BERT_BIOMAT_NER1800 – AI Model by judithrosell | AlphaNeural AI

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BERT_BIOMAT_NER1800

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google-bert/bert-base-uncased

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BERT_BIOMAT_NER1800

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: 0.1528
Precision: 0.8240
Recall: 0.8614
F1: 0.8422
Accuracy: 0.9741

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
0.257	1.0	869	0.0952	0.7898	0.8157	0.8025	0.9694
0.0707	2.0	1738	0.1023	0.8197	0.8494	0.8343	0.9729
0.0412	3.0	2607	0.1078	0.8234	0.8569	0.8398	0.9739
0.0263	4.0	3476	0.1201	0.8178	0.8675	0.8419	0.9732
0.0143	5.0	4345	0.1208	0.8317	0.8572	0.8443	0.9748
0.0094	6.0	5214	0.1353	0.8212	0.8566	0.8385	0.9736
0.0059	7.0	6083	0.1476	0.8128	0.8644	0.8378	0.9732
0.0047	8.0	6952	0.1474	0.8208	0.8630	0.8414	0.9741
0.0032	9.0	7821	0.1572	0.8129	0.8550	0.8334	0.9728
0.0024	10.0	8690	0.1528	0.8240	0.8614	0.8422	0.9741

Framework versions

Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1