--- license: mit base_model: microsoft/mdeberta-v3-base tags: - generated_from_trainer datasets: - massive metrics: - accuracy - f1 model-index: - name: scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_155 results: [] --- # scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_155 This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.0315 - F1: 0.0010 ## 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 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 0.0 | 0.27 | 5000 | nan | 0.0315 | 0.0010 | | 0.0 | 0.53 | 10000 | nan | 0.0315 | 0.0010 | | 0.0 | 0.8 | 15000 | nan | 0.0315 | 0.0010 | | 0.0 | 1.07 | 20000 | nan | 0.0315 | 0.0010 | | 0.0 | 1.34 | 25000 | nan | 0.0315 | 0.0010 | | 0.0 | 1.6 | 30000 | nan | 0.0315 | 0.0010 | | 0.0 | 1.87 | 35000 | nan | 0.0315 | 0.0010 | | 0.0 | 2.14 | 40000 | nan | 0.0315 | 0.0010 | | 0.0 | 2.41 | 45000 | nan | 0.0315 | 0.0010 | | 0.0 | 2.67 | 50000 | nan | 0.0315 | 0.0010 | | 0.0 | 2.94 | 55000 | nan | 0.0315 | 0.0010 | | 0.0 | 3.21 | 60000 | nan | 0.0315 | 0.0010 | | 0.0 | 3.47 | 65000 | nan | 0.0315 | 0.0010 | | 0.0 | 3.74 | 70000 | nan | 0.0315 | 0.0010 | | 0.0 | 4.01 | 75000 | nan | 0.0315 | 0.0010 | | 0.0 | 4.28 | 80000 | nan | 0.0315 | 0.0010 | | 0.0 | 4.54 | 85000 | nan | 0.0315 | 0.0010 | | 0.0 | 4.81 | 90000 | nan | 0.0315 | 0.0010 | | 0.0 | 5.08 | 95000 | nan | 0.0315 | 0.0010 | | 0.0 | 5.34 | 100000 | nan | 0.0315 | 0.0010 | | 0.0 | 5.61 | 105000 | nan | 0.0315 | 0.0010 | | 0.0 | 5.88 | 110000 | nan | 0.0315 | 0.0010 | | 0.0 | 6.15 | 115000 | nan | 0.0315 | 0.0010 | | 0.0 | 6.41 | 120000 | nan | 0.0315 | 0.0010 | | 0.0 | 6.68 | 125000 | nan | 0.0315 | 0.0010 | | 0.0 | 6.95 | 130000 | nan | 0.0315 | 0.0010 | | 0.0 | 7.22 | 135000 | nan | 0.0315 | 0.0010 | | 0.0 | 7.48 | 140000 | nan | 0.0315 | 0.0010 | | 0.0 | 7.75 | 145000 | nan | 0.0315 | 0.0010 | | 0.0 | 8.02 | 150000 | nan | 0.0315 | 0.0010 | | 0.0 | 8.28 | 155000 | nan | 0.0315 | 0.0010 | | 0.0 | 8.55 | 160000 | nan | 0.0315 | 0.0010 | | 0.0 | 8.82 | 165000 | nan | 0.0315 | 0.0010 | | 0.0 | 9.09 | 170000 | nan | 0.0315 | 0.0010 | | 0.0 | 9.35 | 175000 | nan | 0.0315 | 0.0010 | | 0.0 | 9.62 | 180000 | nan | 0.0315 | 0.0010 | | 0.0 | 9.89 | 185000 | nan | 0.0315 | 0.0010 | | 0.0 | 10.15 | 190000 | nan | 0.0315 | 0.0010 | | 0.0 | 10.42 | 195000 | nan | 0.0315 | 0.0010 | | 0.0 | 10.69 | 200000 | nan | 0.0315 | 0.0010 | | 0.0 | 10.96 | 205000 | nan | 0.0315 | 0.0010 | | 0.0 | 11.22 | 210000 | nan | 0.0315 | 0.0010 | | 0.0 | 11.49 | 215000 | nan | 0.0315 | 0.0010 | | 0.0 | 11.76 | 220000 | nan | 0.0315 | 0.0010 | | 0.0 | 12.03 | 225000 | nan | 0.0315 | 0.0010 | | 0.0 | 12.29 | 230000 | nan | 0.0315 | 0.0010 | | 0.0 | 12.56 | 235000 | nan | 0.0315 | 0.0010 | | 0.0 | 12.83 | 240000 | nan | 0.0315 | 0.0010 | | 0.0 | 13.09 | 245000 | nan | 0.0315 | 0.0010 | | 0.0 | 13.36 | 250000 | nan | 0.0315 | 0.0010 | | 0.0 | 13.63 | 255000 | nan | 0.0315 | 0.0010 | | 0.0 | 13.9 | 260000 | nan | 0.0315 | 0.0010 | | 0.0 | 14.16 | 265000 | nan | 0.0315 | 0.0010 | | 0.0 | 14.43 | 270000 | nan | 0.0315 | 0.0010 | | 0.0 | 14.7 | 275000 | nan | 0.0315 | 0.0010 | | 0.0 | 14.96 | 280000 | nan | 0.0315 | 0.0010 | | 0.0 | 15.23 | 285000 | nan | 0.0315 | 0.0010 | | 0.0 | 15.5 | 290000 | nan | 0.0315 | 0.0010 | | 0.0 | 15.77 | 295000 | nan | 0.0315 | 0.0010 | | 0.0 | 16.03 | 300000 | nan | 0.0315 | 0.0010 | | 0.0 | 16.3 | 305000 | nan | 0.0315 | 0.0010 | | 0.0 | 16.57 | 310000 | nan | 0.0315 | 0.0010 | | 0.0 | 16.84 | 315000 | nan | 0.0315 | 0.0010 | | 0.0 | 17.1 | 320000 | nan | 0.0315 | 0.0010 | | 0.0 | 17.37 | 325000 | nan | 0.0315 | 0.0010 | | 0.0 | 17.64 | 330000 | nan | 0.0315 | 0.0010 | | 0.0 | 17.9 | 335000 | nan | 0.0315 | 0.0010 | | 0.0 | 18.17 | 340000 | nan | 0.0315 | 0.0010 | | 0.0 | 18.44 | 345000 | nan | 0.0315 | 0.0010 | | 0.0 | 18.71 | 350000 | nan | 0.0315 | 0.0010 | | 0.0 | 18.97 | 355000 | nan | 0.0315 | 0.0010 | | 0.0 | 19.24 | 360000 | nan | 0.0315 | 0.0010 | | 0.0 | 19.51 | 365000 | nan | 0.0315 | 0.0010 | | 0.0 | 19.77 | 370000 | nan | 0.0315 | 0.0010 | | 0.0 | 20.04 | 375000 | nan | 0.0315 | 0.0010 | | 0.0 | 20.31 | 380000 | nan | 0.0315 | 0.0010 | | 0.0 | 20.58 | 385000 | nan | 0.0315 | 0.0010 | | 0.0 | 20.84 | 390000 | nan | 0.0315 | 0.0010 | | 0.0 | 21.11 | 395000 | nan | 0.0315 | 0.0010 | | 0.0 | 21.38 | 400000 | nan | 0.0315 | 0.0010 | | 0.0 | 21.65 | 405000 | nan | 0.0315 | 0.0010 | | 0.0 | 21.91 | 410000 | nan | 0.0315 | 0.0010 | | 0.0 | 22.18 | 415000 | nan | 0.0315 | 0.0010 | | 0.0 | 22.45 | 420000 | nan | 0.0315 | 0.0010 | | 0.0 | 22.71 | 425000 | nan | 0.0315 | 0.0010 | | 0.0 | 22.98 | 430000 | nan | 0.0315 | 0.0010 | | 0.0 | 23.25 | 435000 | nan | 0.0315 | 0.0010 | | 0.0 | 23.52 | 440000 | nan | 0.0315 | 0.0010 | | 0.0 | 23.78 | 445000 | nan | 0.0315 | 0.0010 | | 0.0 | 24.05 | 450000 | nan | 0.0315 | 0.0010 | | 0.0 | 24.32 | 455000 | nan | 0.0315 | 0.0010 | | 0.0 | 24.58 | 460000 | nan | 0.0315 | 0.0010 | | 0.0 | 24.85 | 465000 | nan | 0.0315 | 0.0010 | | 0.0 | 25.12 | 470000 | nan | 0.0315 | 0.0010 | | 0.0 | 25.39 | 475000 | nan | 0.0315 | 0.0010 | | 0.0 | 25.65 | 480000 | nan | 0.0315 | 0.0010 | | 0.0 | 25.92 | 485000 | nan | 0.0315 | 0.0010 | | 0.0 | 26.19 | 490000 | nan | 0.0315 | 0.0010 | | 0.0 | 26.46 | 495000 | nan | 0.0315 | 0.0010 | | 0.0 | 26.72 | 500000 | nan | 0.0315 | 0.0010 | | 0.0 | 26.99 | 505000 | nan | 0.0315 | 0.0010 | | 0.0 | 27.26 | 510000 | nan | 0.0315 | 0.0010 | | 0.0 | 27.52 | 515000 | nan | 0.0315 | 0.0010 | | 0.0 | 27.79 | 520000 | nan | 0.0315 | 0.0010 | | 0.0 | 28.06 | 525000 | nan | 0.0315 | 0.0010 | | 0.0 | 28.33 | 530000 | nan | 0.0315 | 0.0010 | | 0.0 | 28.59 | 535000 | nan | 0.0315 | 0.0010 | | 0.0 | 28.86 | 540000 | nan | 0.0315 | 0.0010 | | 0.0 | 29.13 | 545000 | nan | 0.0315 | 0.0010 | | 0.0 | 29.39 | 550000 | nan | 0.0315 | 0.0010 | | 0.0 | 29.66 | 555000 | nan | 0.0315 | 0.0010 | | 0.0 | 29.93 | 560000 | nan | 0.0315 | 0.0010 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3