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scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_1_alpha-jason

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PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,9 +20,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the massive dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.8052
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- - Accuracy: 0.8270
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- - F1: 0.8027
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  ## Model description
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@@ -53,118 +53,118 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
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- | 5.9621 | 0.27 | 5000 | 5.8534 | 0.3085 | 0.1374 |
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- | 4.2762 | 0.53 | 10000 | 4.2222 | 0.5506 | 0.4008 |
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- | 3.3857 | 0.8 | 15000 | 3.3996 | 0.6458 | 0.5306 |
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- | 2.6536 | 1.07 | 20000 | 2.9551 | 0.6961 | 0.5947 |
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- | 2.4701 | 1.34 | 25000 | 2.6730 | 0.7256 | 0.6394 |
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- | 2.2519 | 1.6 | 30000 | 2.4672 | 0.7521 | 0.6770 |
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- | 2.1267 | 1.87 | 35000 | 2.3678 | 0.7601 | 0.6970 |
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- | 1.7317 | 2.14 | 40000 | 2.2796 | 0.7712 | 0.7112 |
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- | 1.6837 | 2.41 | 45000 | 2.2315 | 0.7779 | 0.7218 |
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- | 1.6611 | 2.67 | 50000 | 2.1565 | 0.7839 | 0.7372 |
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- | 1.6647 | 2.94 | 55000 | 2.1357 | 0.7868 | 0.7468 |
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- | 1.3314 | 3.21 | 60000 | 2.1089 | 0.7911 | 0.7511 |
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- | 1.357 | 3.47 | 65000 | 2.0963 | 0.7926 | 0.7586 |
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- | 1.3339 | 3.74 | 70000 | 2.0273 | 0.7987 | 0.7619 |
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- | 1.2323 | 4.01 | 75000 | 2.0556 | 0.8011 | 0.7612 |
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- | 1.1101 | 4.28 | 80000 | 2.0695 | 0.8017 | 0.7633 |
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- | 1.1174 | 4.54 | 85000 | 2.0361 | 0.8043 | 0.7687 |
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- | 1.1121 | 4.81 | 90000 | 2.0272 | 0.8067 | 0.7749 |
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- | 0.9268 | 5.08 | 95000 | 2.0497 | 0.8053 | 0.7714 |
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- | 0.9366 | 5.34 | 100000 | 2.0511 | 0.8041 | 0.7726 |
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- | 0.9756 | 5.61 | 105000 | 2.0082 | 0.8052 | 0.7724 |
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- | 0.9703 | 5.88 | 110000 | 1.9988 | 0.8065 | 0.7766 |
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- | 0.8228 | 6.15 | 115000 | 2.0636 | 0.8066 | 0.7765 |
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- | 0.8037 | 6.41 | 120000 | 2.0380 | 0.8083 | 0.7817 |
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- | 0.8447 | 6.68 | 125000 | 2.0241 | 0.8101 | 0.7851 |
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- | 0.8646 | 6.95 | 130000 | 1.9780 | 0.8116 | 0.7830 |
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- | 0.7335 | 7.22 | 135000 | 2.0400 | 0.8101 | 0.7835 |
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- | 0.747 | 7.48 | 140000 | 2.0154 | 0.8099 | 0.7844 |
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- | 0.7445 | 7.75 | 145000 | 2.0174 | 0.8116 | 0.7841 |
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- | 0.7124 | 8.02 | 150000 | 2.0317 | 0.8116 | 0.7859 |
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- | 0.683 | 8.28 | 155000 | 2.0252 | 0.8129 | 0.7843 |
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- | 0.6882 | 8.55 | 160000 | 2.0055 | 0.8133 | 0.7888 |
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- | 0.6939 | 8.82 | 165000 | 2.0076 | 0.8129 | 0.7912 |
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- | 0.6298 | 9.09 | 170000 | 2.0252 | 0.8126 | 0.7863 |
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- | 0.6187 | 9.35 | 175000 | 2.0216 | 0.8120 | 0.7865 |
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- | 0.6316 | 9.62 | 180000 | 2.0150 | 0.8123 | 0.7900 |
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- | 0.6566 | 9.89 | 185000 | 1.9868 | 0.8155 | 0.7911 |
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- | 0.5797 | 10.15 | 190000 | 1.9903 | 0.8158 | 0.7918 |
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- | 0.5911 | 10.42 | 195000 | 1.9915 | 0.8152 | 0.7909 |
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- | 0.6004 | 10.69 | 200000 | 1.9793 | 0.8167 | 0.7929 |
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- | 0.6141 | 10.96 | 205000 | 1.9958 | 0.8145 | 0.7904 |
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- | 0.549 | 11.22 | 210000 | 1.9843 | 0.8155 | 0.7912 |
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- | 0.5552 | 11.49 | 215000 | 1.9643 | 0.8163 | 0.7926 |
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- | 0.5622 | 11.76 | 220000 | 1.9738 | 0.8179 | 0.7913 |
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- | 0.512 | 12.03 | 225000 | 1.9708 | 0.8166 | 0.7925 |
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- | 0.5349 | 12.29 | 230000 | 1.9533 | 0.8190 | 0.7953 |
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- | 0.531 | 12.56 | 235000 | 1.9436 | 0.8201 | 0.7952 |
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- | 0.5472 | 12.83 | 240000 | 1.9548 | 0.8174 | 0.7926 |
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- | 0.4982 | 13.09 | 245000 | 1.9473 | 0.8195 | 0.7958 |
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- | 0.4978 | 13.36 | 250000 | 1.9753 | 0.8190 | 0.7969 |
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- | 0.5024 | 13.63 | 255000 | 1.9694 | 0.8186 | 0.7957 |
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- | 0.5089 | 13.9 | 260000 | 1.9483 | 0.8188 | 0.7952 |
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- | 0.4887 | 14.16 | 265000 | 1.9363 | 0.8200 | 0.7960 |
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- | 0.4877 | 14.43 | 270000 | 1.9545 | 0.8189 | 0.7955 |
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- | 0.4843 | 14.7 | 275000 | 1.9503 | 0.8188 | 0.7962 |
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- | 0.4847 | 14.96 | 280000 | 1.9344 | 0.8195 | 0.7958 |
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- | 0.4666 | 15.23 | 285000 | 1.9349 | 0.8202 | 0.7947 |
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- | 0.4617 | 15.5 | 290000 | 1.9180 | 0.8217 | 0.7986 |
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- | 0.4643 | 15.77 | 295000 | 1.9007 | 0.8217 | 0.7982 |
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- | 0.4525 | 16.03 | 300000 | 1.9034 | 0.8199 | 0.7949 |
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- | 0.4446 | 16.3 | 305000 | 1.8952 | 0.8215 | 0.7987 |
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- | 0.4525 | 16.57 | 310000 | 1.9047 | 0.8226 | 0.7995 |
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- | 0.4566 | 16.84 | 315000 | 1.9056 | 0.8230 | 0.8001 |
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- | 0.4354 | 17.1 | 320000 | 1.8935 | 0.8227 | 0.7989 |
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- | 0.4326 | 17.37 | 325000 | 1.8901 | 0.8216 | 0.7979 |
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- | 0.4356 | 17.64 | 330000 | 1.9043 | 0.8217 | 0.7981 |
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- | 0.4334 | 17.9 | 335000 | 1.8808 | 0.8224 | 0.7972 |
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- | 0.4209 | 18.17 | 340000 | 1.8816 | 0.8227 | 0.7987 |
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- | 0.4187 | 18.44 | 345000 | 1.8874 | 0.8226 | 0.7993 |
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- | 0.4287 | 18.71 | 350000 | 1.8839 | 0.8217 | 0.7971 |
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- | 0.4256 | 18.97 | 355000 | 1.8734 | 0.8236 | 0.7978 |
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- | 0.4063 | 19.24 | 360000 | 1.8767 | 0.8242 | 0.8004 |
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- | 0.4174 | 19.51 | 365000 | 1.8699 | 0.8239 | 0.8002 |
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- | 0.3983 | 19.77 | 370000 | 1.8726 | 0.8244 | 0.7982 |
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- | 0.4005 | 20.04 | 375000 | 1.8641 | 0.8241 | 0.8000 |
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- | 0.4014 | 20.31 | 380000 | 1.8417 | 0.8249 | 0.7992 |
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- | 0.3983 | 20.58 | 385000 | 1.8640 | 0.8251 | 0.8010 |
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- | 0.3974 | 20.84 | 390000 | 1.8605 | 0.8253 | 0.7995 |
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- | 0.3946 | 21.11 | 395000 | 1.8348 | 0.8258 | 0.7995 |
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- | 0.3884 | 21.38 | 400000 | 1.8449 | 0.8246 | 0.7997 |
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- | 0.3947 | 21.65 | 405000 | 1.8415 | 0.8248 | 0.7983 |
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- | 0.387 | 21.91 | 410000 | 1.8375 | 0.8257 | 0.8020 |
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- | 0.3818 | 22.18 | 415000 | 1.8320 | 0.8257 | 0.7999 |
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- | 0.381 | 22.45 | 420000 | 1.8411 | 0.8249 | 0.7998 |
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- | 0.3765 | 22.71 | 425000 | 1.8431 | 0.8243 | 0.7969 |
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- | 0.3767 | 22.98 | 430000 | 1.8476 | 0.8236 | 0.7983 |
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- | 0.377 | 23.25 | 435000 | 1.8286 | 0.8256 | 0.8000 |
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- | 0.369 | 23.52 | 440000 | 1.8356 | 0.8263 | 0.8019 |
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- | 0.3698 | 23.78 | 445000 | 1.8325 | 0.8255 | 0.8022 |
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- | 0.3656 | 24.05 | 450000 | 1.8288 | 0.8259 | 0.8013 |
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- | 0.3705 | 24.32 | 455000 | 1.8214 | 0.8273 | 0.8024 |
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- | 0.3637 | 24.58 | 460000 | 1.8297 | 0.8258 | 0.8026 |
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- | 0.3561 | 24.85 | 465000 | 1.8298 | 0.8263 | 0.8025 |
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- | 0.3544 | 25.12 | 470000 | 1.8184 | 0.8267 | 0.8024 |
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- | 0.3505 | 25.39 | 475000 | 1.8172 | 0.8251 | 0.7998 |
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- | 0.3553 | 25.65 | 480000 | 1.8274 | 0.8260 | 0.8004 |
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- | 0.3565 | 25.92 | 485000 | 1.8214 | 0.8260 | 0.8002 |
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- | 0.3502 | 26.19 | 490000 | 1.8178 | 0.8262 | 0.8020 |
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- | 0.3505 | 26.46 | 495000 | 1.8188 | 0.8253 | 0.8009 |
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- | 0.3512 | 26.72 | 500000 | 1.8107 | 0.8261 | 0.8026 |
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- | 0.3505 | 26.99 | 505000 | 1.8175 | 0.8264 | 0.8035 |
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- | 0.3477 | 27.26 | 510000 | 1.8103 | 0.8270 | 0.8021 |
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- | 0.3441 | 27.52 | 515000 | 1.8166 | 0.8261 | 0.8017 |
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- | 0.3449 | 27.79 | 520000 | 1.8147 | 0.8263 | 0.8018 |
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- | 0.3375 | 28.06 | 525000 | 1.8144 | 0.8264 | 0.8013 |
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- | 0.3425 | 28.33 | 530000 | 1.8072 | 0.8268 | 0.8014 |
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- | 0.3423 | 28.59 | 535000 | 1.8119 | 0.8263 | 0.8033 |
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- | 0.3471 | 28.86 | 540000 | 1.8136 | 0.8269 | 0.8024 |
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- | 0.339 | 29.13 | 545000 | 1.8095 | 0.8270 | 0.8023 |
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- | 0.3366 | 29.39 | 550000 | 1.8091 | 0.8270 | 0.8029 |
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- | 0.3377 | 29.66 | 555000 | 1.8085 | 0.8271 | 0.8017 |
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- | 0.3403 | 29.93 | 560000 | 1.8052 | 0.8270 | 0.8027 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the massive dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.8097
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+ - Accuracy: 0.8259
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+ - F1: 0.8016
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
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+ | 5.8591 | 0.27 | 5000 | 5.6952 | 0.3264 | 0.1501 |
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+ | 4.1699 | 0.53 | 10000 | 4.1191 | 0.5584 | 0.4058 |
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+ | 3.363 | 0.8 | 15000 | 3.3295 | 0.6517 | 0.5362 |
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+ | 2.5996 | 1.07 | 20000 | 2.8831 | 0.7073 | 0.6101 |
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+ | 2.4092 | 1.34 | 25000 | 2.6156 | 0.7336 | 0.6540 |
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+ | 2.2034 | 1.6 | 30000 | 2.4512 | 0.7535 | 0.6792 |
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+ | 2.0892 | 1.87 | 35000 | 2.3290 | 0.7658 | 0.6941 |
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+ | 1.6869 | 2.14 | 40000 | 2.2489 | 0.7767 | 0.7205 |
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+ | 1.6442 | 2.41 | 45000 | 2.2293 | 0.7780 | 0.7261 |
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+ | 1.633 | 2.67 | 50000 | 2.1483 | 0.7854 | 0.7392 |
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+ | 1.6348 | 2.94 | 55000 | 2.0885 | 0.7906 | 0.7468 |
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+ | 1.2977 | 3.21 | 60000 | 2.1314 | 0.7903 | 0.7490 |
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+ | 1.3298 | 3.47 | 65000 | 2.0696 | 0.7975 | 0.7590 |
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+ | 1.3 | 3.74 | 70000 | 2.0638 | 0.7961 | 0.7611 |
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+ | 1.2101 | 4.01 | 75000 | 2.0296 | 0.8022 | 0.7627 |
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+ | 1.0841 | 4.28 | 80000 | 2.0720 | 0.8008 | 0.7656 |
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+ | 1.0928 | 4.54 | 85000 | 2.0490 | 0.8031 | 0.7684 |
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+ | 1.0846 | 4.81 | 90000 | 1.9852 | 0.8068 | 0.7751 |
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+ | 0.9008 | 5.08 | 95000 | 2.0298 | 0.8076 | 0.7749 |
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+ | 0.9178 | 5.34 | 100000 | 2.0931 | 0.8025 | 0.7735 |
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+ | 0.9507 | 5.61 | 105000 | 2.0079 | 0.8066 | 0.7790 |
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+ | 0.9577 | 5.88 | 110000 | 1.9660 | 0.8103 | 0.7780 |
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+ | 0.7877 | 6.15 | 115000 | 2.0676 | 0.8072 | 0.7772 |
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+ | 0.7916 | 6.41 | 120000 | 2.0080 | 0.8089 | 0.7832 |
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+ | 0.8493 | 6.68 | 125000 | 2.0347 | 0.8078 | 0.7780 |
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+ | 0.8544 | 6.95 | 130000 | 2.0131 | 0.8093 | 0.7806 |
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+ | 0.7207 | 7.22 | 135000 | 2.0612 | 0.8089 | 0.7827 |
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+ | 0.7387 | 7.48 | 140000 | 2.0334 | 0.8100 | 0.7829 |
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+ | 0.7341 | 7.75 | 145000 | 2.0446 | 0.8096 | 0.7826 |
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+ | 0.6886 | 8.02 | 150000 | 2.0384 | 0.8114 | 0.7853 |
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+ | 0.6826 | 8.28 | 155000 | 2.0159 | 0.8103 | 0.7850 |
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+ | 0.6944 | 8.55 | 160000 | 1.9987 | 0.8136 | 0.7879 |
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+ | 0.6858 | 8.82 | 165000 | 2.0162 | 0.8124 | 0.7905 |
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+ | 0.6204 | 9.09 | 170000 | 2.0336 | 0.8128 | 0.7875 |
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+ | 0.6063 | 9.35 | 175000 | 2.0218 | 0.8125 | 0.7879 |
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+ | 0.6253 | 9.62 | 180000 | 2.0256 | 0.8130 | 0.7874 |
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+ | 0.6354 | 9.89 | 185000 | 1.9910 | 0.8149 | 0.7889 |
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+ | 0.5804 | 10.15 | 190000 | 2.0027 | 0.8139 | 0.7898 |
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+ | 0.5932 | 10.42 | 195000 | 1.9711 | 0.8157 | 0.7919 |
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+ | 0.5965 | 10.69 | 200000 | 1.9713 | 0.8158 | 0.7930 |
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+ | 0.6028 | 10.96 | 205000 | 2.0039 | 0.8135 | 0.7884 |
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+ | 0.5417 | 11.22 | 210000 | 1.9622 | 0.8164 | 0.7926 |
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+ | 0.5556 | 11.49 | 215000 | 1.9953 | 0.8157 | 0.7937 |
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+ | 0.5552 | 11.76 | 220000 | 1.9741 | 0.8166 | 0.7928 |
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+ | 0.5146 | 12.03 | 225000 | 1.9948 | 0.8146 | 0.7892 |
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+ | 0.5328 | 12.29 | 230000 | 1.9546 | 0.8175 | 0.7969 |
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+ | 0.5224 | 12.56 | 235000 | 1.9565 | 0.8171 | 0.7927 |
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+ | 0.5491 | 12.83 | 240000 | 1.9538 | 0.8178 | 0.7932 |
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+ | 0.5001 | 13.09 | 245000 | 1.9559 | 0.8184 | 0.7944 |
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+ | 0.4904 | 13.36 | 250000 | 1.9734 | 0.8165 | 0.7947 |
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+ | 0.5091 | 13.63 | 255000 | 1.9647 | 0.8177 | 0.7936 |
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+ | 0.5157 | 13.9 | 260000 | 1.9391 | 0.8194 | 0.7953 |
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+ | 0.4824 | 14.16 | 265000 | 1.9494 | 0.8189 | 0.7967 |
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+ | 0.4757 | 14.43 | 270000 | 1.9423 | 0.8174 | 0.7920 |
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+ | 0.4859 | 14.7 | 275000 | 1.9255 | 0.8193 | 0.7949 |
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+ | 0.4878 | 14.96 | 280000 | 1.9229 | 0.8197 | 0.7957 |
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+ | 0.4629 | 15.23 | 285000 | 1.9201 | 0.8191 | 0.7950 |
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+ | 0.4634 | 15.5 | 290000 | 1.9189 | 0.8209 | 0.7990 |
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+ | 0.4593 | 15.77 | 295000 | 1.9161 | 0.8200 | 0.7991 |
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+ | 0.4484 | 16.03 | 300000 | 1.8980 | 0.8210 | 0.7952 |
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+ | 0.4473 | 16.3 | 305000 | 1.9098 | 0.8204 | 0.7983 |
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+ | 0.4531 | 16.57 | 310000 | 1.8917 | 0.8210 | 0.7964 |
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+ | 0.4493 | 16.84 | 315000 | 1.8937 | 0.8205 | 0.7979 |
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+ | 0.4288 | 17.1 | 320000 | 1.8914 | 0.8200 | 0.7989 |
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+ | 0.4291 | 17.37 | 325000 | 1.8920 | 0.8216 | 0.7988 |
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+ | 0.4215 | 17.64 | 330000 | 1.8951 | 0.8224 | 0.7987 |
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+ | 0.4351 | 17.9 | 335000 | 1.8831 | 0.8220 | 0.7964 |
123
+ | 0.4164 | 18.17 | 340000 | 1.8704 | 0.8223 | 0.7971 |
124
+ | 0.4205 | 18.44 | 345000 | 1.8835 | 0.8227 | 0.7985 |
125
+ | 0.4239 | 18.71 | 350000 | 1.8768 | 0.8227 | 0.7985 |
126
+ | 0.4269 | 18.97 | 355000 | 1.8723 | 0.8226 | 0.7988 |
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+ | 0.4051 | 19.24 | 360000 | 1.8555 | 0.8235 | 0.8016 |
128
+ | 0.4122 | 19.51 | 365000 | 1.8716 | 0.8234 | 0.7997 |
129
+ | 0.3921 | 19.77 | 370000 | 1.8650 | 0.8231 | 0.7978 |
130
+ | 0.3973 | 20.04 | 375000 | 1.8550 | 0.8236 | 0.7983 |
131
+ | 0.4 | 20.31 | 380000 | 1.8512 | 0.8225 | 0.7966 |
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+ | 0.4027 | 20.58 | 385000 | 1.8653 | 0.8236 | 0.7982 |
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+ | 0.3932 | 20.84 | 390000 | 1.8594 | 0.8243 | 0.7974 |
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+ | 0.392 | 21.11 | 395000 | 1.8373 | 0.8247 | 0.8006 |
135
+ | 0.3887 | 21.38 | 400000 | 1.8420 | 0.8252 | 0.8012 |
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+ | 0.3887 | 21.65 | 405000 | 1.8425 | 0.8241 | 0.7984 |
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+ | 0.38 | 21.91 | 410000 | 1.8413 | 0.8244 | 0.8017 |
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+ | 0.3793 | 22.18 | 415000 | 1.8325 | 0.8240 | 0.7978 |
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+ | 0.3806 | 22.45 | 420000 | 1.8338 | 0.8249 | 0.7990 |
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+ | 0.3726 | 22.71 | 425000 | 1.8488 | 0.8231 | 0.7990 |
141
+ | 0.3771 | 22.98 | 430000 | 1.8441 | 0.8243 | 0.7998 |
142
+ | 0.3728 | 23.25 | 435000 | 1.8380 | 0.8238 | 0.8005 |
143
+ | 0.3677 | 23.52 | 440000 | 1.8289 | 0.8246 | 0.7999 |
144
+ | 0.368 | 23.78 | 445000 | 1.8334 | 0.8256 | 0.8012 |
145
+ | 0.3659 | 24.05 | 450000 | 1.8188 | 0.8261 | 0.8010 |
146
+ | 0.3706 | 24.32 | 455000 | 1.8239 | 0.8250 | 0.7992 |
147
+ | 0.3649 | 24.58 | 460000 | 1.8236 | 0.8258 | 0.8013 |
148
+ | 0.3537 | 24.85 | 465000 | 1.8327 | 0.8254 | 0.7991 |
149
+ | 0.3548 | 25.12 | 470000 | 1.8175 | 0.8258 | 0.8020 |
150
+ | 0.3483 | 25.39 | 475000 | 1.8225 | 0.8255 | 0.8008 |
151
+ | 0.3516 | 25.65 | 480000 | 1.8200 | 0.8254 | 0.8004 |
152
+ | 0.3588 | 25.92 | 485000 | 1.8265 | 0.8256 | 0.8001 |
153
+ | 0.3492 | 26.19 | 490000 | 1.8052 | 0.8270 | 0.8015 |
154
+ | 0.3497 | 26.46 | 495000 | 1.8165 | 0.8268 | 0.8022 |
155
+ | 0.3467 | 26.72 | 500000 | 1.8172 | 0.8265 | 0.8026 |
156
+ | 0.3463 | 26.99 | 505000 | 1.8084 | 0.8266 | 0.8023 |
157
+ | 0.3448 | 27.26 | 510000 | 1.8105 | 0.8267 | 0.8021 |
158
+ | 0.3414 | 27.52 | 515000 | 1.8109 | 0.8267 | 0.8014 |
159
+ | 0.3439 | 27.79 | 520000 | 1.8146 | 0.8254 | 0.8005 |
160
+ | 0.3374 | 28.06 | 525000 | 1.8081 | 0.8264 | 0.8033 |
161
+ | 0.3412 | 28.33 | 530000 | 1.8125 | 0.8264 | 0.8022 |
162
+ | 0.3396 | 28.59 | 535000 | 1.8141 | 0.8264 | 0.8022 |
163
+ | 0.3451 | 28.86 | 540000 | 1.8072 | 0.8258 | 0.8005 |
164
+ | 0.337 | 29.13 | 545000 | 1.8056 | 0.8265 | 0.8028 |
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+ | 0.3335 | 29.39 | 550000 | 1.8083 | 0.8263 | 0.8010 |
166
+ | 0.3402 | 29.66 | 555000 | 1.8107 | 0.8260 | 0.8013 |
167
+ | 0.3409 | 29.93 | 560000 | 1.8097 | 0.8259 | 0.8016 |
168
 
169
 
170
  ### Framework versions
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