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: 0.2960
Precision: 0.7614
Recall: 0.7900
F1: 0.7754
Accuracy: 0.9143

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
0.6844	1.0	863	0.3318	0.7248	0.7739	0.7486	0.9004
0.2658	2.0	1726	0.3057	0.7686	0.7865	0.7774	0.9104
0.2126	3.0	2589	0.3072	0.7724	0.7939	0.7830	0.9115

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

0uz2z/distilbert-base-uncased-finetuned-ner

distilbert-base-uncased-finetuned-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

0uz2z/
distilbert-base-uncased-finetuned-ner