roberta-base-ner-demo – AI Model by Tengisbold | AlphaNeural AI

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Tengisbold/
roberta-base-ner-demo

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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roberta-base-ner-demo

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: 0.1349
Precision: 0.9210
Recall: 0.9330
F1: 0.9269
Accuracy: 0.9788

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: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1615	1.0	477	0.0917	0.8327	0.8817	0.8565	0.9680
0.0615	2.0	954	0.0917	0.8432	0.8918	0.8668	0.9701
0.0404	3.0	1431	0.0961	0.8411	0.8970	0.8682	0.9710
0.0227	4.0	1908	0.1056	0.9081	0.9262	0.9171	0.9770
0.0133	5.0	2385	0.1117	0.8781	0.9108	0.8942	0.9744
0.0083	6.0	2862	0.1231	0.9111	0.9288	0.9199	0.9775
0.0061	7.0	3339	0.1263	0.9110	0.9293	0.9200	0.9776
0.0071	8.0	3816	0.1316	0.9197	0.9302	0.9249	0.9783
0.0031	9.0	4293	0.1335	0.9228	0.9327	0.9277	0.9790
0.0021	10.0	4770	0.1349	0.9210	0.9330	0.9269	0.9788

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3