This model is a fine-tuned version of distilbert-base-uncased on the New York Times Topics dataset. It achieves the following results on the evaluation set:

Loss: 0.2871
Accuracy: 0.9135

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: 1e-05
train_batch_size: 128
eval_batch_size: 128
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	Accuracy
0.386	1.0	1340	0.3275	0.8921
0.2833	2.0	2680	0.2840	0.9033
0.2411	3.0	4020	0.2694	0.9102
0.2069	4.0	5360	0.2665	0.9114
0.1796	5.0	6700	0.2657	0.9128
0.1636	6.0	8040	0.2674	0.9142
0.144	7.0	9380	0.2761	0.9129
0.1277	8.0	10720	0.2820	0.9125
0.1201	9.0	12060	0.2853	0.9136
0.1104	10.0	13400	0.2871	0.9135

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

alex-miller/nyt-cat

DistilBERT base classify news topics - Devinit

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alex-miller/
nyt-cat