roberta-base-ner-demo-turshilt4

This model is a fine-tuned version of bayartsogt/mongolian-roberta-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1101
• Precision: 0.9354
• Recall: 0.9427
• F1: 0.9390
• Accuracy: 0.9815

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: 32
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 15
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.9016	0.9958	119	0.1663	0.7064	0.7773	0.7401	0.9416
0.1182	2.0	239	0.1002	0.8118	0.8752	0.8423	0.9656
0.064	2.9958	358	0.0733	0.9162	0.9282	0.9221	0.9788
0.0366	4.0	478	0.0826	0.9221	0.9297	0.9259	0.9791
0.0233	4.9958	597	0.0843	0.9251	0.9359	0.9305	0.9798
0.0157	6.0	717	0.0898	0.9305	0.9395	0.9350	0.9802
0.0121	6.9958	836	0.0964	0.9269	0.9369	0.9319	0.9795
0.0093	8.0	956	0.0995	0.9332	0.9408	0.9370	0.9805
0.0072	8.9958	1075	0.1046	0.9306	0.9387	0.9346	0.9799
0.0057	10.0	1195	0.1060	0.9332	0.9410	0.9371	0.9804
0.0049	10.9958	1314	0.1052	0.9322	0.9399	0.9360	0.9810
0.0042	12.0	1434	0.1091	0.9350	0.9416	0.9383	0.9811
0.0037	12.9958	1553	0.1098	0.9350	0.9414	0.9382	0.9812
0.0035	14.0	1673	0.1099	0.9362	0.9426	0.9394	0.9815
0.0033	14.9372	1785	0.1101	0.9354	0.9427	0.9390	0.9815

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1