prodm93/covid_fakenews_classifier_LF

covid_fakenews_longformer_model

This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0571
• Accuracy: 0.9787
• F1: 0.9816
• Precision: 0.9796
• Recall: 0.9836

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 64
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	27	0.6234	0.7938	0.8324	0.7855	0.8852
No log	2.0	54	0.3374	0.8744	0.8959	0.8604	0.9344
No log	3.0	81	0.1397	0.9645	0.9702	0.9421	1.0
No log	4.0	108	0.1219	0.9621	0.9672	0.9672	0.9672
No log	4.8341	130	0.0571	0.9787	0.9816	0.9796	0.9836

Framework versions

• Transformers 4.49.0
• Pytorch 2.2.1
• Datasets 3.3.2
• Tokenizers 0.21.0