This model is a fine-tuned version of bert-base-uncased on Amazon Massive dataset (only en-US subset). It achieves the following results on the evaluation set:

Loss: 0.4897
Accuracy: 0.8903
F1: 0.8903

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
2.5862	1.0	720	1.0160	0.8096	0.8096
1.0591	2.0	1440	0.6003	0.8716	0.8716
0.4151	3.0	2160	0.5113	0.8859	0.8859
0.3028	4.0	2880	0.5030	0.8883	0.8883
0.1852	5.0	3600	0.4897	0.8903	0.8903

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1

cartesinus/bert-base-uncased-amazon-massive-intent

bert-base-uncased-amazon-massive-intent

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

cartesinus/
bert-base-uncased-amazon-massive-intent