token_classification_NER

This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2888
Precision: 0.5572
Recall: 0.3837
F1: 0.4544
Accuracy: 0.9464

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2756	0.6546	0.2688	0.3811	0.9398
No log	2.0	426	0.2604	0.5433	0.3373	0.4162	0.9434
0.1817	3.0	639	0.2846	0.5891	0.3494	0.4386	0.9460
0.1817	4.0	852	0.2784	0.5614	0.3772	0.4512	0.9463
0.0509	5.0	1065	0.2888	0.5572	0.3837	0.4544	0.9464

Framework versions

Transformers 4.53.3
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

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dany0407/token_classification_NER

token_classification_NER

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

token_classification_NER

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions