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

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Baljinnyam/
xlm-roberta-base-mongolian-ner-finetuning

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bloom-mongolian-ner-demo

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: 0.1048
Precision: 0.9267
Recall: 0.9354
F1: 0.9310
Accuracy: 0.9796

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
0.195	1.0	477	0.0947	0.8845	0.8994	0.8919	0.9707
0.0848	2.0	954	0.0761	0.9095	0.9235	0.9164	0.9774
0.0614	3.0	1431	0.0724	0.9218	0.9317	0.9267	0.9797
0.0452	4.0	1908	0.0756	0.9283	0.9350	0.9316	0.9806
0.035	5.0	2385	0.0824	0.9221	0.9337	0.9279	0.9796
0.0263	6.0	2862	0.0895	0.9191	0.9319	0.9254	0.9787
0.02	7.0	3339	0.0991	0.9238	0.9335	0.9286	0.9789
0.0148	8.0	3816	0.1005	0.9277	0.9358	0.9317	0.9798
0.0124	9.0	4293	0.1014	0.9254	0.9356	0.9305	0.9801
0.01	10.0	4770	0.1048	0.9267	0.9354	0.9310	0.9796

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3