ner-gec-v2

This model is a fine-tuned version of bert-base-uncased on the fursov/gec_ner_val3 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2067
• Precision: 0.3670
• Recall: 0.2328
• F1: 0.2849
• Accuracy: 0.9420

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: 128
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2324	1.15	500	0.2359	0.2070	0.0883	0.1238	0.9353
0.1901	2.3	1000	0.2137	0.3467	0.2212	0.2701	0.9399

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.0+cu118
• Datasets 2.16.1
• Tokenizers 0.15.0