BC5CDR_PubMedBERT_NER

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BC5CDR_PubMedBERT_NER

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0783

• Seqeval classification report: precision recall f1-score support

  Chemical 0.99 0.98 0.98 103336 Disease 0.76 0.86 0.81 3447

  micro avg 0.98 0.98 0.98 106783 macro avg 0.87 0.92 0.89 106783 weighted avg 0.98 0.98 0.98 106783

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       0.99      0.97      0.98    103336
 Disease       0.68      0.88      0.76      3447

micro avg 0.97 0.97 0.97 106783 macro avg 0.83 0.92 0.87 106783 weighted avg 0.98 0.97 0.97 106783 | | No log | 2.0 | 286 | 0.0804 | precision recall f1-score support

Chemical       0.99      0.98      0.98    103336
 Disease       0.75      0.86      0.80      3447

micro avg 0.98 0.97 0.97 106783 macro avg 0.87 0.92 0.89 106783 weighted avg 0.98 0.97 0.98 106783 | | No log | 3.0 | 429 | 0.0783 | precision recall f1-score support

Chemical       0.99      0.98      0.98    103336
 Disease       0.76      0.86      0.81      3447

micro avg 0.98 0.98 0.98 106783 macro avg 0.87 0.92 0.89 106783 weighted avg 0.98 0.98 0.98 106783 |

Framework versions

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