bert-base-casedhealth_model1 – AI Model by mr-rov | AlphaNeural AI

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mr-rov/
bert-base-casedhealth_model1

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text-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

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test_trainer

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.1319
Accuracy: {'accuracy': 0.7010869565217391}

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: 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	92	0.9215	{'accuracy': 0.625}
No log	2.0	184	0.8079	{'accuracy': 0.6739130434782609}
No log	3.0	276	1.1221	{'accuracy': 0.6902173913043478}
No log	4.0	368	1.7338	{'accuracy': 0.6684782608695652}
No log	5.0	460	1.9489	{'accuracy': 0.6847826086956522}
0.3959	6.0	552	1.9017	{'accuracy': 0.7065217391304348}
0.3959	7.0	644	1.9854	{'accuracy': 0.7065217391304348}
0.3959	8.0	736	2.0787	{'accuracy': 0.7010869565217391}
0.3959	9.0	828	2.1006	{'accuracy': 0.7119565217391305}
0.3959	10.0	920	2.1319	{'accuracy': 0.7010869565217391}

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cpu
Datasets 2.16.1
Tokenizers 0.14.1