message-parser-ner

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0666
• Precision: 0.9750
• Recall: 0.9756
• F1: 0.9750
• Accuracy: 0.9756
• O Precision: 0.9840
• O Recall: 0.9884
• O F1: 0.9862
• Content Precision: 0.8776
• Content Recall: 0.7414
• Content F1: 0.8037
• Person Precision: 0.9501
• Person Recall: 0.9716
• Person F1: 0.9607

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: 16
• eval_batch_size: 16
• seed: 42
• 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: 100
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	O Precision	O Recall	O F1	Content Precision	Content Recall	Content F1	Person Precision	Person Recall	Person F1
0.1374	1.0	129	0.1257	0.9507	0.9536	0.9515	0.9536	0.9716	0.9815	0.9765	0.7008	0.5115	0.5914	0.9004	0.9225	0.9113
0.0886	2.0	258	0.0865	0.9695	0.9682	0.9687	0.9682	0.9876	0.9758	0.9817	0.7316	0.7989	0.7637	0.9331	0.9754	0.9538
0.0406	3.0	387	0.0774	0.9748	0.9751	0.9741	0.9751	0.9802	0.9919	0.9861	0.9528	0.6954	0.8040	0.9478	0.9603	0.9540
0.0394	4.0	516	0.0698	0.9748	0.9753	0.9746	0.9753	0.9828	0.9893	0.9860	0.9007	0.7299	0.8063	0.9481	0.9679	0.9579
0.0259	5.0	645	0.0666	0.9750	0.9756	0.9750	0.9756	0.9840	0.9884	0.9862	0.8776	0.7414	0.8037	0.9501	0.9716	0.9607

Framework versions

• Transformers 4.56.0
• Pytorch 2.8.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.0