Docket_Classification_NER_04_15

This model is a fine-tuned version of answerdotai/ModernBERT-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5623
• Precision: 0.8737
• Recall: 0.8823
• F1: 0.8760

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.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_ratio: 0.1
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
No log	1.0	21	0.4220	0.8540	0.8819	0.8621
No log	2.0	42	0.4700	0.8680	0.8596	0.8620
No log	3.0	63	0.4701	0.8683	0.8705	0.8682
No log	4.0	84	0.5636	0.8680	0.8765	0.8698
No log	4.7901	100	0.5623	0.8737	0.8823	0.8760

Framework versions

• Transformers 4.51.1
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1