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: 0.2843
Precision: 0.5375
Recall: 0.3920
F1: 0.4534
Accuracy: 0.9466

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	0.2777	0.6327	0.2651	0.3736	0.9396
No log	2.0	426	0.2499	0.5356	0.3698	0.4375	0.9437
0.1938	3.0	639	0.2813	0.5947	0.3522	0.4424	0.9457
0.1938	4.0	852	0.2752	0.5637	0.3939	0.4637	0.9471
0.0532	5.0	1065	0.2843	0.5375	0.3920	0.4534	0.9466

Framework versions

Transformers 4.53.3
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

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Surbhipatil/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