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deberta_MP_dynamic

Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
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  1. README.md +57 -34
  2. best_model/model.safetensors +1 -1
  3. best_model/training_args.bin +1 -1
  4. model.safetensors +1 -1
  5. runs/Aug06_16-58-04_erc-hpc-comp248/events.out.tfevents.1786073374.erc-hpc-comp248.1390536.1 +3 -0
README.md CHANGED
@@ -16,15 +16,15 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0161
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- - Macro F1: 0.4774
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- - Micro F1: 0.6157
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- - Macro Precision: 0.7044
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- - Macro Recall: 0.3876
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- - Micro Precision: 0.7930
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- - Micro Recall: 0.5033
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- - Exact Match Ratio: 0.1179
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- - Macro Roc Auc: 0.9285
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  ## Model description
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@@ -43,7 +43,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -52,35 +52,58 @@ The following hyperparameters were used during training:
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_steps: 0.1
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- - num_epochs: 300
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  ### Training results
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59
  | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Macro Precision | Macro Recall | Micro Precision | Micro Recall | Exact Match Ratio | Macro Roc Auc |
60
  |:-------------:|:--------:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|:-----------------:|:-------------:|
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- | 0.0284 | 13.1579 | 500 | 0.0218 | 0.0295 | 0.1013 | 0.1733 | 0.0194 | 0.7882 | 0.0541 | 0.0097 | 0.7788 |
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- | 0.0245 | 26.3158 | 1000 | 0.0193 | 0.0866 | 0.2206 | 0.3332 | 0.0575 | 0.8743 | 0.1262 | 0.0393 | 0.8454 |
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- | 0.0220 | 39.4737 | 1500 | 0.0177 | 0.2134 | 0.4123 | 0.5269 | 0.1519 | 0.8249 | 0.2749 | 0.0708 | 0.8787 |
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- | 0.0196 | 52.6316 | 2000 | 0.0173 | 0.2260 | 0.3989 | 0.6134 | 0.1577 | 0.8608 | 0.2596 | 0.0719 | 0.8917 |
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- | 0.0181 | 65.7895 | 2500 | 0.0168 | 0.2959 | 0.5003 | 0.6590 | 0.2252 | 0.8068 | 0.3626 | 0.0832 | 0.9004 |
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- | 0.0174 | 78.9474 | 3000 | 0.0165 | 0.3242 | 0.5262 | 0.6748 | 0.2462 | 0.8011 | 0.3918 | 0.0869 | 0.9059 |
67
- | 0.0162 | 92.1053 | 3500 | 0.0163 | 0.3373 | 0.5380 | 0.6913 | 0.2584 | 0.8024 | 0.4047 | 0.0883 | 0.9097 |
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- | 0.0151 | 105.2632 | 4000 | 0.0162 | 0.3506 | 0.5519 | 0.6683 | 0.2737 | 0.8048 | 0.4199 | 0.0922 | 0.9130 |
69
- | 0.0140 | 118.4211 | 4500 | 0.0165 | 0.3816 | 0.5667 | 0.6721 | 0.3001 | 0.7953 | 0.4402 | 0.0933 | 0.9118 |
70
- | 0.0130 | 131.5789 | 5000 | 0.0165 | 0.3832 | 0.5772 | 0.6768 | 0.3069 | 0.7773 | 0.4590 | 0.0908 | 0.9156 |
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- | 0.0123 | 144.7368 | 5500 | 0.0167 | 0.4083 | 0.5708 | 0.6855 | 0.3245 | 0.7845 | 0.4487 | 0.0925 | 0.9159 |
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- | 0.0117 | 157.8947 | 6000 | 0.0166 | 0.4101 | 0.5904 | 0.6904 | 0.3310 | 0.7760 | 0.4764 | 0.0942 | 0.9164 |
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- | 0.0110 | 171.0526 | 6500 | 0.0163 | 0.4059 | 0.5742 | 0.7061 | 0.3145 | 0.8047 | 0.4464 | 0.0951 | 0.9182 |
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- | 0.0103 | 184.2105 | 7000 | 0.0168 | 0.4422 | 0.5878 | 0.6783 | 0.3585 | 0.7815 | 0.4710 | 0.0987 | 0.9194 |
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- | 0.0097 | 197.3684 | 7500 | 0.0161 | 0.4262 | 0.5869 | 0.7098 | 0.3383 | 0.7951 | 0.4650 | 0.0973 | 0.9215 |
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- | 0.0096 | 210.5263 | 8000 | 0.0169 | 0.4577 | 0.6076 | 0.6768 | 0.3725 | 0.7741 | 0.5 | 0.0982 | 0.9208 |
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- | 0.0091 | 223.6842 | 8500 | 0.0166 | 0.4603 | 0.5994 | 0.6960 | 0.3704 | 0.7838 | 0.4853 | 0.0983 | 0.9220 |
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- | 0.0088 | 236.8421 | 9000 | 0.0168 | 0.4557 | 0.6011 | 0.6903 | 0.3665 | 0.7842 | 0.4874 | 0.1018 | 0.9218 |
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- | 0.0086 | 250.0 | 9500 | 0.0170 | 0.4673 | 0.6144 | 0.7100 | 0.3820 | 0.7726 | 0.5100 | 0.1029 | 0.9217 |
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- | 0.0082 | 263.1579 | 10000 | 0.0165 | 0.4601 | 0.6057 | 0.6922 | 0.3692 | 0.7851 | 0.4930 | 0.1041 | 0.9231 |
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- | 0.0083 | 276.3158 | 10500 | 0.0166 | 0.4669 | 0.6052 | 0.6946 | 0.3741 | 0.7875 | 0.4915 | 0.1031 | 0.9229 |
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- | 0.0081 | 289.4737 | 11000 | 0.0168 | 0.4694 | 0.6096 | 0.6894 | 0.3794 | 0.7827 | 0.4992 | 0.1030 | 0.9228 |
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- | 0.0080 | 300.0 | 11400 | 0.0167 | 0.4672 | 0.6075 | 0.6897 | 0.3762 | 0.7847 | 0.4956 | 0.1024 | 0.9229 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
16
 
17
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 0.0168
20
+ - Macro F1: 0.4727
21
+ - Micro F1: 0.6043
22
+ - Macro Precision: 0.7187
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+ - Macro Recall: 0.3780
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+ - Micro Precision: 0.7985
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+ - Micro Recall: 0.4860
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+ - Exact Match Ratio: 0.1168
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+ - Macro Roc Auc: 0.9238
28
 
29
  ## Model description
30
 
 
43
  ### Training hyperparameters
44
 
45
  The following hyperparameters were used during training:
46
+ - learning_rate: 2e-05
47
  - train_batch_size: 16
48
  - eval_batch_size: 16
49
  - seed: 42
 
52
  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
53
  - lr_scheduler_type: cosine
54
  - lr_scheduler_warmup_steps: 0.1
55
+ - num_epochs: 600
56
 
57
  ### Training results
58
 
59
  | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Macro Precision | Macro Recall | Micro Precision | Micro Recall | Exact Match Ratio | Macro Roc Auc |
60
  |:-------------:|:--------:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|:-----------------:|:-------------:|
61
+ | 0.0357 | 13.1579 | 500 | 0.0258 | 0.0000 | 0.0000 | 0.0045 | 0.0000 | 0.0189 | 0.0000 | 0.0057 | 0.6533 |
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+ | 0.0273 | 26.3158 | 1000 | 0.0210 | 0.0371 | 0.1188 | 0.1858 | 0.0233 | 0.8534 | 0.0638 | 0.0143 | 0.7992 |
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+ | 0.0247 | 39.4737 | 1500 | 0.0196 | 0.0819 | 0.1937 | 0.4000 | 0.0522 | 0.8955 | 0.1086 | 0.0306 | 0.8427 |
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+ | 0.0227 | 52.6316 | 2000 | 0.0188 | 0.1215 | 0.2689 | 0.4948 | 0.0794 | 0.8786 | 0.1587 | 0.0454 | 0.8638 |
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+ | 0.0210 | 65.7895 | 2500 | 0.0178 | 0.1623 | 0.3199 | 0.5564 | 0.1077 | 0.8790 | 0.1955 | 0.0558 | 0.8793 |
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+ | 0.0202 | 78.9474 | 3000 | 0.0172 | 0.2124 | 0.3941 | 0.6246 | 0.1489 | 0.8656 | 0.2551 | 0.0757 | 0.8898 |
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+ | 0.0190 | 92.1053 | 3500 | 0.0167 | 0.2465 | 0.4484 | 0.6255 | 0.1757 | 0.8485 | 0.3047 | 0.0807 | 0.8969 |
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+ | 0.0180 | 105.2632 | 4000 | 0.0168 | 0.2283 | 0.4148 | 0.6575 | 0.1596 | 0.8725 | 0.2721 | 0.0770 | 0.9012 |
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+ | 0.0168 | 118.4211 | 4500 | 0.0165 | 0.2929 | 0.4806 | 0.6900 | 0.2143 | 0.8461 | 0.3356 | 0.0889 | 0.9026 |
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+ | 0.0162 | 131.5789 | 5000 | 0.0165 | 0.2999 | 0.4973 | 0.6551 | 0.2217 | 0.8266 | 0.3557 | 0.0876 | 0.9059 |
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+ | 0.0152 | 144.7368 | 5500 | 0.0163 | 0.3163 | 0.4897 | 0.6524 | 0.2296 | 0.8396 | 0.3456 | 0.0856 | 0.9082 |
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+ | 0.0147 | 157.8947 | 6000 | 0.0167 | 0.3460 | 0.5503 | 0.6838 | 0.2689 | 0.7918 | 0.4217 | 0.0912 | 0.9085 |
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+ | 0.0142 | 171.0526 | 6500 | 0.0166 | 0.3465 | 0.5168 | 0.6859 | 0.2588 | 0.8239 | 0.3765 | 0.0891 | 0.9107 |
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+ | 0.0135 | 184.2105 | 7000 | 0.0166 | 0.3614 | 0.5270 | 0.7102 | 0.2749 | 0.8203 | 0.3882 | 0.0892 | 0.9109 |
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+ | 0.0129 | 197.3684 | 7500 | 0.0161 | 0.3506 | 0.5254 | 0.7256 | 0.2600 | 0.8246 | 0.3855 | 0.0877 | 0.9110 |
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+ | 0.0128 | 210.5263 | 8000 | 0.0165 | 0.3648 | 0.5367 | 0.7236 | 0.2756 | 0.8168 | 0.3996 | 0.0927 | 0.9128 |
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+ | 0.0122 | 223.6842 | 8500 | 0.0161 | 0.3644 | 0.5436 | 0.7168 | 0.2734 | 0.8240 | 0.4056 | 0.0911 | 0.9138 |
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+ | 0.0116 | 236.8421 | 9000 | 0.0165 | 0.3697 | 0.5508 | 0.6876 | 0.2817 | 0.8151 | 0.4160 | 0.0893 | 0.9141 |
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+ | 0.0115 | 250.0 | 9500 | 0.0163 | 0.3962 | 0.5709 | 0.7120 | 0.3049 | 0.7996 | 0.4439 | 0.0914 | 0.9144 |
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+ | 0.0108 | 263.1579 | 10000 | 0.0170 | 0.4181 | 0.5795 | 0.7015 | 0.3288 | 0.7812 | 0.4606 | 0.0901 | 0.9152 |
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+ | 0.0106 | 276.3158 | 10500 | 0.0170 | 0.4134 | 0.5827 | 0.7131 | 0.3265 | 0.7829 | 0.4641 | 0.0886 | 0.9160 |
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+ | 0.0103 | 289.4737 | 11000 | 0.0166 | 0.4160 | 0.5826 | 0.7024 | 0.3269 | 0.7866 | 0.4627 | 0.0909 | 0.9155 |
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+ | 0.0099 | 302.6316 | 11500 | 0.0169 | 0.3875 | 0.5526 | 0.7278 | 0.2940 | 0.8180 | 0.4172 | 0.0932 | 0.9145 |
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+ | 0.0095 | 315.7895 | 12000 | 0.0168 | 0.3892 | 0.5476 | 0.7462 | 0.2940 | 0.8131 | 0.4128 | 0.0893 | 0.9153 |
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+ | 0.0093 | 328.9474 | 12500 | 0.0170 | 0.4017 | 0.5594 | 0.7369 | 0.3042 | 0.8139 | 0.4261 | 0.0954 | 0.9156 |
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+ | 0.0094 | 342.1053 | 13000 | 0.0170 | 0.4152 | 0.5597 | 0.7313 | 0.3187 | 0.8032 | 0.4295 | 0.0896 | 0.9159 |
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+ | 0.0089 | 355.2632 | 13500 | 0.0170 | 0.4377 | 0.5887 | 0.7235 | 0.3461 | 0.7898 | 0.4692 | 0.0952 | 0.9179 |
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+ | 0.0090 | 368.4211 | 14000 | 0.0173 | 0.4290 | 0.5792 | 0.7208 | 0.3343 | 0.7924 | 0.4564 | 0.0927 | 0.9158 |
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+ | 0.0087 | 381.5789 | 14500 | 0.0172 | 0.4378 | 0.5847 | 0.7241 | 0.3440 | 0.7901 | 0.4641 | 0.0937 | 0.9161 |
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+ | 0.0081 | 394.7368 | 15000 | 0.0169 | 0.4186 | 0.5693 | 0.7369 | 0.3201 | 0.8040 | 0.4407 | 0.0938 | 0.9163 |
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+ | 0.0081 | 407.8947 | 15500 | 0.0170 | 0.4349 | 0.5868 | 0.7210 | 0.3411 | 0.7900 | 0.4668 | 0.0924 | 0.9169 |
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+ | 0.0080 | 421.0526 | 16000 | 0.0175 | 0.4544 | 0.5936 | 0.7102 | 0.3612 | 0.7842 | 0.4776 | 0.0951 | 0.9170 |
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+ | 0.0079 | 434.2105 | 16500 | 0.0175 | 0.4580 | 0.5989 | 0.6976 | 0.3679 | 0.7806 | 0.4859 | 0.0952 | 0.9174 |
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+ | 0.0077 | 447.3684 | 17000 | 0.0173 | 0.4487 | 0.5907 | 0.7144 | 0.3527 | 0.7909 | 0.4714 | 0.0962 | 0.9170 |
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+ | 0.0076 | 460.5263 | 17500 | 0.0179 | 0.4580 | 0.5988 | 0.7039 | 0.3659 | 0.7815 | 0.4853 | 0.0958 | 0.9171 |
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+ | 0.0074 | 473.6842 | 18000 | 0.0176 | 0.4447 | 0.5866 | 0.7201 | 0.3461 | 0.7948 | 0.4648 | 0.0954 | 0.9167 |
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+ | 0.0073 | 486.8421 | 18500 | 0.0174 | 0.4468 | 0.5899 | 0.7157 | 0.3494 | 0.7948 | 0.4690 | 0.0952 | 0.9177 |
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+ | 0.0070 | 500.0 | 19000 | 0.0176 | 0.4511 | 0.5919 | 0.7119 | 0.3552 | 0.7917 | 0.4726 | 0.0974 | 0.9177 |
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+ | 0.0073 | 513.1579 | 19500 | 0.0174 | 0.4522 | 0.5915 | 0.7144 | 0.3541 | 0.7922 | 0.4719 | 0.0969 | 0.9176 |
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+ | 0.0070 | 526.3158 | 20000 | 0.0176 | 0.4547 | 0.5957 | 0.7105 | 0.3594 | 0.7886 | 0.4786 | 0.0981 | 0.9173 |
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+ | 0.0070 | 539.4737 | 20500 | 0.0176 | 0.4531 | 0.5924 | 0.7084 | 0.3567 | 0.7917 | 0.4733 | 0.0982 | 0.9174 |
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+ | 0.0072 | 552.6316 | 21000 | 0.0176 | 0.4560 | 0.5958 | 0.7118 | 0.3598 | 0.7905 | 0.4781 | 0.0983 | 0.9174 |
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+ | 0.0068 | 565.7895 | 21500 | 0.0177 | 0.4581 | 0.5965 | 0.7081 | 0.3621 | 0.7882 | 0.4799 | 0.0969 | 0.9173 |
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+ | 0.0070 | 578.9474 | 22000 | 0.0178 | 0.4607 | 0.5986 | 0.7038 | 0.3655 | 0.7863 | 0.4833 | 0.0978 | 0.9174 |
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+ | 0.0070 | 592.1053 | 22500 | 0.0177 | 0.4578 | 0.5963 | 0.7063 | 0.3619 | 0.7882 | 0.4795 | 0.0976 | 0.9173 |
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+ | 0.0071 | 600.0 | 22800 | 0.0177 | 0.4577 | 0.5964 | 0.7051 | 0.3619 | 0.7885 | 0.4796 | 0.0976 | 0.9173 |
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  ### Framework versions
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