roberta-base-ner-test – AI Model by Dondog | AlphaNeural AI

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Dondog/
roberta-base-ner-test

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token-classification

generated_from_trainer

bayartsogt/mongolian-roberta-base

autotrain_compatible

endpoints_compatible

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roberta-base-ner-test

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: 0.1051
Precision: 0.9154
Recall: 0.9295
F1: 0.9224
Accuracy: 0.9778

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: 128
eval_batch_size: 64
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
0.4118	1.0	60	0.1230	0.7683	0.8344	0.8000	0.9584
0.1013	2.0	120	0.0996	0.8134	0.8677	0.8397	0.9649
0.0694	3.0	180	0.0961	0.8295	0.8783	0.8532	0.9676
0.0523	4.0	240	0.0861	0.9030	0.9198	0.9113	0.9762
0.0309	5.0	300	0.0847	0.9088	0.9239	0.9163	0.9775
0.0236	6.0	360	0.0950	0.9103	0.9253	0.9177	0.9772
0.019	7.0	420	0.0974	0.9158	0.9277	0.9217	0.9775
0.0153	8.0	480	0.0996	0.9139	0.9278	0.9208	0.9781
0.0122	9.0	540	0.1029	0.9143	0.9284	0.9213	0.9781
0.0104	10.0	600	0.1051	0.9154	0.9295	0.9224	0.9778

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2