bert-finetuned-n2c2-ner – AI Model by georgeleung30 | AlphaNeural AI

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georgeleung30/
bert-finetuned-n2c2-ner

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-finetuned-n2c2-ner

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2116
Precision: 0.9059
Recall: 0.8858
F1: 0.8958
Accuracy: 0.9758

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: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0545	1.0	10469	0.1255	0.8929	0.8933	0.8931	0.9762
0.0639	2.0	20938	0.1136	0.8933	0.8784	0.8858	0.9747
0.0452	3.0	31407	0.1221	0.8864	0.8991	0.8927	0.9753
0.0284	4.0	41876	0.1453	0.9003	0.8821	0.8911	0.9756
0.0269	5.0	52345	0.1587	0.9011	0.8934	0.8972	0.9765
0.0202	6.0	62814	0.1756	0.9190	0.8660	0.8917	0.9755
0.0153	7.0	73283	0.1818	0.9063	0.8831	0.8945	0.9757
0.0119	8.0	83752	0.2012	0.9163	0.8744	0.8948	0.9760
0.0122	9.0	94221	0.1986	0.9001	0.8908	0.8954	0.9757
0.0073	10.0	104690	0.2116	0.9059	0.8858	0.8958	0.9758

Framework versions

Transformers 4.30.2
Pytorch 1.8.1+cu111
Datasets 2.13.1
Tokenizers 0.13.3