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: 0.1478
Precision: 0.7360
Recall: 0.8434
F1: 0.7861
Accuracy: 0.8974

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	48	0.1606	0.6667	0.7829	0.7201	0.8609
No log	2.0	96	0.1518	0.6646	0.7758	0.7159	0.8574
No log	3.0	144	0.1535	0.6152	0.7509	0.6763	0.8417
No log	4.0	192	0.1510	0.6747	0.7972	0.7308	0.8626
No log	5.0	240	0.1562	0.7547	0.8541	0.8013	0.9061
No log	6.0	288	0.1436	0.7205	0.8256	0.7695	0.8974
No log	7.0	336	0.1466	0.7484	0.8363	0.7899	0.9026
No log	8.0	384	0.1450	0.7690	0.8648	0.8141	0.9130
No log	9.0	432	0.1474	0.7453	0.8541	0.7960	0.9043
No log	10.0	480	0.1478	0.7360	0.8434	0.7861	0.8974

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

SiriusW/multi_balanced_model

multi_balanced_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

SiriusW/
multi_balanced_model