initial-dq-model

This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1677
• Precision: 0.7763
• Recall: 0.9380
• F1: 0.8495
• Accuracy: 0.9423

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	Precision	Recall	F1	Accuracy
0.2251	1.0	1220	0.1768	0.7481	0.9264	0.8277	0.9378
0.186	2.0	2440	0.1677	0.7763	0.9380	0.8495	0.9423

Framework versions

• Transformers 4.25.1
• Pytorch 1.10.2+cu113
• Datasets 2.8.0
• Tokenizers 0.13.2