BERT_BIOMAT_NER_1000 – AI Model by judithrosell | AlphaNeural AI

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judithrosell/
BERT_BIOMAT_NER_1000

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google-bert/bert-base-uncased

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BERT_BIOMAT_NER_1000

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

Loss: 0.3920
Precision: 0.9495
Recall: 0.9444
F1: 0.9470
Accuracy: 0.9380

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: 32
eval_batch_size: 32
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
No log	1.0	211	0.2669	0.9407	0.9352	0.9380	0.9275
No log	2.0	422	0.2809	0.9457	0.9432	0.9444	0.9343
0.2064	3.0	633	0.3114	0.9472	0.9454	0.9463	0.9353
0.2064	4.0	844	0.3323	0.9491	0.9422	0.9456	0.9358
0.0481	5.0	1055	0.3478	0.9493	0.9441	0.9467	0.9382
0.0481	6.0	1266	0.3731	0.9486	0.9438	0.9462	0.9374
0.0481	7.0	1477	0.3723	0.9491	0.9445	0.9468	0.9379
0.0201	8.0	1688	0.3830	0.9489	0.9443	0.9466	0.9369
0.0201	9.0	1899	0.3873	0.9503	0.9448	0.9475	0.9378
0.0106	10.0	2110	0.3920	0.9495	0.9444	0.9470	0.9380

Framework versions

Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1