StivenLancheros/mBERT-base-Biomedical-NER

bert-base-multilingual-cased-finetuned-ner-4

#This model is part of a test for creating multilingual BioMedical NER systems. Not intended for proffesional use now.

This model is a fine-tuned version of bert-base-multilingual-cased on the CRAFT+BC4CHEMD+BioNLP09 datasets concatenated. It achieves the following results on the evaluation set:

• Loss: 0.1027
• Precision: 0.9830
• Recall: 0.9832
• F1: 0.9831
• Accuracy: 0.9799

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: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0658	1.0	6128	0.0751	0.9795	0.9795	0.9795	0.9758
0.0406	2.0	12256	0.0753	0.9827	0.9815	0.9821	0.9786
0.0182	3.0	18384	0.0934	0.9834	0.9825	0.9829	0.9796
0.011	4.0	24512	0.1027	0.9830	0.9832	0.9831	0.9799

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0