distil-bert-fintuned-issues-cfpb-complaints

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