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sagorbert_nwp_finetuning_def_v3

This model is a fine-tuned version of sagorsarker/bangla-bert-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.7146

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: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • 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 Validation Loss
4.1717 1.0 1551 3.9996
3.7173 2.0 3102 3.6406
3.4565 3.0 4653 3.4235
3.2657 4.0 6204 3.2330
3.1522 5.0 7755 3.2134
3.0686 6.0 9306 3.1522
2.9315 7.0 10857 3.0937
2.8902 8.0 12408 3.0556
2.7995 9.0 13959 3.0475
2.7451 10.0 15510 2.9813
2.7015 11.0 17061 2.9560
2.6528 12.0 18612 2.9613
2.5797 13.0 20163 2.9195
2.5343 14.0 21714 2.8609
2.4927 15.0 23265 2.8933
2.4433 16.0 24816 2.8718
2.3995 17.0 26367 2.8405
2.3875 18.0 27918 2.8703
2.3171 19.0 29469 2.8371
2.319 20.0 31020 2.8027
2.2824 21.0 32571 2.7959
2.2633 22.0 34122 2.8165
2.2149 23.0 35673 2.7747
2.1812 24.0 37224 2.7879
2.1677 25.0 38775 2.7723
2.1521 26.0 40326 2.7887
2.14 27.0 41877 2.7839
2.059 28.0 43428 2.8150
2.0881 29.0 44979 2.7617
2.0583 30.0 46530 2.7491
2.0574 31.0 48081 2.7303
2.0416 32.0 49632 2.7490
1.9837 33.0 51183 2.7419
1.9747 34.0 52734 2.7409
1.9486 35.0 54285 2.7757
1.941 36.0 55836 2.7546
1.9549 37.0 57387 2.7046
1.9346 38.0 58938 2.7700
1.8979 39.0 60489 2.7033
1.9104 40.0 62040 2.7383
1.8989 41.0 63591 2.6837
1.8691 42.0 65142 2.7084
1.8492 43.0 66693 2.7000
1.8271 44.0 68244 2.6792
1.8723 45.0 69795 2.7325
1.8208 46.0 71346 2.6998
1.8218 47.0 72897 2.7490
1.8305 48.0 74448 2.7394
1.8067 49.0 75999 2.6545
1.7974 50.0 77550 2.6925

Framework versions

  • Transformers 4.38.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.17.1
  • Tokenizers 0.15.2
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