BERT-full-finetuned-ner-pablo

This model is a fine-tuned version of google-bert/bert-base-uncased on the n2c2 2018 dataset for the paper https://arxiv.org/abs/2409.19467. It achieves the following results on the evaluation set:

• Loss: 0.0854
• Precision: 0.7857
• Recall: 0.7899
• F1: 0.7878
• Accuracy: 0.9747

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	231	0.1015	0.7485	0.7440	0.7462	0.9703
No log	2.0	462	0.0878	0.7618	0.7750	0.7684	0.9728
0.2646	3.0	693	0.0859	0.7759	0.7912	0.7835	0.9737
0.2646	4.0	924	0.0854	0.7857	0.7899	0.7878	0.9747

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1