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

Loss: 0.1575
Precision: 0.7908
Recall: 0.8167
F1: 0.8035
Accuracy: 0.9533

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	144	0.2222	0.6615	0.6686	0.6650	0.9266
No log	2.0	288	0.1752	0.7359	0.7684	0.7518	0.9442
No log	3.0	432	0.1541	0.7709	0.7987	0.7846	0.9507
0.2098	4.0	576	0.1601	0.7755	0.8224	0.7983	0.9537
0.2098	5.0	720	0.1575	0.7908	0.8167	0.8035	0.9533

Framework versions

Transformers 4.33.0
Pytorch 2.0.0
Datasets 2.14.5
Tokenizers 0.13.3

mirfan899/uner-distilbert-ner

uner-distilbert-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mirfan899/
uner-distilbert-ner