xlm-roberta-base-mongolian-ner – AI Model by nemuwn | AlphaNeural AI

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nemuwn/
xlm-roberta-base-mongolian-ner

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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xlm-roberta-base-mongolian-ner

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

Loss: 0.1166
Precision: 0.9251
Recall: 0.9335
F1: 0.9293
Accuracy: 0.9787

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
0.2013	1.0	477	0.0958	0.8951	0.9124	0.9037	0.9731
0.0846	2.0	954	0.0825	0.9155	0.9240	0.9197	0.9774
0.0622	3.0	1431	0.0844	0.9109	0.9235	0.9172	0.9766
0.0456	4.0	1908	0.0940	0.9174	0.9266	0.9220	0.9767
0.0347	5.0	2385	0.1015	0.9184	0.9284	0.9234	0.9770
0.0253	6.0	2862	0.1117	0.9174	0.9254	0.9214	0.9764
0.0203	7.0	3339	0.1147	0.9225	0.9310	0.9267	0.9780
0.0152	8.0	3816	0.1129	0.9229	0.9316	0.9272	0.9779
0.0129	9.0	4293	0.1150	0.9245	0.9324	0.9285	0.9784
0.0102	10.0	4770	0.1166	0.9251	0.9335	0.9293	0.9787

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3