This model is a fine-tuned version of bert-base-cased on the article250v2_wikigold_split dataset. It achieves the following results on the evaluation set:

Loss: 0.2484
Precision: 0.6846
Recall: 0.6809
F1: 0.6827
Accuracy: 0.9284

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	102	0.2354	0.6141	0.6162	0.6151	0.9221
No log	2.0	204	0.2347	0.6732	0.6840	0.6786	0.9290
No log	3.0	306	0.2484	0.6846	0.6809	0.6827	0.9284

Framework versions

Transformers 4.17.0
Pytorch 1.11.0+cu113
Datasets 2.4.0
Tokenizers 0.11.6

DOOGLAK/Article_250v2_NER_Model_3Epochs_AUGMENTED

Article_250v2_NER_Model_3Epochs_AUGMENTED

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DOOGLAK/
Article_250v2_NER_Model_3Epochs_AUGMENTED