bert-finetuned-ner – AI Model by dayvidwang | AlphaNeural AI

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dayvidwang/
bert-finetuned-ner

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-finetuned-ner

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

Loss: 0.1622
Precision: 0.7774
Recall: 0.7937
F1: 0.7854
Accuracy: 0.9707

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: 8e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	131	0.1355	0.6880	0.7298	0.7083	0.9604
No log	2.0	262	0.1194	0.7564	0.7727	0.7645	0.9684
No log	3.0	393	0.1277	0.7731	0.7868	0.7799	0.9691
0.0433	4.0	524	0.1433	0.7553	0.7829	0.7688	0.9685
0.0433	5.0	655	0.1515	0.7734	0.7946	0.7839	0.9700
0.0433	6.0	786	0.1518	0.7819	0.8008	0.7912	0.9708
0.0433	7.0	917	0.1602	0.7752	0.7914	0.7832	0.9704
0.0094	8.0	1048	0.1622	0.7774	0.7937	0.7854	0.9707

Framework versions

Transformers 4.30.1
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3