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scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_155

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deberta-v2
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metadata
base_model: haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1
datasets:
  - massive
library_name: transformers
license: mit
metrics:
  - accuracy
  - f1
tags:
  - generated_from_trainer
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 haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1 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 0.2672 5000 nan 0.0315 0.0010
0.0 0.5344 10000 nan 0.0315 0.0010
0.0 0.8017 15000 nan 0.0315 0.0010
0.0 1.0689 20000 nan 0.0315 0.0010
0.0 1.3361 25000 nan 0.0315 0.0010
0.0 1.6033 30000 nan 0.0315 0.0010
0.0 1.8706 35000 nan 0.0315 0.0010
0.0 2.1378 40000 nan 0.0315 0.0010
0.0 2.4050 45000 nan 0.0315 0.0010
0.0 2.6722 50000 nan 0.0315 0.0010
0.0 2.9394 55000 nan 0.0315 0.0010
0.0 3.2067 60000 nan 0.0315 0.0010
0.0 3.4739 65000 nan 0.0315 0.0010
0.0 3.7411 70000 nan 0.0315 0.0010
0.0 4.0083 75000 nan 0.0315 0.0010
0.0 4.2756 80000 nan 0.0315 0.0010
0.0 4.5428 85000 nan 0.0315 0.0010
0.0 4.8100 90000 nan 0.0315 0.0010
0.0 5.0772 95000 nan 0.0315 0.0010
0.0 5.3444 100000 nan 0.0315 0.0010
0.0 5.6117 105000 nan 0.0315 0.0010
0.0 5.8789 110000 nan 0.0315 0.0010
0.0 6.1461 115000 nan 0.0315 0.0010
0.0 6.4133 120000 nan 0.0315 0.0010
0.0 6.6806 125000 nan 0.0315 0.0010
0.0 6.9478 130000 nan 0.0315 0.0010
0.0 7.2150 135000 nan 0.0315 0.0010
0.0 7.4822 140000 nan 0.0315 0.0010
0.0 7.7495 145000 nan 0.0315 0.0010
0.0 8.0167 150000 nan 0.0315 0.0010
0.0 8.2839 155000 nan 0.0315 0.0010
0.0 8.5511 160000 nan 0.0315 0.0010
0.0 8.8183 165000 nan 0.0315 0.0010
0.0 9.0856 170000 nan 0.0315 0.0010
0.0 9.3528 175000 nan 0.0315 0.0010
0.0 9.6200 180000 nan 0.0315 0.0010
0.0 9.8872 185000 nan 0.0315 0.0010
0.0 10.1545 190000 nan 0.0315 0.0010
0.0 10.4217 195000 nan 0.0315 0.0010
0.0 10.6889 200000 nan 0.0315 0.0010
0.0 10.9561 205000 nan 0.0315 0.0010
0.0 11.2233 210000 nan 0.0315 0.0010
0.0 11.4906 215000 nan 0.0315 0.0010
0.0 11.7578 220000 nan 0.0315 0.0010
0.0 12.0250 225000 nan 0.0315 0.0010
0.0 12.2922 230000 nan 0.0315 0.0010
0.0 12.5595 235000 nan 0.0315 0.0010
0.0 12.8267 240000 nan 0.0315 0.0010
0.0 13.0939 245000 nan 0.0315 0.0010
0.0 13.3611 250000 nan 0.0315 0.0010
0.0 13.6283 255000 nan 0.0315 0.0010
0.0 13.8956 260000 nan 0.0315 0.0010
0.0 14.1628 265000 nan 0.0315 0.0010
0.0 14.4300 270000 nan 0.0315 0.0010
0.0 14.6972 275000 nan 0.0315 0.0010
0.0 14.9645 280000 nan 0.0315 0.0010
0.0 15.2317 285000 nan 0.0315 0.0010
0.0 15.4989 290000 nan 0.0315 0.0010
0.0 15.7661 295000 nan 0.0315 0.0010
0.0 16.0333 300000 nan 0.0315 0.0010
0.0 16.3006 305000 nan 0.0315 0.0010
0.0 16.5678 310000 nan 0.0315 0.0010
0.0 16.8350 315000 nan 0.0315 0.0010
0.0 17.1022 320000 nan 0.0315 0.0010
0.0 17.3695 325000 nan 0.0315 0.0010
0.0 17.6367 330000 nan 0.0315 0.0010
0.0 17.9039 335000 nan 0.0315 0.0010
0.0 18.1711 340000 nan 0.0315 0.0010
0.0 18.4384 345000 nan 0.0315 0.0010
0.0 18.7056 350000 nan 0.0315 0.0010
0.0 18.9728 355000 nan 0.0315 0.0010
0.0 19.2400 360000 nan 0.0315 0.0010
0.0 19.5072 365000 nan 0.0315 0.0010
0.0 19.7745 370000 nan 0.0315 0.0010
0.0 20.0417 375000 nan 0.0315 0.0010
0.0 20.3089 380000 nan 0.0315 0.0010
0.0 20.5761 385000 nan 0.0315 0.0010
0.0 20.8434 390000 nan 0.0315 0.0010
0.0 21.1106 395000 nan 0.0315 0.0010
0.0 21.3778 400000 nan 0.0315 0.0010
0.0 21.6450 405000 nan 0.0315 0.0010
0.0 21.9122 410000 nan 0.0315 0.0010
0.0 22.1795 415000 nan 0.0315 0.0010
0.0 22.4467 420000 nan 0.0315 0.0010
0.0 22.7139 425000 nan 0.0315 0.0010
0.0 22.9811 430000 nan 0.0315 0.0010
0.0 23.2484 435000 nan 0.0315 0.0010
0.0 23.5156 440000 nan 0.0315 0.0010
0.0 23.7828 445000 nan 0.0315 0.0010
0.0 24.0500 450000 nan 0.0315 0.0010
0.0 24.3172 455000 nan 0.0315 0.0010
0.0 24.5845 460000 nan 0.0315 0.0010
0.0 24.8517 465000 nan 0.0315 0.0010
0.0 25.1189 470000 nan 0.0315 0.0010
0.0 25.3861 475000 nan 0.0315 0.0010
0.0 25.6534 480000 nan 0.0315 0.0010
0.0 25.9206 485000 nan 0.0315 0.0010
0.0 26.1878 490000 nan 0.0315 0.0010
0.0 26.4550 495000 nan 0.0315 0.0010
0.0 26.7222 500000 nan 0.0315 0.0010
0.0 26.9895 505000 nan 0.0315 0.0010
0.0 27.2567 510000 nan 0.0315 0.0010
0.0 27.5239 515000 nan 0.0315 0.0010
0.0 27.7911 520000 nan 0.0315 0.0010
0.0 28.0584 525000 nan 0.0315 0.0010
0.0 28.3256 530000 nan 0.0315 0.0010
0.0 28.5928 535000 nan 0.0315 0.0010
0.0 28.8600 540000 nan 0.0315 0.0010
0.0 29.1273 545000 nan 0.0315 0.0010
0.0 29.3945 550000 nan 0.0315 0.0010
0.0 29.6617 555000 nan 0.0315 0.0010
0.0 29.9289 560000 nan 0.0315 0.0010

Framework versions

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