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scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_144

PyTorch
deberta-v2
Generated from Trainer
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metadata
license: mit
base_model: haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1
tags:
  - generated_from_trainer
datasets:
  - massive
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_144
    results: []

scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_144

This model is a fine-tuned version of haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1 on the massive dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1139
  • Accuracy: 0.8659
  • F1: 0.8466

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 44
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
1.7627 0.27 5000 1.9858 0.8200 0.7785
1.3268 0.53 10000 1.7497 0.8353 0.8042
1.1431 0.8 15000 1.6427 0.8403 0.8129
0.846 1.07 20000 1.5717 0.8445 0.8134
0.7952 1.34 25000 1.5650 0.8457 0.8207
0.7416 1.6 30000 1.5495 0.8493 0.8246
0.713 1.87 35000 1.4702 0.8518 0.8325
0.6101 2.14 40000 1.4505 0.8522 0.8287
0.591 2.41 45000 1.4639 0.8522 0.8257
0.5774 2.67 50000 1.4352 0.8498 0.8292
0.5564 2.94 55000 1.4054 0.8556 0.8357
0.48 3.21 60000 1.4085 0.8539 0.8330
0.4767 3.47 65000 1.3640 0.8578 0.8352
0.4725 3.74 70000 1.3570 0.8553 0.8326
0.4599 4.01 75000 1.3478 0.8568 0.8356
0.4328 4.28 80000 1.3349 0.8561 0.8329
0.4237 4.54 85000 1.3403 0.8585 0.8375
0.4186 4.81 90000 1.3329 0.8591 0.8402
0.3964 5.08 95000 1.3430 0.8552 0.8350
0.3896 5.34 100000 1.3224 0.8572 0.8369
0.4012 5.61 105000 1.3126 0.8597 0.8389
0.3845 5.88 110000 1.3096 0.8574 0.8368
0.3601 6.15 115000 1.2671 0.8597 0.8378
0.3608 6.41 120000 1.2839 0.8596 0.8374
0.3612 6.68 125000 1.2874 0.8590 0.8393
0.3617 6.95 130000 1.3028 0.8572 0.8382
0.3357 7.22 135000 1.2707 0.8597 0.8397
0.3467 7.48 140000 1.2735 0.8597 0.8407
0.3516 7.75 145000 1.2572 0.8606 0.8397
0.3298 8.02 150000 1.2609 0.8601 0.8396
0.3258 8.28 155000 1.2527 0.8585 0.8381
0.3301 8.55 160000 1.2414 0.8613 0.8445
0.3193 8.82 165000 1.2525 0.8602 0.8407
0.307 9.09 170000 1.2398 0.8613 0.8428
0.3155 9.35 175000 1.2209 0.8621 0.8416
0.3138 9.62 180000 1.2155 0.8617 0.8428
0.3108 9.89 185000 1.2363 0.8618 0.8420
0.2954 10.15 190000 1.2141 0.8609 0.8414
0.2968 10.42 195000 1.2331 0.8611 0.8430
0.3017 10.69 200000 1.2081 0.8618 0.8433
0.2989 10.96 205000 1.2025 0.8630 0.8438
0.2792 11.22 210000 1.2063 0.8635 0.8433
0.2946 11.49 215000 1.1898 0.8639 0.8435
0.2778 11.76 220000 1.2013 0.8625 0.8428
0.2757 12.03 225000 1.1908 0.8627 0.8439
0.2763 12.29 230000 1.1906 0.8631 0.8424
0.2699 12.56 235000 1.1894 0.8629 0.8422
0.2716 12.83 240000 1.1887 0.8643 0.8460
0.2715 13.09 245000 1.1940 0.8634 0.8463
0.2659 13.36 250000 1.1844 0.8642 0.8443
0.2693 13.63 255000 1.1844 0.8642 0.8446
0.2654 13.9 260000 1.1784 0.8637 0.8454
0.2522 14.16 265000 1.1728 0.8641 0.8433
0.2604 14.43 270000 1.1878 0.8627 0.8415
0.2489 14.7 275000 1.1795 0.8640 0.8437
0.2611 14.96 280000 1.1585 0.8648 0.8450
0.246 15.23 285000 1.1574 0.8647 0.8452
0.2482 15.5 290000 1.1654 0.8633 0.8427
0.2423 15.77 295000 1.1683 0.8632 0.8416
0.2387 16.03 300000 1.1736 0.8625 0.8415
0.2417 16.3 305000 1.1686 0.8635 0.8437
0.2433 16.57 310000 1.1639 0.8626 0.8410
0.2405 16.84 315000 1.1647 0.8632 0.8421
0.2327 17.1 320000 1.1459 0.8642 0.8446
0.2374 17.37 325000 1.1513 0.8643 0.8454
0.233 17.64 330000 1.1479 0.8649 0.8438
0.2381 17.9 335000 1.1556 0.8643 0.8450
0.2228 18.17 340000 1.1523 0.8648 0.8461
0.2305 18.44 345000 1.1523 0.8635 0.8440
0.2244 18.71 350000 1.1507 0.8648 0.8450
0.2212 18.97 355000 1.1413 0.8650 0.8451
0.2207 19.24 360000 1.1401 0.8644 0.8466
0.2164 19.51 365000 1.1518 0.8630 0.8426
0.2232 19.77 370000 1.1469 0.8640 0.8452
0.2147 20.04 375000 1.1495 0.8629 0.8433
0.2089 20.31 380000 1.1392 0.8657 0.8457
0.2074 20.58 385000 1.1381 0.8643 0.8441
0.2149 20.84 390000 1.1415 0.8639 0.8447
0.2074 21.11 395000 1.1307 0.8647 0.8441
0.2087 21.38 400000 1.1351 0.8641 0.8432
0.2104 21.65 405000 1.1312 0.8644 0.8448
0.2078 21.91 410000 1.1296 0.8650 0.8457
0.2038 22.18 415000 1.1249 0.8657 0.8452
0.2037 22.45 420000 1.1334 0.8643 0.8444
0.2027 22.71 425000 1.1280 0.8644 0.8446
0.2041 22.98 430000 1.1321 0.8640 0.8467
0.1921 23.25 435000 1.1324 0.8635 0.8445
0.2007 23.52 440000 1.1248 0.8649 0.8458
0.2018 23.78 445000 1.1274 0.8649 0.8451
0.2009 24.05 450000 1.1204 0.8654 0.8473
0.2017 24.32 455000 1.1245 0.8651 0.8450
0.1977 24.58 460000 1.1234 0.8649 0.8461
0.1974 24.85 465000 1.1234 0.8646 0.8445
0.1934 25.12 470000 1.1220 0.8655 0.8457
0.1937 25.39 475000 1.1183 0.8659 0.8470
0.1947 25.65 480000 1.1207 0.8653 0.8449
0.1932 25.92 485000 1.1172 0.8660 0.8465
0.1904 26.19 490000 1.1250 0.8651 0.8462
0.1892 26.46 495000 1.1171 0.8661 0.8470
0.196 26.72 500000 1.1207 0.8652 0.8456
0.1927 26.99 505000 1.1174 0.8651 0.8454
0.1888 27.26 510000 1.1183 0.8653 0.8457
0.1906 27.52 515000 1.1190 0.8646 0.8455
0.1854 27.79 520000 1.1098 0.8655 0.8450
0.1869 28.06 525000 1.1173 0.8658 0.8463
0.1829 28.33 530000 1.1157 0.8654 0.8453
0.1909 28.59 535000 1.1135 0.8655 0.8459
0.1853 28.86 540000 1.1103 0.8663 0.8475
0.1847 29.13 545000 1.1141 0.8651 0.8455
0.1814 29.39 550000 1.1101 0.8655 0.8456
0.1794 29.66 555000 1.1113 0.8657 0.8460
0.1842 29.93 560000 1.1139 0.8659 0.8466

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.13.3