BioBERT-finetuned-ner

This is a BioBERT-based model is fine-tuned to perform Named Entity Recognition for drug names and adverse drug effects.

This model classifies input tokens into one of five classes:

B-DRUG: beginning of a drug entity I-DRUG: within a drug entity B-EFFECT: beginning of an AE entity I-EFFECT: within an AE entity O: outside either of the above entities

BioBERT-finetuned-ner

This model is a fine-tuned version of Dinithi/BioBERT on ade_corpus_v2. It achieves the following results on the evaluation set:

• Loss: 0.1602
• Precision: 0.8136
• Recall: 0.8961
• F1: 0.8528
• Accuracy: 0.9524

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.1+cu121
• Tokenizers 0.20.3

Dataset used

Dataset: https://huggingface.co/datasets/ade_corpus_v2