BC5CDR_bioBERT_NER

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BC5CDR_bioBERT_NER

This model is a fine-tuned version of dmis-lab/biobert-v1.1 on the None dataset. It achieves the following results on the evaluation set:

• 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

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: 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

micro avg 0.97 0.97 0.97 110280 macro avg 0.89 0.92 0.90 110280 weighted avg 0.97 0.97 0.97 110280 | | No log | 2.0 | 286 | 0.0823 | precision recall f1-score support

Chemical       0.99      0.98      0.98    103336
 Disease       0.79      0.87      0.83      6944

micro avg 0.97 0.97 0.97 110280 macro avg 0.89 0.92 0.91 110280 weighted avg 0.97 0.97 0.97 110280 | | No log | 3.0 | 429 | 0.0808 | 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 |

Framework versions

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