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IndoNanoT5-base-TyDiQA

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LazarusNLP/IndoNanoT5-base-TyDiQA

This model is a fine-tuned version of LazarusNLP/IndoNanoT5-base on the indonlg dataset. It achieves the following results on the evaluation set:

  • Exact: 58.9474
  • F1: 72.1969
  • Total: 855
  • Hasans Exact: 58.9474
  • Hasans F1: 72.1969
  • Hasans Total: 855
  • Best Exact: 58.9474
  • Best Exact Thresh: 0.0
  • Best F1: 72.1969
  • Best F1 Thresh: 0.0
  • Loss: 0.1283

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: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Exact F1 Total Hasans Exact Hasans F1 Hasans Total Best Exact Best Exact Thresh Best F1 Best F1 Thresh Validation Loss
1.9173 1.0 606 45.1327 63.8499 565 45.1327 63.8499 565 45.1327 0.0 63.8499 0.0 0.1147
0.1971 2.0 1212 50.4425 68.7240 565 50.4425 68.7240 565 50.4425 0.0 68.7240 0.0 0.1025
0.1475 3.0 1818 53.8053 71.0124 565 53.8053 71.0124 565 53.8053 0.0 71.0124 0.0 0.0992
0.1175 4.0 2424 53.6283 71.1353 565 53.6283 71.1353 565 53.6283 0.0 71.1353 0.0 0.1008
0.0814 5.0 3030 53.4513 71.0439 565 53.4513 71.0439 565 53.4513 0.0 71.0439 0.0 0.1040
0.0665 6.0 3636 54.1593 71.5788 565 54.1593 71.5788 565 54.1593 0.0 71.5788 0.0 0.1051
0.0555 7.0 4242 54.8673 72.4372 565 54.8673 72.4372 565 54.8673 0.0 72.4372 0.0 0.1137
0.0483 8.0 4848 56.2832 72.3749 565 56.2832 72.3749 565 56.2832 0.0 72.3749 0.0 0.1188
0.0416 9.0 5454 55.5752 72.2892 565 55.5752 72.2892 565 55.5752 0.0 72.2892 0.0 0.1154
0.031 10.0 6060 55.0442 71.8127 565 55.0442 71.8127 565 55.0442 0.0 71.8127 0.0 0.1312
0.0278 11.0 6666 55.7522 73.4756 565 55.7522 73.4756 565 55.7522 0.0 73.4756 0.0 0.1253
0.0257 12.0 7272 55.7522 73.0958 565 55.7522 73.0958 565 55.7522 0.0 73.0958 0.0 0.1292
0.023 13.0 7878 56.2832 73.3269 565 56.2832 73.3269 565 56.2832 0.0 73.3269 0.0 0.1271

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.1
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Evaluation results

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