bact_roberta_base_essentiality_Network

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

• Loss: 0.4141
• Accuracy: 0.8310
• Precision: 0.8400
• Recall: 0.8178
• F1: 0.8287

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: 60
• eval_batch_size: 60
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 240
• 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: 0.1
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	72	0.6554	0.6588	0.6020	0.9368	0.7330
No log	2.0	144	0.4637	0.7838	0.7997	0.7573	0.7779
No log	3.0	216	0.4541	0.7931	0.7745	0.8271	0.7999
No log	4.0	288	0.4764	0.7982	0.7630	0.8652	0.8109
No log	5.0	360	0.4254	0.8129	0.8037	0.8280	0.8157
No log	6.0	432	0.4175	0.8224	0.8329	0.8066	0.8196
1.8208	7.0	504	0.4262	0.8259	0.8251	0.8271	0.8261
1.8208	8.0	576	0.4154	0.8296	0.8376	0.8178	0.8276
1.8208	9.0	648	0.4140	0.8310	0.8400	0.8178	0.8287
1.8208	10.0	720	0.4235	0.8254	0.8241	0.8275	0.8258

Framework versions

• Transformers 5.0.0
• Pytorch 2.9.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2