distilbert-ner-improved

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: 0.0471
• Precision: 0.9588
• Recall: 0.9758
• F1: 0.9672
• Accuracy: 0.9875

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: 32
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• 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
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.0197	2.64	100	0.5256	0.4831	0.4319	0.4561	0.8719
0.1435	5.2667	200	0.1149	0.9453	0.9329	0.9390	0.9744
0.0655	7.9067	300	0.0673	0.9473	0.9688	0.9579	0.9809
0.0441	10.5333	400	0.0754	0.9708	0.9693	0.9700	0.9832
0.0236	13.16	500	0.0790	0.9697	0.9772	0.9734	0.9832
0.0171	15.8	600	0.0849	0.9670	0.9797	0.9733	0.9835

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0