BC5CDR_bioBERT_NER

• Loss: 0.0808
• Seqeval classification report: precision recall f1-score support
  Chemical 0.99 0.98 0.98 103336 Disease 0.83 0.88 0.85 6944
  micro avg 0.97 0.97 0.97 110280 macro avg 0.91 0.93 0.92 110280 weighted avg 0.98 0.97 0.97 110280

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

Chemical       0.98      0.98      0.98    103336
 Disease       0.79      0.87      0.83      6944

Chemical       0.99      0.98      0.98    103336
 Disease       0.79      0.87      0.83      6944

Chemical       0.99      0.98      0.98    103336
 Disease       0.83      0.88      0.85      6944

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0