This model is a fine-tuned version of bert-base-cased on the article250v4_wikigold_split dataset. It achieves the following results on the evaluation set:

Loss: 0.2337
Precision: 0.6301
Recall: 0.6385
F1: 0.6342
Accuracy: 0.9239

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	96	0.2343	0.5944	0.6046	0.5994	0.9191
No log	2.0	192	0.2215	0.6239	0.6412	0.6325	0.9251
No log	3.0	288	0.2337	0.6301	0.6385	0.6342	0.9239

Framework versions

Transformers 4.17.0
Pytorch 1.11.0+cu113
Datasets 2.4.0
Tokenizers 0.11.6

DOOGLAK/Article_250v4_NER_Model_3Epochs_AUGMENTED

Article_250v4_NER_Model_3Epochs_AUGMENTED

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DOOGLAK/
Article_250v4_NER_Model_3Epochs_AUGMENTED