CeLLaTe3.0_no_vague_pubmed

This model is a fine-tuned version of Mardiyyah/cellate1.0-tapt_freeze_llrd_ww_mask-LR_2e-05 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2279
  • Precision: 0.7706
  • Recall: 0.8273
  • F1: 0.7979
  • Accuracy: 0.9672

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: 16
  • eval_batch_size: 16
  • seed: 3407
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.8103 1.0 402 0.1761 0.5832 0.7740 0.6652 0.9529
0.1291 2.0 804 0.1448 0.6999 0.7842 0.7396 0.9618
0.0705 3.0 1206 0.1829 0.6816 0.8217 0.7451 0.9572
0.0393 4.0 1608 0.1870 0.6990 0.7938 0.7434 0.9620
0.0253 5.0 2010 0.2277 0.7128 0.7826 0.7461 0.9612
0.0155 6.0 2412 0.2306 0.7044 0.7877 0.7437 0.9641
0.0115 7.0 2814 0.2300 0.7706 0.8273 0.7979 0.9672
0.0082 8.0 3216 0.2642 0.7331 0.8121 0.7706 0.9644
0.006 9.0 3618 0.2808 0.7183 0.7887 0.7519 0.9634
0.0046 10.0 4020 0.2718 0.7441 0.8151 0.7780 0.9648
0.0034 11.0 4422 0.2977 0.7215 0.8172 0.7664 0.9635
0.0024 12.0 4824 0.3096 0.7462 0.8151 0.7791 0.9637
0.0022 13.0 5226 0.3120 0.7335 0.8217 0.7751 0.9643
0.0017 14.0 5628 0.3049 0.7420 0.8238 0.7807 0.9652
0.0014 15.0 6030 0.3178 0.7311 0.8273 0.7763 0.9649
0.0014 16.0 6432 0.3172 0.7349 0.8167 0.7736 0.9647
0.0009 17.0 6834 0.3216 0.7516 0.8192 0.7840 0.9644
0.0007 18.0 7236 0.3236 0.7441 0.8228 0.7815 0.9655
0.0007 19.0 7638 0.3225 0.7543 0.8217 0.7866 0.9657
0.0008 20.0 8040 0.3233 0.7513 0.8207 0.7845 0.9655

Framework versions

  • Transformers 4.48.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.2
  • Tokenizers 0.21.0
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