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fine-tuned-DatasetQAS-IDK-MRC-with-indobert-large-p2-with-ITTL-without-freeze-LR-1e-05

This model is a fine-tuned version of indobenchmark/indobert-large-p2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2364
  • Exact Match: 50.2618
  • F1: 57.5214

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: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Exact Match F1
6.151 0.49 36 2.7223 32.5916 35.4445
3.5424 0.98 72 2.0664 24.2147 31.0371
2.2082 1.48 108 1.7388 28.0105 37.2690
2.2082 1.97 144 1.4742 37.0419 45.3625
1.6932 2.46 180 1.3193 43.3246 51.1270
1.3154 2.95 216 1.2731 46.2042 53.5503
1.1699 3.45 252 1.2327 46.4660 53.5656
1.1699 3.94 288 1.1998 48.1675 55.1907
1.0749 4.44 324 1.1949 51.0471 57.7164
0.9423 4.93 360 1.1855 50.6545 57.3903
0.9423 5.42 396 1.1931 51.3089 58.5981
0.9036 5.91 432 1.2045 50.3927 57.7468
0.8324 6.41 468 1.2363 48.2984 55.5302
0.7846 6.9 504 1.2364 50.2618 57.5214

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.2.0
  • Tokenizers 0.13.2
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