classify-clickbait-gpu

This model is a fine-tuned version of albert/albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0130
• Accuracy: 0.9976
• F1: 0.9976
• Precision: 0.9976
• Recall: 0.9976
• Accuracy Label Clickbait: 0.9933
• Accuracy Label Factual: 1.0

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Accuracy Label Clickbait	Accuracy Label Factual
0.0546	0.4831	100	0.0504	0.9902	0.9902	0.9902	0.9902	0.9866	0.9923
0.0071	0.9662	200	0.0060	0.9988	0.9988	0.9988	0.9988	0.9967	1.0
0.0008	1.4493	300	0.0088	0.9976	0.9976	0.9976	0.9976	0.9933	1.0
0.0006	1.9324	400	0.0310	0.9939	0.9939	0.9939	0.9939	0.9833	1.0
0.0007	2.4155	500	0.0002	1.0	1.0	1.0	1.0	1.0	1.0
0.0009	2.8986	600	0.0079	0.9988	0.9988	0.9988	0.9988	0.9967	1.0

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu118
• Datasets 2.19.1
• Tokenizers 0.19.1