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: 0.2720
Precision: 0.6096
Recall: 0.3170
F1: 0.4171
Accuracy: 0.9426

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2820	0.6278	0.2641	0.3718	0.9398
No log	2.0	426	0.2720	0.6096	0.3170	0.4171	0.9426

Framework versions

Transformers 4.37.2
Pytorch 2.2.0
Datasets 2.17.1
Tokenizers 0.15.2

madanagrawal/token_classifier

token_classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

madanagrawal/
token_classifier