--- 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_166 results: [] --- # scenario-KD-PR-MSV-D2_data-AmazonScience_massive_all_1_166 This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 1.4485 - Accuracy: 0.8574 - F1: 0.8341 ## 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: 66 - 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 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 1.4298 | 0.27 | 5000 | 1.5967 | 0.8151 | 0.7666 | | 1.2734 | 0.53 | 10000 | 1.5427 | 0.8274 | 0.7960 | | 1.1941 | 0.8 | 15000 | 1.4827 | 0.8390 | 0.8068 | | 1.0639 | 1.07 | 20000 | 1.4810 | 0.8417 | 0.8047 | | 1.0604 | 1.34 | 25000 | 1.4630 | 0.8423 | 0.8143 | | 1.0654 | 1.6 | 30000 | 1.4684 | 0.8419 | 0.8110 | | 1.0463 | 1.87 | 35000 | 1.4657 | 0.8411 | 0.8138 | | 0.968 | 2.14 | 40000 | 1.4824 | 0.8423 | 0.8168 | | 0.9725 | 2.41 | 45000 | 1.4736 | 0.8450 | 0.8233 | | 0.9661 | 2.67 | 50000 | 1.4821 | 0.8410 | 0.8187 | | 0.9729 | 2.94 | 55000 | 1.4726 | 0.8433 | 0.8198 | | 0.9376 | 3.21 | 60000 | 1.4574 | 0.8484 | 0.8260 | | 0.9369 | 3.47 | 65000 | 1.4585 | 0.8462 | 0.8227 | | 0.9335 | 3.74 | 70000 | 1.4680 | 0.8440 | 0.8141 | | 0.9214 | 4.01 | 75000 | 1.4614 | 0.8489 | 0.8250 | | 0.8969 | 4.28 | 80000 | 1.4837 | 0.8443 | 0.8192 | | 0.9043 | 4.54 | 85000 | 1.4667 | 0.8455 | 0.8190 | | 0.9048 | 4.81 | 90000 | 1.4842 | 0.8445 | 0.8158 | | 0.8714 | 5.08 | 95000 | 1.4747 | 0.8458 | 0.8190 | | 0.8811 | 5.34 | 100000 | 1.4862 | 0.8462 | 0.8213 | | 0.8825 | 5.61 | 105000 | 1.4670 | 0.8462 | 0.8244 | | 0.8876 | 5.88 | 110000 | 1.4813 | 0.8446 | 0.8228 | | 0.8584 | 6.15 | 115000 | 1.4841 | 0.8454 | 0.8241 | | 0.8612 | 6.41 | 120000 | 1.4797 | 0.8472 | 0.8238 | | 0.864 | 6.68 | 125000 | 1.4770 | 0.8485 | 0.8221 | | 0.8711 | 6.95 | 130000 | 1.4883 | 0.8459 | 0.8204 | | 0.8464 | 7.22 | 135000 | 1.4838 | 0.8478 | 0.8223 | | 0.8622 | 7.48 | 140000 | 1.4896 | 0.8453 | 0.8225 | | 0.8519 | 7.75 | 145000 | 1.4882 | 0.8464 | 0.8215 | | 0.8475 | 8.02 | 150000 | 1.4759 | 0.8501 | 0.8269 | | 0.8429 | 8.28 | 155000 | 1.4686 | 0.8501 | 0.8283 | | 0.8407 | 8.55 | 160000 | 1.4810 | 0.8488 | 0.8243 | | 0.8429 | 8.82 | 165000 | 1.4810 | 0.8484 | 0.8253 | | 0.8337 | 9.09 | 170000 | 1.4810 | 0.8484 | 0.8275 | | 0.8283 | 9.35 | 175000 | 1.4905 | 0.8471 | 0.8241 | | 0.8369 | 9.62 | 180000 | 1.4951 | 0.8458 | 0.8192 | | 0.837 | 9.89 | 185000 | 1.4808 | 0.8489 | 0.8251 | | 0.8277 | 10.15 | 190000 | 1.4792 | 0.8514 | 0.8269 | | 0.8264 | 10.42 | 195000 | 1.4921 | 0.8471 | 0.8203 | | 0.8326 | 10.69 | 200000 | 1.4932 | 0.8446 | 0.8187 | | 0.8226 | 10.96 | 205000 | 1.4738 | 0.8511 | 0.8297 | | 0.8214 | 11.22 | 210000 | 1.5004 | 0.8452 | 0.8218 | | 0.8184 | 11.49 | 215000 | 1.4897 | 0.8477 | 0.8254 | | 0.8191 | 11.76 | 220000 | 1.4932 | 0.8476 | 0.8225 | | 0.8067 | 12.03 | 225000 | 1.4959 | 0.8475 | 0.8248 | | 0.8098 | 12.29 | 230000 | 1.4868 | 0.8487 | 0.8243 | | 0.8194 | 12.56 | 235000 | 1.4966 | 0.8451 | 0.8213 | | 0.8128 | 12.83 | 240000 | 1.4956 | 0.8484 | 0.8244 | | 0.8045 | 13.09 | 245000 | 1.4935 | 0.8488 | 0.8214 | | 0.8093 | 13.36 | 250000 | 1.4740 | 0.8509 | 0.8262 | | 0.8107 | 13.63 | 255000 | 1.4858 | 0.8492 | 0.8226 | | 0.8129 | 13.9 | 260000 | 1.4863 | 0.8489 | 0.8252 | | 0.8038 | 14.16 | 265000 | 1.4865 | 0.8491 | 0.8261 | | 0.805 | 14.43 | 270000 | 1.4785 | 0.8523 | 0.8286 | | 0.8096 | 14.7 | 275000 | 1.4740 | 0.8505 | 0.8273 | | 0.8101 | 14.96 | 280000 | 1.4742 | 0.8512 | 0.8273 | | 0.8008 | 15.23 | 285000 | 1.4889 | 0.8495 | 0.8252 | | 0.7987 | 15.5 | 290000 | 1.4783 | 0.8509 | 0.8285 | | 0.7991 | 15.77 | 295000 | 1.4758 | 0.8523 | 0.8266 | | 0.7949 | 16.03 | 300000 | 1.4831 | 0.8511 | 0.8291 | | 0.794 | 16.3 | 305000 | 1.4831 | 0.8497 | 0.8239 | | 0.799 | 16.57 | 310000 | 1.4948 | 0.8480 | 0.8259 | | 0.7973 | 16.84 | 315000 | 1.4819 | 0.8501 | 0.8253 | | 0.7901 | 17.1 | 320000 | 1.4868 | 0.8498 | 0.8263 | | 0.7955 | 17.37 | 325000 | 1.5029 | 0.8469 | 0.8222 | | 0.7954 | 17.64 | 330000 | 1.4902 | 0.8492 | 0.8234 | | 0.7926 | 17.9 | 335000 | 1.4990 | 0.8467 | 0.8232 | | 0.7938 | 18.17 | 340000 | 1.4764 | 0.8511 | 0.8236 | | 0.7926 | 18.44 | 345000 | 1.4741 | 0.8523 | 0.8310 | | 0.7928 | 18.71 | 350000 | 1.4800 | 0.8516 | 0.8252 | | 0.793 | 18.97 | 355000 | 1.4695 | 0.8525 | 0.8260 | | 0.7902 | 19.24 | 360000 | 1.4677 | 0.8529 | 0.8260 | | 0.7887 | 19.51 | 365000 | 1.4774 | 0.8507 | 0.8259 | | 0.7935 | 19.77 | 370000 | 1.4762 | 0.8519 | 0.8272 | | 0.7828 | 20.04 | 375000 | 1.4700 | 0.8533 | 0.8313 | | 0.788 | 20.31 | 380000 | 1.4768 | 0.8501 | 0.8267 | | 0.7845 | 20.58 | 385000 | 1.4724 | 0.8532 | 0.8302 | | 0.7907 | 20.84 | 390000 | 1.4712 | 0.8504 | 0.8250 | | 0.7901 | 21.11 | 395000 | 1.4787 | 0.8520 | 0.8269 | | 0.7809 | 21.38 | 400000 | 1.4745 | 0.8532 | 0.8270 | | 0.7822 | 21.65 | 405000 | 1.4824 | 0.8499 | 0.8260 | | 0.7882 | 21.91 | 410000 | 1.4711 | 0.8534 | 0.8296 | | 0.7825 | 22.18 | 415000 | 1.4755 | 0.8516 | 0.8264 | | 0.7839 | 22.45 | 420000 | 1.4754 | 0.8519 | 0.8258 | | 0.7836 | 22.71 | 425000 | 1.4671 | 0.8546 | 0.8301 | | 0.7816 | 22.98 | 430000 | 1.4654 | 0.8532 | 0.8297 | | 0.7786 | 23.25 | 435000 | 1.4786 | 0.8527 | 0.8278 | | 0.7841 | 23.52 | 440000 | 1.4678 | 0.8544 | 0.8309 | | 0.7802 | 23.78 | 445000 | 1.4735 | 0.8526 | 0.8304 | | 0.7801 | 24.05 | 450000 | 1.4703 | 0.8521 | 0.8289 | | 0.7796 | 24.32 | 455000 | 1.4668 | 0.8543 | 0.8323 | | 0.78 | 24.58 | 460000 | 1.4628 | 0.8549 | 0.8332 | | 0.7796 | 24.85 | 465000 | 1.4616 | 0.8549 | 0.8312 | | 0.7797 | 25.12 | 470000 | 1.4615 | 0.8540 | 0.8295 | | 0.7791 | 25.39 | 475000 | 1.4615 | 0.8542 | 0.8293 | | 0.7797 | 25.65 | 480000 | 1.4636 | 0.8543 | 0.8305 | | 0.7779 | 25.92 | 485000 | 1.4599 | 0.8547 | 0.8301 | | 0.7764 | 26.19 | 490000 | 1.4543 | 0.8565 | 0.8322 | | 0.7785 | 26.46 | 495000 | 1.4557 | 0.8551 | 0.8309 | | 0.7738 | 26.72 | 500000 | 1.4580 | 0.8549 | 0.8327 | | 0.7776 | 26.99 | 505000 | 1.4516 | 0.8568 | 0.8317 | | 0.7756 | 27.26 | 510000 | 1.4575 | 0.8553 | 0.8312 | | 0.7759 | 27.52 | 515000 | 1.4503 | 0.8567 | 0.8343 | | 0.7748 | 27.79 | 520000 | 1.4507 | 0.8569 | 0.8327 | | 0.7757 | 28.06 | 525000 | 1.4528 | 0.8556 | 0.8309 | | 0.7703 | 28.33 | 530000 | 1.4584 | 0.8550 | 0.8305 | | 0.7745 | 28.59 | 535000 | 1.4507 | 0.8569 | 0.8333 | | 0.7754 | 28.86 | 540000 | 1.4547 | 0.8558 | 0.8324 | | 0.7743 | 29.13 | 545000 | 1.4499 | 0.8568 | 0.8329 | | 0.7759 | 29.39 | 550000 | 1.4498 | 0.8566 | 0.8319 | | 0.7729 | 29.66 | 555000 | 1.4474 | 0.8571 | 0.8328 | | 0.7734 | 29.93 | 560000 | 1.4485 | 0.8574 | 0.8341 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3