 0.0308 | 0.9940 | 0.9940 | 0.9940 | 0.994 |
88
+ | 0.0349 | 0.155 | 1550 | 0.0295 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
89
+ | 0.0457 | 0.16 | 1600 | 0.0325 | 0.9938 | 0.9938 | 0.9939 | 0.9938 |
90
+ | 0.0439 | 0.165 | 1650 | 0.0374 | 0.9918 | 0.9918 | 0.9919 | 0.9918 |
91
+ | 0.0351 | 0.17 | 1700 | 0.0304 | 0.9936 | 0.9936 | 0.9936 | 0.9936 |
92
+ | 0.0365 | 0.175 | 1750 | 0.0284 | 0.9940 | 0.9940 | 0.9940 | 0.9940 |
93
+ | 0.0401 | 0.18 | 1800 | 0.0285 | 0.9940 | 0.9940 | 0.9940 | 0.994 |
94
+ | 0.0356 | 0.185 | 1850 | 0.0234 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
95
+ | 0.0157 | 0.19 | 1900 | 0.0373 | 0.9934 | 0.9934 | 0.9936 | 0.9934 |
96
+ | 0.0304 | 0.195 | 1950 | 0.0216 | 0.9954 | 0.9954 | 0.9954 | 0.9954 |
97
+ | 0.0259 | 0.2 | 2000 | 0.0281 | 0.9944 | 0.9944 | 0.9944 | 0.9944 |
98
+ | 0.0344 | 0.205 | 2050 | 0.0272 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
99
+ | 0.0393 | 0.21 | 2100 | 0.0234 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
100
+ | 0.0165 | 0.215 | 2150 | 0.0251 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
101
+ | 0.0336 | 0.22 | 2200 | 0.0297 | 0.9938 | 0.9938 | 0.9938 | 0.9938 |
102
+ | 0.0333 | 0.225 | 2250 | 0.0258 | 0.9946 | 0.9946 | 0.9946 | 0.9946 |
103
+ | 0.0302 | 0.23 | 2300 | 0.0294 | 0.9936 | 0.9936 | 0.9936 | 0.9936 |
104
+ | 0.0461 | 0.235 | 2350 | 0.0218 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
105
+ | 0.0439 | 0.24 | 2400 | 0.0308 | 0.9932 | 0.9932 | 0.9933 | 0.9932 |
106
+ | 0.0253 | 0.245 | 2450 | 0.0229 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
107
+ | 0.0206 | 0.25 | 2500 | 0.0257 | 0.9946 | 0.9946 | 0.9946 | 0.9946 |
108
+ | 0.0285 | 0.255 | 2550 | 0.0219 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
109
+ | 0.032 | 0.26 | 2600 | 0.0250 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
110
+ | 0.0467 | 0.265 | 2650 | 0.0296 | 0.9934 | 0.9934 | 0.9935 | 0.9934 |
111
+ | 0.0186 | 0.27 | 2700 | 0.0241 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
112
+ | 0.0377 | 0.275 | 2750 | 0.0252 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
113
+ | 0.0245 | 0.28 | 2800 | 0.0244 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
114
+ | 0.0238 | 0.285 | 2850 | 0.0216 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
115
+ | 0.0381 | 0.29 | 2900 | 0.0234 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
116
+ | 0.0215 | 0.295 | 2950 | 0.0242 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
117
+ | 0.0327 | 0.3 | 3000 | 0.0342 | 0.9926 | 0.9926 | 0.9927 | 0.9926 |
118
+ | 0.0274 | 0.305 | 3050 | 0.0229 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
119
+ | 0.0294 | 0.31 | 3100 | 0.0214 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
120
+ | 0.035 | 0.315 | 3150 | 0.0228 | 0.9946 | 0.9946 | 0.9946 | 0.9946 |
121
+ | 0.0321 | 0.32 | 3200 | 0.0198 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
122
+ | 0.0106 | 0.325 | 3250 | 0.0237 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
123
+ | 0.028 | 0.33 | 3300 | 0.0232 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
124
+ | 0.0275 | 0.335 | 3350 | 0.0262 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
125
+ | 0.0435 | 0.34 | 3400 | 0.0239 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
126
+ | 0.0444 | 0.345 | 3450 | 0.0234 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
127
+ | 0.051 | 0.35 | 3500 | 0.0206 | 0.9954 | 0.9954 | 0.9954 | 0.9954 |
128
+ | 0.0202 | 0.355 | 3550 | 0.0204 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
129
+ | 0.024 | 0.36 | 3600 | 0.0238 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
130
+ | 0.0265 | 0.365 | 3650 | 0.0239 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
131
+ | 0.0311 | 0.37 | 3700 | 0.0234 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
132
+ | 0.03 | 0.375 | 3750 | 0.0237 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
133
+ | 0.027 | 0.38 | 3800 | 0.0230 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
134
+ | 0.0356 | 0.385 | 3850 | 0.0199 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
135
+ | 0.0264 | 0.39 | 3900 | 0.0222 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
136
+ | 0.0162 | 0.395 | 3950 | 0.0225 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
137
+ | 0.0285 | 0.4 | 4000 | 0.0212 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
138
+ | 0.0158 | 0.405 | 4050 | 0.0222 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
139
+ | 0.0348 | 0.41 | 4100 | 0.0209 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
140
+ | 0.0102 | 0.415 | 4150 | 0.0207 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
141
+ | 0.0158 | 0.42 | 4200 | 0.0217 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
142
+ | 0.0297 | 0.425 | 4250 | 0.0234 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
143
+ | 0.0318 | 0.43 | 4300 | 0.0206 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
144
+ | 0.0269 | 0.435 | 4350 | 0.0204 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
145
+ | 0.0015 | 0.44 | 4400 | 0.0210 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
146
+ | 0.0068 | 0.445 | 4450 | 0.0219 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
147
+ | 0.0294 | 0.45 | 4500 | 0.0216 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
148
+ | 0.0276 | 0.455 | 4550 | 0.0219 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
149
+ | 0.0245 | 0.46 | 4600 | 0.0196 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
150
+ | 0.0149 | 0.465 | 4650 | 0.0198 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
151
+ | 0.0139 | 0.47 | 4700 | 0.0202 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
152
+ | 0.0206 | 0.475 | 4750 | 0.0215 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
153
+ | 0.0047 | 0.48 | 4800 | 0.0219 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
154
+ | 0.0153 | 0.485 | 4850 | 0.0206 | 0.9972 | 0.9972 | 0.9972 | 0.9972 |
155
+ | 0.0312 | 0.49 | 4900 | 0.0168 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
156
+ | 0.0308 | 0.495 | 4950 | 0.0195 | 0.9954 | 0.9954 | 0.9954 | 0.9954 |
157
+ | 0.0417 | 0.5 | 5000 | 0.0183 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
158
+ | 0.0105 | 0.505 | 5050 | 0.0194 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
159
+ | 0.0256 | 0.51 | 5100 | 0.0170 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
160
+ | 0.0114 | 0.515 | 5150 | 0.0183 | 0.9972 | 0.9972 | 0.9972 | 0.9972 |
161
+ | 0.0253 | 0.52 | 5200 | 0.0187 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
162
+ | 0.0256 | 0.525 | 5250 | 0.0196 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
163
+ | 0.0527 | 0.53 | 5300 | 0.0171 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
164
+ | 0.0236 | 0.535 | 5350 | 0.0173 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
165
+ | 0.0199 | 0.54 | 5400 | 0.0168 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
166
+ | 0.0145 | 0.545 | 5450 | 0.0220 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
167
+ | 0.0208 | 0.55 | 5500 | 0.0215 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
168
+ | 0.0234 | 0.555 | 5550 | 0.0192 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
169
+ | 0.0214 | 0.56 | 5600 | 0.0171 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
170
+ | 0.0226 | 0.565 | 5650 | 0.0169 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
171
+ | 0.0089 | 0.57 | 5700 | 0.0174 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
172
+ | 0.0215 | 0.575 | 5750 | 0.0192 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
173
+ | 0.0034 | 0.58 | 5800 | 0.0202 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
174
+ | 0.0275 | 0.585 | 5850 | 0.0178 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
175
+ | 0.0205 | 0.59 | 5900 | 0.0169 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
176
+ | 0.0089 | 0.595 | 5950 | 0.0184 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
177
+ | 0.0185 | 0.6 | 6000 | 0.0168 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
178
+ | 0.0193 | 0.605 | 6050 | 0.0159 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
179
+ | 0.0071 | 0.61 | 6100 | 0.0170 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
180
+ | 0.009 | 0.615 | 6150 | 0.0166 | 0.9974 | 0.9974 | 0.9974 | 0.9974 |
181
+ | 0.0222 | 0.62 | 6200 | 0.0167 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
182
+ | 0.0129 | 0.625 | 6250 | 0.0200 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
183
+ | 0.0107 | 0.63 | 6300 | 0.0182 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
184
+ | 0.0161 | 0.635 | 6350 | 0.0186 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
185
+ | 0.02 | 0.64 | 6400 | 0.0178 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
186
+ | 0.011 | 0.645 | 6450 | 0.0173 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
187
+ | 0.0172 | 0.65 | 6500 | 0.0193 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
188
+ | 0.0265 | 0.655 | 6550 | 0.0180 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
189
+ | 0.0248 | 0.66 | 6600 | 0.0176 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
190
+ | 0.0265 | 0.665 | 6650 | 0.0182 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
191
+ | 0.0062 | 0.67 | 6700 | 0.0196 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
192
+ | 0.0066 | 0.675 | 6750 | 0.0206 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
193
+ | 0.0346 | 0.68 | 6800 | 0.0185 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
194
+ | 0.0337 | 0.685 | 6850 | 0.0167 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
195
+ | 0.0128 | 0.69 | 6900 | 0.0189 | 0.9960 | 0.9960 | 0.9960 | 0.9960 |
196
+ | 0.0132 | 0.695 | 6950 | 0.0166 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
197
+ | 0.0153 | 0.7 | 7000 | 0.0166 | 0.9972 | 0.9972 | 0.9972 | 0.9972 |
198
+ | 0.0034 | 0.705 | 7050 | 0.0175 | 0.9972 | 0.9972 | 0.9972 | 0.9972 |
199
+ | 0.0046 | 0.71 | 7100 | 0.0180 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
200
+ | 0.0221 | 0.715 | 7150 | 0.0168 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
201
+ | 0.0089 | 0.72 | 7200 | 0.0192 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
202
+ | 0.0222 | 0.725 | 7250 | 0.0165 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
203
+ | 0.0051 | 0.73 | 7300 | 0.0172 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
204
+ | 0.0191 | 0.735 | 7350 | 0.0162 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
205
+ | 0.0136 | 0.74 | 7400 | 0.0165 | 0.9972 | 0.9972 | 0.9972 | 0.9972 |
206
+ | 0.0108 | 0.745 | 7450 | 0.0189 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
207
+ | 0.0265 | 0.75 | 7500 | 0.0175 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
208
+ | 0.0038 | 0.755 | 7550 | 0.0187 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
209
+ | 0.0074 | 0.76 | 7600 | 0.0172 | 0.9972 | 0.9972 | 0.9972 | 0.9972 |
210
+ | 0.0258 | 0.765 | 7650 | 0.0169 | 0.9972 | 0.9972 | 0.9972 | 0.9972 |
211
+ | 0.0378 | 0.77 | 7700 | 0.0175 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
212
+ | 0.0035 | 0.775 | 7750 | 0.0186 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
213
+ | 0.013 | 0.78 | 7800 | 0.0206 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
214
+ | 0.003 | 0.785 | 7850 | 0.0178 | 0.9972 | 0.9972 | 0.9972 | 0.9972 |
215
+ | 0.0204 | 0.79 | 7900 | 0.0189 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
216
+ | 0.0233 | 0.795 | 7950 | 0.0173 | 0.9972 | 0.9972 | 0.9972 | 0.9972 |
217
+ | 0.0172 | 0.8 | 8000 | 0.0181 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
218
+ | 0.0053 | 0.805 | 8050 | 0.0206 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
219
+ | 0.0093 | 0.81 | 8100 | 0.0195 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
220
+ | 0.0094 | 0.815 | 8150 | 0.0180 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
221
+ | 0.0096 | 0.82 | 8200 | 0.0188 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
222
+ | 0.0099 | 0.825 | 8250 | 0.0184 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
223
+ | 0.0099 | 0.83 | 8300 | 0.0188 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
224
+ | 0.0034 | 0.835 | 8350 | 0.0181 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
225
+ | 0.0104 | 0.84 | 8400 | 0.0180 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
226
+ | 0.0058 | 0.845 | 8450 | 0.0185 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
227
+ | 0.0074 | 0.85 | 8500 | 0.0191 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
228
+ | 0.019 | 0.855 | 8550 | 0.0179 | 0.9972 | 0.9972 | 0.9972 | 0.9972 |
229
+ | 0.0189 | 0.86 | 8600 | 0.0182 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
230
+ | 0.0142 | 0.865 | 8650 | 0.0182 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
231
+ | 0.0079 | 0.87 | 8700 | 0.0186 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
232
+ | 0.0191 | 0.875 | 8750 | 0.0180 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
233
+ | 0.0032 | 0.88 | 8800 | 0.0191 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
234
+ | 0.0105 | 0.885 | 8850 | 0.0185 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
235
+ | 0.0054 | 0.89 | 8900 | 0.0172 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
236
+ | 0.0076 | 0.895 | 8950 | 0.0170 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
237
+ | 0.016 | 0.9 | 9000 | 0.0177 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
238
+ | 0.0041 | 0.905 | 9050 | 0.0173 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
239
+ | 0.0105 | 0.91 | 9100 | 0.0174 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
240
+ | 0.018 | 0.915 | 9150 | 0.0182 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
241
+ | 0.0056 | 0.92 | 9200 | 0.0178 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
242
+ | 0.0064 | 0.925 | 9250 | 0.0178 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
243
+ | 0.0107 | 0.93 | 9300 | 0.0175 | 0.9970 | 0.9970 | 0.9970 | 0.9970 |
244
+ | 0.0083 | 0.935 | 9350 | 0.0179 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
245
+ | 0.0065 | 0.94 | 9400 | 0.0180 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
246
+ | 0.0117 | 0.945 | 9450 | 0.0179 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
247
+ | 0.0108 | 0.95 | 9500 | 0.0182 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
248
+ | 0.0135 | 0.955 | 9550 | 0.0184 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
249
+ | 0.0108 | 0.96 | 9600 | 0.0186 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
250
+ | 0.0075 | 0.965 | 9650 | 0.0185 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
251
+ | 0.0308 | 0.97 | 9700 | 0.0187 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
252
+ | 0.0012 | 0.975 | 9750 | 0.0183 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
253
+ | 0.0002 | 0.98 | 9800 | 0.0187 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
254
+ | 0.0106 | 0.985 | 9850 | 0.0186 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
255
+ | 0.0046 | 0.99 | 9900 | 0.0187 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
256
+ | 0.0206 | 0.995 | 9950 | 0.0187 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
257
+ | 0.0112 | 1.0 | 10000 | 0.0187 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
258
 
259
 
260
  ### Framework versions
261
 
262
+ - Transformers 4.43.3
263
+ - Pytorch 2.4.0
264
+ - Datasets 2.20.0
265
+ - Tokenizers 0.19.1
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