JNLPBA_bioBERT_NER

• Loss: 0.1445
• Seqeval classification report: precision recall f1-score support

     DNA       0.72      0.80      0.76       507
     RNA       0.81      0.83      0.82      1593

  cell_line 0.76 0.78 0.77 5750 cell_type 0.76 0.81 0.79 618 protein 0.81 0.81 0.81 1452
  micro avg 0.77 0.80 0.78 9920 macro avg 0.77 0.81 0.79 9920 weighted avg 0.77 0.80 0.78 9920

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

     DNA       0.72      0.81      0.76       507
     RNA       0.78      0.82      0.80      1593

     DNA       0.72      0.81      0.76       507
     RNA       0.83      0.79      0.81      1593

     DNA       0.72      0.80      0.76       507
     RNA       0.81      0.83      0.82      1593

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0