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

Loss: 0.0071
Precision: 0.9312
Recall: 0.9329
F1: 0.9320
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	0.9989	435	0.0060	0.8714	0.9217	0.8958	0.9981
0.0156	2.0	871	0.0044	0.9183	0.9217	0.92	0.9987
0.0038	2.9989	1306	0.0040	0.8969	0.9404	0.9181	0.9987
0.0025	4.0	1742	0.0045	0.9078	0.9357	0.9215	0.9986
0.0016	4.9989	2177	0.0054	0.9182	0.9096	0.9139	0.9986
0.0011	6.0	2613	0.0053	0.9152	0.9254	0.9203	0.9986
0.0009	6.9989	3048	0.0060	0.9263	0.9366	0.9314	0.9987
0.0009	8.0	3484	0.0059	0.9181	0.9404	0.9291	0.9988
0.0005	8.9989	3919	0.0067	0.9258	0.9301	0.9279	0.9988
0.0003	9.9885	4350	0.0071	0.9312	0.9329	0.9320	0.9988

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

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