bert-ner-2 – AI Model by mpalaval | AlphaNeural AI

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mpalaval/
bert-ner-2

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token-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

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bert-ner-2

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: 0.7358
Precision: 0.1646
Recall: 0.4605
F1: 0.2425
Accuracy: 0.8784

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: 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	Precision	Recall	F1	Accuracy
No log	1.0	149	0.5651	0.1347	0.4192	0.2038	0.8686
No log	2.0	298	0.5818	0.1440	0.4227	0.2148	0.8785
No log	3.0	447	0.6011	0.1432	0.3986	0.2107	0.8808
0.0328	4.0	596	0.5546	0.1613	0.3986	0.2297	0.8955
0.0328	5.0	745	0.7685	0.1371	0.4467	0.2098	0.8600
0.0328	6.0	894	0.7755	0.1486	0.4570	0.2243	0.8686
0.0102	7.0	1043	0.6831	0.1669	0.4570	0.2445	0.8834
0.0102	8.0	1192	0.7698	0.1524	0.4639	0.2294	0.8715
0.0102	9.0	1341	0.7303	0.1681	0.4708	0.2477	0.8791
0.0102	10.0	1490	0.7358	0.1646	0.4605	0.2425	0.8784

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1