This model is a fine-tuned version of bert-base-cased on the article250v5_wikigold_split dataset. It achieves the following results on the evaluation set:

Loss: 0.2378
Precision: 0.6724
Recall: 0.6475
F1: 0.6597
Accuracy: 0.9277

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	101	0.2402	0.5235	0.5003	0.5116	0.9157
No log	2.0	202	0.2579	0.6314	0.6043	0.6176	0.9189
No log	3.0	303	0.2378	0.6724	0.6475	0.6597	0.9277

Framework versions

Transformers 4.17.0
Pytorch 1.11.0+cu113
Datasets 2.4.0
Tokenizers 0.11.6

DOOGLAK/Article_250v5_NER_Model_3Epochs_AUGMENTED

Article_250v5_NER_Model_3Epochs_AUGMENTED

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DOOGLAK/
Article_250v5_NER_Model_3Epochs_AUGMENTED