Hugging Face's logo

haryoaw
/
scenario-KD-PR-MSV-D2_data-AmazonScience_massive_all_1_144

Transformers
PyTorch
deberta-v2
Generated from Trainer
Inference Endpoints
Model card Files Community
scenario-KD-PR-MSV-D2_data-AmazonScience_massive_all_1_144 / README.md
haryoaw's picture
haryoaw
Initial Commit
a553e83 verified
preview code
|
raw
history blame contribute delete
9.74 kB
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-PR-MSV-D2_data-AmazonScience_massive_all_1_144
    results: []

scenario-KD-PR-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: 1.4509
  • Accuracy: 0.8576
  • F1: 0.8345

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
1.4358 0.27 5000 1.5925 0.8182 0.7743
1.291 0.53 10000 1.5425 0.8257 0.7947
1.207 0.8 15000 1.4865 0.8367 0.8041
1.0927 1.07 20000 1.4621 0.8439 0.8136
1.0824 1.34 25000 1.4744 0.8388 0.8066
1.0564 1.6 30000 1.4548 0.8454 0.8143
1.0461 1.87 35000 1.4542 0.8452 0.8125
0.9801 2.14 40000 1.4552 0.8461 0.8179
0.9812 2.41 45000 1.4601 0.8467 0.8224
0.9878 2.67 50000 1.4641 0.8454 0.8238
0.9767 2.94 55000 1.4509 0.8480 0.8259
0.9201 3.21 60000 1.4822 0.8429 0.8192
0.9235 3.47 65000 1.4676 0.8451 0.8237
0.9299 3.74 70000 1.4711 0.8456 0.8216
0.9204 4.01 75000 1.4883 0.8425 0.8188
0.8934 4.28 80000 1.4950 0.8425 0.8188
0.8967 4.54 85000 1.4874 0.8444 0.8193
0.9039 4.81 90000 1.4726 0.8480 0.8238
0.8764 5.08 95000 1.4938 0.8446 0.8197
0.8737 5.34 100000 1.4776 0.8484 0.8282
0.8766 5.61 105000 1.4764 0.8488 0.8257
0.8836 5.88 110000 1.4775 0.8464 0.8221
0.861 6.15 115000 1.4765 0.8486 0.8244
0.8595 6.41 120000 1.4726 0.8502 0.8276
0.8687 6.68 125000 1.4702 0.8502 0.8230
0.8641 6.95 130000 1.4841 0.8480 0.8272
0.8474 7.22 135000 1.4871 0.8485 0.8229
0.8503 7.48 140000 1.4910 0.8456 0.8181
0.8548 7.75 145000 1.4877 0.8484 0.8223
0.8404 8.02 150000 1.4682 0.8509 0.8275
0.8481 8.28 155000 1.4994 0.8471 0.8226
0.8427 8.55 160000 1.4933 0.8476 0.8240
0.8418 8.82 165000 1.4812 0.8484 0.8201
0.8323 9.09 170000 1.4808 0.8500 0.8254
0.8353 9.35 175000 1.4962 0.8462 0.8196
0.8331 9.62 180000 1.4947 0.8459 0.8219
0.8384 9.89 185000 1.4916 0.8476 0.8228
0.8202 10.15 190000 1.4909 0.8476 0.8232
0.8308 10.42 195000 1.4897 0.8498 0.8233
0.8283 10.69 200000 1.4882 0.8476 0.8246
0.8303 10.96 205000 1.4837 0.8477 0.8228
0.8154 11.22 210000 1.4930 0.8489 0.8233
0.8269 11.49 215000 1.5044 0.8454 0.8191
0.8172 11.76 220000 1.4946 0.8484 0.8237
0.8071 12.03 225000 1.4824 0.8513 0.8271
0.8124 12.29 230000 1.4778 0.8514 0.8262
0.8187 12.56 235000 1.4866 0.8478 0.8236
0.8153 12.83 240000 1.5002 0.8469 0.8257
0.8117 13.09 245000 1.4883 0.8492 0.8235
0.807 13.36 250000 1.4906 0.8511 0.8293
0.8151 13.63 255000 1.4702 0.8526 0.8295
0.8107 13.9 260000 1.4772 0.8513 0.8237
0.8012 14.16 265000 1.4784 0.8518 0.8245
0.8039 14.43 270000 1.4933 0.8485 0.8258
0.8031 14.7 275000 1.4811 0.8519 0.8252
0.8058 14.96 280000 1.4802 0.8508 0.8263
0.8014 15.23 285000 1.4919 0.8498 0.8240
0.8002 15.5 290000 1.4780 0.8515 0.8247
0.8043 15.77 295000 1.4755 0.8519 0.8237
0.7967 16.03 300000 1.4765 0.8516 0.8296
0.7958 16.3 305000 1.4910 0.8509 0.8260
0.8032 16.57 310000 1.4795 0.8499 0.8221
0.8002 16.84 315000 1.4864 0.8497 0.8235
0.7938 17.1 320000 1.4832 0.8509 0.8280
0.7981 17.37 325000 1.4866 0.8508 0.8285
0.798 17.64 330000 1.4922 0.8496 0.8274
0.8004 17.9 335000 1.4848 0.8505 0.8278
0.7899 18.17 340000 1.4919 0.8490 0.8244
0.7961 18.44 345000 1.4764 0.8522 0.8300
0.793 18.71 350000 1.4795 0.8522 0.8279
0.7933 18.97 355000 1.4845 0.8511 0.8257
0.788 19.24 360000 1.4814 0.8508 0.8272
0.787 19.51 365000 1.4739 0.8524 0.8282
0.7911 19.77 370000 1.4788 0.8514 0.8305
0.7843 20.04 375000 1.4762 0.8533 0.8285
0.7859 20.31 380000 1.4777 0.8525 0.8299
0.7846 20.58 385000 1.4696 0.8524 0.8309
0.7857 20.84 390000 1.4812 0.8519 0.8267
0.7861 21.11 395000 1.4924 0.8491 0.8241
0.7845 21.38 400000 1.4847 0.8511 0.8253
0.7847 21.65 405000 1.4748 0.8527 0.8277
0.785 21.91 410000 1.4794 0.8530 0.8281
0.7816 22.18 415000 1.4764 0.8529 0.8268
0.7845 22.45 420000 1.4738 0.8529 0.8288
0.7807 22.71 425000 1.4864 0.8511 0.8278
0.7809 22.98 430000 1.4782 0.8509 0.8259
0.7761 23.25 435000 1.4829 0.8527 0.8281
0.7815 23.52 440000 1.4627 0.8541 0.8298
0.7833 23.78 445000 1.4756 0.8532 0.8293
0.7786 24.05 450000 1.4612 0.8556 0.8318
0.7826 24.32 455000 1.4582 0.8553 0.8304
0.7821 24.58 460000 1.4614 0.8562 0.8323
0.7793 24.85 465000 1.4600 0.8564 0.8319
0.7763 25.12 470000 1.4650 0.8549 0.8311
0.776 25.39 475000 1.4658 0.8553 0.8285
0.7796 25.65 480000 1.4572 0.8560 0.8308
0.7794 25.92 485000 1.4638 0.8545 0.8302
0.7747 26.19 490000 1.4541 0.8565 0.8329
0.7761 26.46 495000 1.4596 0.8553 0.8283
0.7754 26.72 500000 1.4622 0.8566 0.8325
0.7769 26.99 505000 1.4665 0.8544 0.8303
0.777 27.26 510000 1.4622 0.8557 0.8300
0.7741 27.52 515000 1.4599 0.8562 0.8321
0.7747 27.79 520000 1.4575 0.8558 0.8301
0.7764 28.06 525000 1.4513 0.8565 0.8310
0.7731 28.33 530000 1.4551 0.8562 0.8322
0.7752 28.59 535000 1.4530 0.8568 0.8322
0.7729 28.86 540000 1.4563 0.8562 0.8323
0.7751 29.13 545000 1.4548 0.8567 0.8332
0.7742 29.39 550000 1.4494 0.8569 0.8333
0.7725 29.66 555000 1.4496 0.8576 0.8342
0.7756 29.93 560000 1.4509 0.8576 0.8345

Framework versions

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