BC5CDR_SciBERT_NER

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BC5CDR_SciBERT_NER

This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0818

• Seqeval classification report: precision recall f1-score support

  Chemical 0.92 0.94 0.93 7079 Disease 0.98 0.98 0.98 103426

  micro avg 0.98 0.97 0.98 110505 macro avg 0.95 0.96 0.95 110505 weighted avg 0.98 0.97 0.98 110505

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       0.91      0.93      0.92      7079
 Disease       0.98      0.97      0.97    103426

micro avg 0.97 0.97 0.97 110505 macro avg 0.94 0.95 0.95 110505 weighted avg 0.97 0.97 0.97 110505 | | No log | 2.0 | 286 | 0.0830 | precision recall f1-score support

Chemical       0.93      0.93      0.93      7079
 Disease       0.98      0.97      0.98    103426

micro avg 0.98 0.97 0.97 110505 macro avg 0.96 0.95 0.95 110505 weighted avg 0.98 0.97 0.97 110505 | | No log | 3.0 | 429 | 0.0818 | precision recall f1-score support

Chemical       0.92      0.94      0.93      7079
 Disease       0.98      0.98      0.98    103426

micro avg 0.98 0.97 0.98 110505 macro avg 0.95 0.96 0.95 110505 weighted avg 0.98 0.97 0.98 110505 |

Framework versions

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