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library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
model-index:
- name: deberta_MP_dynamic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta_MP_dynamic
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/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
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