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deberta_MP_dynamic

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metadata 
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
  - generated_from_trainer
model-index:
  - name: deberta_MP_dynamic
    results: []

deberta_MP_dynamic

This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1315
  • Macro F1: 0.6518
  • Micro F1: 0.6962
  • Macro Precision: 0.6229
  • Macro Recall: 0.6869
  • Micro Precision: 0.6614
  • Micro Recall: 0.7348
  • Exact Match Ratio: 0.0764
  • Macro Roc Auc: 0.9035

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 0.1
  • num_epochs: 1000

Training results

Training Loss Epoch Step Validation Loss Macro F1 Micro F1 Macro Precision Macro Recall Micro Precision Micro Recall Exact Match Ratio Macro Roc Auc
0.1612 12.8312 500 0.1435 0.2058 0.3965 0.3632 0.1788 0.5793 0.3014 0.0187 0.7830
0.1494 25.6494 1000 0.1391 0.4377 0.5473 0.4825 0.4386 0.5611 0.5341 0.0284 0.8402
0.1443 38.4675 1500 0.1366 0.4759 0.5803 0.5350 0.4824 0.5938 0.5674 0.0315 0.8641
0.1404 51.2857 2000 0.1334 0.5131 0.6173 0.5879 0.4986 0.6492 0.5884 0.0518 0.8832
0.1376 64.1039 2500 0.1319 0.5475 0.6361 0.5572 0.5796 0.5942 0.6843 0.0328 0.8915
0.1357 76.9351 3000 0.1314 0.5660 0.6440 0.5519 0.6105 0.6023 0.6918 0.0337 0.8963
0.1328 89.7532 3500 0.1298 0.5918 0.6626 0.5663 0.6415 0.6128 0.7212 0.0395 0.9035
0.1311 102.5714 4000 0.1303 0.5968 0.6590 0.5597 0.6617 0.5945 0.7394 0.0365 0.9051
0.1300 115.3896 4500 0.1315 0.5922 0.6528 0.5358 0.6861 0.5751 0.7549 0.0296 0.9083
0.1282 128.2078 5000 0.1306 0.6106 0.6689 0.5691 0.6768 0.6095 0.7409 0.0435 0.9077
0.1265 141.0260 5500 0.1292 0.6195 0.6787 0.5987 0.6552 0.6361 0.7274 0.0520 0.9085
0.1248 153.8571 6000 0.1297 0.6180 0.6755 0.5711 0.6894 0.6052 0.7644 0.0407 0.9113
0.1238 166.6753 6500 0.1302 0.6154 0.6782 0.5973 0.6578 0.6469 0.7127 0.0539 0.9109
0.1230 179.4935 7000 0.1296 0.6288 0.6838 0.5932 0.6778 0.6421 0.7312 0.0584 0.9075
0.1221 192.3117 7500 0.1299 0.6275 0.6830 0.5998 0.6745 0.6389 0.7337 0.0582 0.9077
0.1205 205.1299 8000 0.1299 0.6316 0.6839 0.6201 0.6586 0.6585 0.7114 0.0594 0.9066
0.1201 217.9610 8500 0.1304 0.6306 0.6809 0.5783 0.7029 0.6141 0.7639 0.0447 0.9086
0.1189 230.7792 9000 0.1303 0.6330 0.6851 0.5977 0.6803 0.6427 0.7335 0.0541 0.9086
0.1184 243.5974 9500 0.1316 0.6303 0.6822 0.5795 0.7018 0.6196 0.7588 0.0451 0.9073
0.1184 256.4156 10000 0.1301 0.6362 0.6855 0.5955 0.6933 0.6377 0.7410 0.0545 0.9089
0.1180 269.2338 10500 0.1306 0.6373 0.6873 0.5997 0.6902 0.6405 0.7415 0.0557 0.9056
0.1175 282.0519 11000 0.1305 0.6390 0.6854 0.5882 0.7044 0.6273 0.7553 0.0479 0.9106
0.1171 294.8831 11500 0.1300 0.6392 0.6908 0.6201 0.6708 0.6598 0.7250 0.0636 0.9062
0.1169 307.7013 12000 0.1304 0.6398 0.6876 0.5901 0.7043 0.6285 0.7591 0.0512 0.9104
0.1154 320.5195 12500 0.1305 0.6388 0.6898 0.6170 0.6742 0.6647 0.7168 0.0690 0.9059
0.1151 333.3377 13000 0.1306 0.6392 0.6867 0.5990 0.6946 0.6395 0.7414 0.0573 0.9092
0.1156 346.1558 13500 0.1312 0.6369 0.6882 0.6061 0.6851 0.6482 0.7334 0.0609 0.9078
0.1147 358.9870 14000 0.1313 0.6378 0.6882 0.5958 0.6942 0.6426 0.7409 0.0596 0.9089
0.1140 371.8052 14500 0.1311 0.6397 0.6896 0.6088 0.6822 0.6550 0.7279 0.0651 0.9066
0.1138 384.6234 15000 0.1319 0.6412 0.6891 0.5995 0.6953 0.6434 0.7417 0.0581 0.9080
0.1136 397.4416 15500 0.1307 0.6428 0.6906 0.6087 0.6887 0.6462 0.7415 0.0622 0.9068
0.1131 410.2597 16000 0.1318 0.6399 0.6890 0.5955 0.6970 0.6421 0.7433 0.0631 0.9067
0.1129 423.0779 16500 0.1317 0.6407 0.6904 0.6019 0.6954 0.6497 0.7366 0.0634 0.9086
0.1126 435.9091 17000 0.1304 0.6415 0.6920 0.6218 0.6707 0.6677 0.7181 0.0683 0.9064
0.1123 448.7273 17500 0.1317 0.6412 0.6895 0.5953 0.6999 0.6399 0.7475 0.0593 0.9070
0.1121 461.5455 18000 0.1326 0.6414 0.6904 0.6008 0.6966 0.6467 0.7404 0.0609 0.9063
0.1122 474.3636 18500 0.1322 0.6414 0.6893 0.6037 0.6917 0.6451 0.7401 0.0607 0.9046
0.1114 487.1818 19000 0.1313 0.6419 0.6922 0.6065 0.6880 0.6523 0.7372 0.0644 0.9076
0.1110 500.0 19500 0.1310 0.6430 0.6938 0.6304 0.6629 0.6796 0.7086 0.0772 0.9034
0.1110 512.8312 20000 0.1318 0.6436 0.6930 0.6151 0.6836 0.6614 0.7278 0.0675 0.9045
0.1107 525.6494 20500 0.1313 0.6413 0.6919 0.6100 0.6843 0.6545 0.7337 0.0669 0.9056
0.1107 538.4675 21000 0.1318 0.6461 0.6936 0.6102 0.6923 0.6549 0.7370 0.0657 0.9051
0.1105 551.2857 21500 0.1321 0.6410 0.6896 0.5957 0.7011 0.6399 0.7478 0.0589 0.9059
0.1099 564.1039 22000 0.1327 0.6458 0.6926 0.6015 0.7041 0.6440 0.7491 0.0598 0.9052
0.1100 576.9351 22500 0.1325 0.6437 0.6922 0.5991 0.7011 0.6438 0.7485 0.0626 0.9061
0.1095 589.7532 23000 0.1325 0.6428 0.6930 0.6055 0.6924 0.6533 0.7378 0.0643 0.9053
0.1098 602.5714 23500 0.1313 0.6470 0.6956 0.6209 0.6807 0.6654 0.7288 0.0717 0.9035
0.1097 615.3896 24000 0.1324 0.6482 0.6959 0.6072 0.6981 0.6530 0.7449 0.0671 0.9055
0.1091 628.2078 24500 0.1324 0.6468 0.6933 0.6113 0.6933 0.6532 0.7386 0.0662 0.9035
0.1091 641.0260 25000 0.1322 0.6464 0.6944 0.6059 0.6966 0.6508 0.7442 0.0692 0.9044
0.1088 653.8571 25500 0.1319 0.6517 0.6977 0.6230 0.6863 0.6682 0.7299 0.0750 0.9036
0.1086 666.6753 26000 0.1315 0.6478 0.6970 0.6229 0.6807 0.6678 0.7290 0.0739 0.9033
0.1087 679.4935 26500 0.1328 0.6493 0.6945 0.6060 0.7041 0.6459 0.7510 0.0636 0.9055
0.1086 692.3117 27000 0.1329 0.6450 0.6933 0.6032 0.6972 0.6472 0.7464 0.0655 0.9020
0.1085 705.1299 27500 0.1328 0.6452 0.6938 0.6142 0.6871 0.6594 0.7321 0.0714 0.9024
0.1081 717.9610 28000 0.1326 0.6491 0.6960 0.6132 0.6933 0.6596 0.7366 0.0709 0.9027
0.1083 730.7792 28500 0.1325 0.6484 0.6951 0.6119 0.6933 0.6552 0.7403 0.0695 0.9044
0.1078 743.5974 29000 0.1326 0.6487 0.6955 0.6167 0.6884 0.6605 0.7344 0.0700 0.9032
0.1080 756.4156 29500 0.1322 0.6467 0.6948 0.6169 0.6845 0.6591 0.7346 0.0733 0.9018
0.1078 769.2338 30000 0.1325 0.6472 0.6948 0.6095 0.6933 0.6564 0.7380 0.0696 0.9034
0.1075 782.0519 30500 0.1326 0.6484 0.6962 0.6102 0.6944 0.6545 0.7436 0.0696 0.9019
0.1078 794.8831 31000 0.1327 0.6497 0.6962 0.6132 0.6941 0.6570 0.7404 0.0705 0.9035
0.1076 807.7013 31500 0.1328 0.6484 0.6958 0.6128 0.6925 0.6577 0.7386 0.0696 0.9050
0.1074 820.5195 32000 0.1328 0.6475 0.6958 0.6098 0.6942 0.6574 0.7390 0.0722 0.9041
0.1074 833.3377 32500 0.1321 0.6516 0.6982 0.6202 0.6906 0.6636 0.7367 0.0739 0.9040
0.1074 846.1558 33000 0.1326 0.6486 0.6967 0.6117 0.6946 0.6572 0.7413 0.0714 0.9037
0.1072 858.9870 33500 0.1321 0.6503 0.6975 0.6215 0.6855 0.6669 0.7310 0.0755 0.9031
0.1070 871.8052 34000 0.1327 0.6488 0.6961 0.6117 0.6946 0.6576 0.7394 0.0714 0.9043
0.1072 884.6234 34500 0.1328 0.6501 0.6969 0.6118 0.6965 0.6571 0.7417 0.0722 0.9033
0.1071 897.4416 35000 0.1326 0.6507 0.6974 0.6137 0.6957 0.6593 0.7403 0.0726 0.9034
0.1071 910.2597 35500 0.1323 0.6517 0.6984 0.6225 0.6876 0.6666 0.7333 0.0751 0.9027
0.1071 923.0779 36000 0.1326 0.6513 0.6979 0.6154 0.6949 0.6599 0.7406 0.0722 0.9035
0.1072 935.9091 36500 0.1326 0.6511 0.6976 0.6150 0.6950 0.6598 0.7400 0.0726 0.9034
0.1070 948.7273 37000 0.1325 0.6513 0.6980 0.6170 0.6929 0.6621 0.7381 0.0744 0.9033
0.1071 961.5455 37500 0.1326 0.6509 0.6975 0.6147 0.6950 0.6596 0.7400 0.0722 0.9037
0.1068 974.3636 38000 0.1325 0.6513 0.6980 0.6164 0.6938 0.6615 0.7388 0.0733 0.9035
0.1072 987.1818 38500 0.1325 0.6511 0.6980 0.6154 0.6946 0.6608 0.7396 0.0736 0.9036
0.1071 1000.0 39000 0.1325 0.6513 0.6979 0.6157 0.6945 0.6608 0.7395 0.0732 0.9035

Framework versions

  • Transformers 5.12.1
  • Pytorch 2.5.1+cu121
  • Datasets 5.0.1
  • Tokenizers 0.22.2