adr-ner

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

• Loss: 0.0434
• Precision: 0.7305
• Recall: 0.6934
• F1: 0.7115
• Accuracy: 0.9941

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: 12
• eval_batch_size: 12
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	107	0.0630	0.0	0.0	0.0	0.9876
No log	2.0	214	0.0308	0.4282	0.3467	0.3832	0.9900
No log	3.0	321	0.0254	0.5544	0.5603	0.5573	0.9920
No log	4.0	428	0.0280	0.6430	0.5751	0.6071	0.9929
0.0465	5.0	535	0.0266	0.5348	0.7146	0.6118	0.9915
0.0465	6.0	642	0.0423	0.7632	0.5793	0.6587	0.9939
0.0465	7.0	749	0.0336	0.6957	0.6765	0.6860	0.9939
0.0465	8.0	856	0.0370	0.6876	0.6702	0.6788	0.9936
0.0465	9.0	963	0.0349	0.6555	0.7040	0.6789	0.9932
0.0044	10.0	1070	0.0403	0.6910	0.6808	0.6858	0.9938
0.0044	11.0	1177	0.0415	0.7140	0.6808	0.6970	0.9939
0.0044	12.0	1284	0.0440	0.7349	0.6681	0.6999	0.9941
0.0044	13.0	1391	0.0423	0.7097	0.6977	0.7036	0.9941
0.0044	14.0	1498	0.0435	0.7174	0.6977	0.7074	0.9941
0.0006	15.0	1605	0.0434	0.7305	0.6934	0.7115	0.9941

Framework versions

• Transformers 4.14.1
• Pytorch 1.10.0+cu113
• Datasets 1.16.1
• Tokenizers 0.10.3