This model is a fine-tuned version of michiyasunaga/BioLinkBERT-base on the Rodrigo1771/drugtemist-en-9-ner dataset. It achieves the following results on the evaluation set:

Loss: 0.0046
Precision: 0.9298
Recall: 0.9376
F1: 0.9336
Accuracy: 0.9988

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: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	437	0.0047	0.8995	0.9254	0.9123	0.9985
0.0144	2.0	874	0.0053	0.8960	0.9310	0.9132	0.9985
0.0038	3.0	1311	0.0046	0.9298	0.9376	0.9336	0.9988
0.0022	4.0	1748	0.0055	0.9202	0.9245	0.9224	0.9986
0.0019	5.0	2185	0.0053	0.9118	0.9348	0.9231	0.9986
0.0014	6.0	2622	0.0054	0.9194	0.9254	0.9224	0.9986
0.0009	7.0	3059	0.0073	0.9324	0.9254	0.9289	0.9986
0.0009	8.0	3496	0.0065	0.9341	0.9254	0.9298	0.9987
0.0005	9.0	3933	0.0069	0.9326	0.9292	0.9309	0.9987
0.0004	10.0	4370	0.0071	0.9249	0.9292	0.9270	0.9987

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

Rodrigo1771/BioLinkBERT-base-drugtemist-en-word2vec-9-ner

output

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Rodrigo1771/
BioLinkBERT-base-drugtemist-en-word2vec-9-ner