bart-finetuned-steel-news-general

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

• Loss: 1.3448
• Bertscore Precision: 50.1636
• Bertscore Recall: 48.9044
• Bertscore F1: 49.5061

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: 3e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 500
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bertscore Precision	Bertscore Recall	Bertscore F1
2.681	1.0	152	1.6008	42.9429	43.7367	43.2784
1.6115	2.0	304	1.4576	48.3023	48.1847	48.1681
1.4817	3.0	456	1.4068	48.9183	46.4002	47.5919
1.2738	4.0	608	1.3563	47.6492	47.8798	47.7005
1.1491	5.0	760	1.3365	49.1544	48.0816	48.5460

Framework versions

• Transformers 4.57.6
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.2