bert-base-uncased-biobert – AI Model by AleMarroquin18 | AlphaNeural AI

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AleMarroquin18/
bert-base-uncased-biobert

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token-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-base-uncased-biobert

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

Loss: 0.0999
Precision: 0.9400
Recall: 0.9709
F1: 0.9552
Accuracy: 0.9768

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: 16
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4627	1.0	612	0.1159	0.9268	0.9554	0.9409	0.9701
0.1413	2.0	1224	0.1089	0.9274	0.9722	0.9493	0.9734
0.0953	3.0	1836	0.0993	0.9400	0.9684	0.9540	0.9767
0.0746	4.0	2448	0.0983	0.9399	0.9714	0.9554	0.9768
0.054	5.0	3060	0.0999	0.9400	0.9709	0.9552	0.9768

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3