cause-bert-biocause – AI Model by alenatz | AlphaNeural AI

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alenatz/
cause-bert-biocause

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google-bert/bert-base-cased

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cause-bert-biocause

This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4364
Precision: 0.1647
Recall: 0.3459
F1: 0.2231
Accuracy: 0.8160
Cause P: 0.1647
Cause R: 0.3459
Cause F1: 0.2231

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Cause P	Cause R	Cause F1
0.6498	0.25	20	0.6248	0.0544	0.1698	0.0824	0.7705	0.0544	0.1698	0.0824
0.6498	0.5	40	0.5229	0.0532	0.1572	0.0795	0.6600	0.0532	0.1572	0.0795
0.6498	0.75	60	0.4613	0.1190	0.2327	0.1574	0.8274	0.1190	0.2327	0.1574
0.6498	1.0	80	0.4376	0.1460	0.2956	0.1954	0.8145	0.1460	0.2956	0.1954
0.6498	1.25	100	0.4660	0.1829	0.2956	0.2260	0.8312	0.1829	0.2956	0.2260
0.6498	1.5	120	0.4523	0.1902	0.3899	0.2557	0.8148	0.1902	0.3899	0.2557
0.6498	1.75	140	0.4414	0.1756	0.3711	0.2384	0.8138	0.1756	0.3711	0.2384
0.6498	2.0	160	0.4364	0.1647	0.3459	0.2231	0.8160	0.1647	0.3459	0.2231

Framework versions

Transformers 4.37.2
Pytorch 2.3.1.post100
Datasets 2.20.0
Tokenizers 0.15.1