bert-base-cased-ner – AI Model by itsmeboris | AlphaNeural AI

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itsmeboris/
bert-base-cased-ner

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-base-cased-ner

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: 0.3793
Job Title precision: 0.8079
Job Title recall: 0.8248
Job Title f1: 0.8163
Loc precision: 0.8911
Loc recall: 0.9081
Loc f1: 0.8995
Org precision: 0.6484
Org recall: 0.7620
Org f1: 0.7006
Misc precision: 0.6134
Misc recall: 0.7201
Misc f1: 0.6625
Precision: 0.7800
Recall: 0.8265
F1: 0.8025
Accuracy: 0.8606

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	Job Title precision	Job Title recall	Job Title f1	Loc precision	Loc recall	Loc f1	Org precision	Org recall	Org f1	Misc precision	Misc recall	Misc f1	Precision	Recall	F1	Accuracy
No log	1.0	308	0.3793	0.8079	0.8248	0.8163	0.8911	0.9081	0.8995	0.6484	0.7620	0.7006	0.6134	0.7201	0.6625	0.7800	0.8265	0.8025	0.8606
0.4249	2.0	616	0.3866	0.7911	0.8728	0.8299	0.8676	0.9541	0.9088	0.6551	0.7886	0.7157	0.6623	0.6962	0.6789	0.7719	0.8669	0.8167	0.8685

Framework versions

Transformers 4.28.1
Pytorch 1.7.1+cu110
Datasets 2.12.0
Tokenizers 0.13.2