distilbert-base-uncased-finetuned-ner

This model is a fine-tuned version of distilbert-base-uncased on the conll2002 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2434
• Precision: 0.6413
• Recall: 0.6248
• F1: 0.6329
• Accuracy: 0.9311

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use 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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3551	1.0	521	0.2708	0.5957	0.5858	0.5907	0.9230
0.2055	2.0	1042	0.2434	0.6413	0.6248	0.6329	0.9311

Framework versions

• Transformers 4.46.0
• Pytorch 2.5.0+cpu
• Datasets 3.0.2
• Tokenizers 0.20.1