bert-finetuned-ner-requirements – AI Model by thescripterr | AlphaNeural AI

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thescripterr/
bert-finetuned-ner-requirements

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token-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-finetuned-ner-requirements

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: 0.9697
Precision: 0.2404
Recall: 0.3788
F1: 0.2941
Accuracy: 0.75

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	10	1.5976	0.0	0.0	0.0	0.7226
No log	2.0	20	1.2824	0.1364	0.1364	0.1364	0.7555
No log	3.0	30	1.1017	0.1829	0.2273	0.2027	0.7464
No log	4.0	40	1.0788	0.1321	0.2121	0.1628	0.7464
No log	5.0	50	1.0091	0.1651	0.2727	0.2057	0.7482
No log	6.0	60	0.9949	0.1667	0.2879	0.2111	0.7427
No log	7.0	70	0.9766	0.2	0.3182	0.2456	0.7536
No log	8.0	80	0.9734	0.2202	0.3636	0.2743	0.7482
No log	9.0	90	0.9744	0.2336	0.3788	0.2890	0.75
No log	10.0	100	0.9697	0.2404	0.3788	0.2941	0.75

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.19.1