bact_roberta_large_essentiality

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

• Loss: 0.4253
• Accuracy: 0.8152
• Precision: 0.8148
• Recall: 0.8159
• F1: 0.8153

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: 1e-05
• train_batch_size: 50
• eval_batch_size: 50
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 200
• 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
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	148	0.5084	0.7545	0.7120	0.8545	0.7768
No log	2.0	296	0.4916	0.7710	0.7436	0.8273	0.7832
No log	3.0	444	0.4772	0.7823	0.7433	0.8624	0.7984
2.0612	4.0	592	0.4455	0.8008	0.7755	0.8466	0.8095
2.0612	5.0	740	0.4282	0.8056	0.8265	0.7735	0.7991
2.0612	6.0	888	0.4282	0.8071	0.8382	0.7609	0.7977
1.7027	7.0	1036	0.4304	0.8079	0.7849	0.8482	0.8153
1.7027	8.0	1184	0.4276	0.8102	0.8180	0.7979	0.8078
1.7027	9.0	1332	0.4222	0.8148	0.8139	0.8162	0.8150
1.7027	10.0	1480	0.4253	0.8152	0.8148	0.8159	0.8153

Framework versions

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