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

output

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