finetuned_text_class

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4994
• Accuracy: 0.7702
• Recall: 0.8076
• Precision: 0.7557
• F1: 0.7808

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: 8e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1
0.499	0.9961	193	0.4700	0.7602	0.7599	0.7652	0.7625
0.3852	1.9974	387	0.4994	0.7702	0.8076	0.7557	0.7808
0.1778	2.9987	581	0.6317	0.7638	0.6688	0.8320	0.7415
0.1007	4.0	775	0.8801	0.7609	0.7662	0.7628	0.7645
0.0567	4.9806	965	1.0289	0.7657	0.7586	0.7744	0.7664

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1