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scenario-NON-KD-PO-COPY-D2_data-AmazonScience_massive_all_1_155

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metadata
library_name: transformers
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-NON-KD-PO-COPY-D2_data-AmazonScience_massive_all_1_155
    results: []

scenario-NON-KD-PO-COPY-D2_data-AmazonScience_massive_all_1_155

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: 2.1172
  • Accuracy: 0.8547
  • F1: 0.8307

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: 55
  • 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
0.5096 0.2672 5000 0.6672 0.8301 0.7901
0.4124 0.5344 10000 0.6564 0.8398 0.8058
0.3501 0.8017 15000 0.6697 0.8466 0.8172
0.232 1.0689 20000 0.7193 0.8480 0.8171
0.2262 1.3361 25000 0.7387 0.8489 0.8243
0.2146 1.6033 30000 0.7377 0.8480 0.8243
0.2007 1.8706 35000 0.7711 0.8463 0.8186
0.1472 2.1378 40000 0.8809 0.8492 0.8218
0.1509 2.4050 45000 0.8559 0.8495 0.8242
0.1513 2.6722 50000 0.8919 0.8481 0.8226
0.1428 2.9394 55000 0.8636 0.8503 0.8272
0.1154 3.2067 60000 0.9868 0.8512 0.8312
0.1148 3.4739 65000 0.9541 0.8502 0.8253
0.1152 3.7411 70000 0.9729 0.8503 0.8305
0.1031 4.0083 75000 1.0581 0.8459 0.8260
0.0942 4.2756 80000 1.0732 0.8487 0.8248
0.0961 4.5428 85000 1.0528 0.8509 0.8284
0.099 4.8100 90000 1.0421 0.8486 0.8250
0.0761 5.0772 95000 1.1302 0.8480 0.8221
0.0822 5.3444 100000 1.1138 0.8492 0.8261
0.0833 5.6117 105000 1.1368 0.8478 0.8243
0.0768 5.8789 110000 1.1240 0.8503 0.8256
0.0593 6.1461 115000 1.2497 0.8491 0.8277
0.0687 6.4133 120000 1.2261 0.8484 0.8252
0.0738 6.6806 125000 1.1406 0.8482 0.8257
0.0651 6.9478 130000 1.2154 0.8488 0.8232
0.0604 7.2150 135000 1.3001 0.8459 0.8245
0.0646 7.4822 140000 1.2426 0.8488 0.8223
0.0653 7.7495 145000 1.2843 0.8499 0.8283
0.0433 8.0167 150000 1.3361 0.8500 0.8261
0.0497 8.2839 155000 1.2612 0.8492 0.8241
0.0517 8.5511 160000 1.3544 0.8474 0.8270
0.0624 8.8183 165000 1.2964 0.8489 0.8275
0.0381 9.0856 170000 1.3753 0.8486 0.8242
0.0405 9.3528 175000 1.4366 0.8484 0.8219
0.0488 9.6200 180000 1.4429 0.8458 0.8235
0.0495 9.8872 185000 1.3962 0.8496 0.8273
0.0363 10.1545 190000 1.5269 0.8483 0.8247
0.0423 10.4217 195000 1.4415 0.8489 0.8236
0.0448 10.6889 200000 1.3974 0.8482 0.8218
0.0373 10.9561 205000 1.4432 0.8465 0.8245
0.0294 11.2233 210000 1.5381 0.8493 0.8234
0.0336 11.4906 215000 1.5496 0.8490 0.8218
0.041 11.7578 220000 1.4867 0.8468 0.8240
0.028 12.0250 225000 1.5215 0.8498 0.8270
0.0287 12.2922 230000 1.5916 0.8495 0.8256
0.0314 12.5595 235000 1.6239 0.8500 0.8291
0.0347 12.8267 240000 1.5390 0.8464 0.8243
0.0247 13.0939 245000 1.5492 0.8502 0.8271
0.0281 13.3611 250000 1.5729 0.8465 0.8262
0.025 13.6283 255000 1.6933 0.8470 0.8249
0.0325 13.8956 260000 1.5823 0.8511 0.8289
0.0268 14.1628 265000 1.6099 0.8476 0.8213
0.0339 14.4300 270000 1.6234 0.8514 0.8306
0.0285 14.6972 275000 1.5725 0.8524 0.8288
0.0309 14.9645 280000 1.6213 0.8498 0.8251
0.0274 15.2317 285000 1.6650 0.8481 0.8235
0.02 15.4989 290000 1.7295 0.8488 0.8261
0.0299 15.7661 295000 1.6868 0.8513 0.8277
0.0196 16.0333 300000 1.7188 0.8492 0.8234
0.0221 16.3006 305000 1.7492 0.8511 0.8265
0.0273 16.5678 310000 1.6775 0.8505 0.8267
0.0212 16.8350 315000 1.7104 0.8492 0.8253
0.0188 17.1022 320000 1.6627 0.8502 0.8271
0.0225 17.3695 325000 1.7774 0.8504 0.8256
0.0204 17.6367 330000 1.7537 0.8489 0.8240
0.0261 17.9039 335000 1.6829 0.8516 0.8273
0.0153 18.1711 340000 1.8292 0.8532 0.8295
0.0226 18.4384 345000 1.7632 0.8511 0.8273
0.0213 18.7056 350000 1.7662 0.8515 0.8252
0.0162 18.9728 355000 1.7639 0.8519 0.8280
0.0155 19.2400 360000 1.8033 0.8521 0.8262
0.0151 19.5072 365000 1.8838 0.8505 0.8241
0.0151 19.7745 370000 1.8777 0.8532 0.8268
0.0138 20.0417 375000 1.8770 0.8522 0.8284
0.0152 20.3089 380000 1.8785 0.8525 0.8275
0.0136 20.5761 385000 1.8761 0.8530 0.8310
0.0181 20.8434 390000 1.8843 0.8526 0.8288
0.0113 21.1106 395000 1.8946 0.8540 0.8306
0.0144 21.3778 400000 1.8532 0.8530 0.8318
0.0149 21.6450 405000 1.9300 0.8519 0.8296
0.0165 21.9122 410000 1.9072 0.8532 0.8316
0.0128 22.1795 415000 1.9919 0.8516 0.8270
0.0071 22.4467 420000 1.9807 0.8543 0.8313
0.0099 22.7139 425000 1.9709 0.8527 0.8277
0.011 22.9811 430000 1.9782 0.8521 0.8279
0.0102 23.2484 435000 2.0483 0.8515 0.8268
0.013 23.5156 440000 2.0266 0.8527 0.8290
0.0145 23.7828 445000 1.9633 0.8533 0.8303
0.0091 24.0500 450000 2.0645 0.8514 0.8274
0.0128 24.3172 455000 2.0243 0.8541 0.8314
0.0101 24.5845 460000 2.0680 0.8518 0.8281
0.0087 24.8517 465000 2.0453 0.8526 0.8290
0.0087 25.1189 470000 2.0808 0.8529 0.8294
0.0079 25.3861 475000 2.1050 0.8522 0.8285
0.0101 25.6534 480000 2.0773 0.8518 0.8275
0.0094 25.9206 485000 2.0602 0.8533 0.8305
0.0097 26.1878 490000 2.0994 0.8519 0.8267
0.01 26.4550 495000 2.0608 0.8549 0.8309
0.0107 26.7222 500000 2.0748 0.8539 0.8300
0.0088 26.9895 505000 2.1072 0.8539 0.8305
0.0057 27.2567 510000 2.1086 0.8548 0.8318
0.0096 27.5239 515000 2.1032 0.8539 0.8297
0.0082 27.7911 520000 2.1119 0.8538 0.8294
0.0081 28.0584 525000 2.1045 0.8543 0.8296
0.0079 28.3256 530000 2.1268 0.8539 0.8296
0.0089 28.5928 535000 2.1128 0.8547 0.8303
0.0079 28.8600 540000 2.1003 0.8540 0.8294
0.0056 29.1273 545000 2.1130 0.8541 0.8294
0.0082 29.3945 550000 2.1167 0.8544 0.8302
0.0056 29.6617 555000 2.1182 0.8546 0.8306
0.0076 29.9289 560000 2.1172 0.8547 0.8307

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.19.1