dhmsw-e-bert

DHMSW: E-BERT

Use

This model corresponds to E_BERT in our paper Targeting Transparency: Early Evidence on Mandatory Adoption of European Sustainability Reporting Standards. We use the model to classify sentences from firms' sustainability reports.

Base and metrics

This model is a fine-tuned version of distilbert/distilroberta-base on the esrs-sentences dataset. It achieves the following results on the evaluation set:

• Loss: 0.2439
• Accuracy: 0.9693
• F1: 0.9415
• Precision: 0.9430
• Recall: 0.9405

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Use 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_ratio: 0.1
• num_epochs: 15
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.57.3
• Pytorch 2.8.0+cu128
• Datasets 4.4.2
• Tokenizers 0.22.2