custom_ner_bert-uncased – AI Model by MoinKhan3012 | AlphaNeural AI

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MoinKhan3012/
custom_ner_bert-uncased

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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fine_tune_bert_masking

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

Loss: 0.4384
Overall Precision: 0.5833
Overall Recall: 0.4691
Overall F1: 0.52
Overall Accuracy: 0.9323
App F1: 0.52

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	App F1
0.2048	3.45	100	0.2554	0.5442	0.4124	0.4692	0.9210	0.4692
0.0658	6.9	200	0.3147	0.6119	0.4227	0.5	0.9314	0.5
0.0279	10.34	300	0.3509	0.5357	0.4639	0.4972	0.9281	0.4972
0.0146	13.79	400	0.4032	0.5759	0.4691	0.5170	0.9314	0.5170
0.0093	17.24	500	0.4384	0.5833	0.4691	0.52	0.9323	0.52

Framework versions

Transformers 4.30.0
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.13.3