relation-biobert-biocause

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2103
• Precision: 0.1164
• Recall: 0.625
• F1: 0.1963
• Accuracy: 0.9448
• Relation P: 0.1164
• Relation R: 0.625
• Relation F1: 0.1963

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: 4e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Relation P	Relation R	Relation F1
0.6563	0.1282	20	0.2984	0.0211	0.2105	0.0384	0.8265	0.0211	0.2105	0.0384
0.6563	0.2564	40	0.2302	0.0763	0.4605	0.1308	0.9266	0.0763	0.4605	0.1308
0.6563	0.3846	60	0.4003	0.1406	0.5921	0.2273	0.9617	0.1406	0.5921	0.2273
0.6563	0.5128	80	0.2185	0.0554	0.4671	0.0990	0.8933	0.0554	0.4671	0.0990
0.6563	0.6410	100	0.2261	0.1345	0.7105	0.2262	0.9510	0.1345	0.7105	0.2262
0.6563	0.7692	120	0.2315	0.1259	0.6579	0.2114	0.9502	0.1259	0.6579	0.2114
0.6563	0.8974	140	0.2324	0.1417	0.6711	0.2339	0.9547	0.1417	0.6711	0.2339

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1