bert-base-uncased-finetuned-ner-biobert – AI Model by GetialJuan | AlphaNeural AI

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GetialJuan/
bert-base-uncased-finetuned-ner-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-finetuned-ner-biobert

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

Loss: 0.1078
Precision: 0.9466
Recall: 0.9706
F1: 0.9585
Accuracy: 0.9788

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: 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	Precision	Recall	F1	Accuracy
0.4602	1.0	612	0.1189	0.9232	0.9431	0.9330	0.9683
0.1394	2.0	1224	0.1071	0.9236	0.9702	0.9463	0.9728
0.0939	3.0	1836	0.0952	0.9445	0.9652	0.9547	0.9774
0.0759	4.0	2448	0.0968	0.9417	0.9680	0.9547	0.9770
0.0549	5.0	3060	0.0981	0.9448	0.9670	0.9558	0.9773
0.0481	6.0	3672	0.1025	0.9416	0.9706	0.9559	0.9774
0.0401	7.0	4284	0.1045	0.9407	0.9705	0.9554	0.9771
0.0387	8.0	4896	0.1042	0.9455	0.9705	0.9578	0.9784
0.0301	9.0	5508	0.1059	0.9475	0.9704	0.9588	0.9790
0.0286	10.0	6120	0.1078	0.9466	0.9706	0.9585	0.9788

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0