bert-ner-chunks – AI Model by moctarsmal | AlphaNeural AI

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moctarsmal/
bert-ner-chunks

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token-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

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bert-ner-chunks

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

Loss: 0.2207
Precision: 0.8917
Recall: 0.9214
F1: 0.9063
Accuracy: 0.9730

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.077	1.0	1756	0.0741	0.9068	0.9334	0.9199	0.9795
0.0443	2.0	3512	0.0649	0.9172	0.9428	0.9298	0.9841
0.0265	3.0	5268	0.0578	0.9250	0.9488	0.9368	0.9863
0.0109	4.0	7024	0.0651	0.9347	0.9492	0.9419	0.9866
0.005	5.0	8780	0.0692	0.9354	0.9502	0.9427	0.9869

Framework versions

Transformers 4.37.0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0