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: 0.6711
Accuracy: 0.7652
Precision: 0.5935
Recall: 0.5577
F1: 0.5702

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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.7269	1.0	11603	0.7187	0.7438	0.5853	0.5327	0.5491
0.612	2.0	23206	0.6711	0.7652	0.5935	0.5577	0.5702

Framework versions

Transformers 4.37.2
Pytorch 2.2.0
Datasets 2.17.1
Tokenizers 0.15.2

Mahesh9/distil-bert-fintuned-issues-cfpb-complaints

distil-bert-fintuned-issues-cfpb-complaints

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Mahesh9/
distil-bert-fintuned-issues-cfpb-complaints