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fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-with-ITTL-without-freeze-LR-1e-05

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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: >-
      fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-with-ITTL-without-freeze-LR-1e-05
    results: []

fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-with-ITTL-without-freeze-LR-1e-05

This model is a fine-tuned version of indolem/indobert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0515
  • Exact Match: 65.9686
  • F1: 71.4684

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
6.1822 0.49 36 2.5004 49.8691 49.8691
3.6893 0.98 72 1.9896 49.8691 49.8691
2.2116 1.48 108 1.8516 49.2147 49.7070
2.2116 1.97 144 1.7367 50.1309 52.0399
1.9945 2.46 180 1.5956 51.7016 56.3444
1.7443 2.95 216 1.4508 54.9738 59.4030
1.5782 3.45 252 1.3234 59.9476 65.0857
1.5782 3.94 288 1.2652 58.1152 63.9949
1.4004 4.44 324 1.1784 62.0419 67.5268
1.241 4.93 360 1.1573 60.4712 66.5284
1.241 5.42 396 1.1217 62.4346 67.8923
1.1603 5.91 432 1.0997 63.3508 68.7351
1.0849 6.41 468 1.0832 64.3979 69.5781
1.0209 6.9 504 1.0773 64.0052 69.3072
1.0209 7.4 540 1.0500 65.0524 70.4355
0.9802 7.89 576 1.0644 65.3141 70.7507
0.9536 8.38 612 1.0516 65.5759 70.9704
0.9536 8.87 648 1.0395 65.4450 71.2117
0.9319 9.37 684 1.0411 65.8377 71.3692
0.9091 9.86 720 1.0515 65.9686 71.4684

Framework versions

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