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

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lltala/
bert-base-cased-ner

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token-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

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bert-base-cased-ner

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: 0.0894
Overall Precision: 0.5187
Overall Recall: 0.5814
Overall F1: 0.5483
Org Precision: 0.5127
Org Recall: 0.5277
Org F1: 0.5201
Per Precision: 0.7294
Per Recall: 0.8052
Per F1: 0.7654
Loc Precision: 0.4329
Loc Recall: 0.7474
Loc F1: 0.5483

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Org Precision	Org Recall	Org F1	Per Precision	Per Recall	Per F1	Loc Precision	Loc Recall	Loc F1
No log	1.0	53	0.1227	0.3066	0.3206	0.3134	0.3084	0.4104	0.3522	0.0	0.0	0.0	0.0	0.0	0.0
No log	2.0	106	0.1055	0.3967	0.4224	0.4091	0.3829	0.3860	0.3844	0.6964	0.5065	0.5865	0.3457	0.5895	0.4358
No log	3.0	159	0.0897	0.4867	0.5598	0.5207	0.4883	0.5098	0.4988	0.7011	0.7922	0.7439	0.375	0.6947	0.4871
No log	4.0	212	0.0901	0.5179	0.5712	0.5433	0.5227	0.5261	0.5244	0.6988	0.7532	0.7250	0.4096	0.7158	0.5211
No log	5.0	265	0.0894	0.5187	0.5814	0.5483	0.5127	0.5277	0.5201	0.7294	0.8052	0.7654	0.4329	0.7474	0.5483

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1