bert-azahead-v1.1 – AI Model by zwellington | AlphaNeural AI

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zwellington/
bert-azahead-v1.1

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-azahead-v1.1

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

Loss: 0.4785
Accuracy: 0.7917
F1: 0.6154
Precision: 0.6667
Recall: 0.5714

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: 2e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.6318	1.0	10	0.5247	0.6667	0.0	0.0	0.0
0.5623	2.0	20	0.4065	0.7917	0.5455	0.75	0.4286
0.4688	3.0	30	0.3514	0.7917	0.5455	0.75	0.4286
0.4252	4.0	40	0.3224	0.8333	0.6667	0.8	0.5714
0.2409	5.0	50	0.4115	0.75	0.4	0.6667	0.2857
0.2196	6.0	60	0.3672	0.7917	0.6667	0.625	0.7143
0.1417	7.0	70	0.4441	0.7917	0.5455	0.75	0.4286
0.0842	8.0	80	0.4422	0.7917	0.6154	0.6667	0.5714
0.065	9.0	90	0.4556	0.7917	0.6154	0.6667	0.5714
0.0657	10.0	100	0.4785	0.7917	0.6154	0.6667	0.5714

Framework versions

Transformers 4.31.0
Pytorch 2.2.0+cu121
Datasets 2.16.1
Tokenizers 0.13.2