metadata

language:
  - mn
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: mongolian-ner-test-xlm-roberta-large-ner-hrl
    results: []

mongolian-ner-test-xlm-roberta-large-ner-hrl

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

Loss: 0.5337
Precision: 0.3060
Recall: 0.1406
F1: 0.1927
Accuracy: 0.8591

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
0.6123	1.0	477	0.5570	0.2422	0.0999	0.1414	0.8536
0.5411	2.0	954	0.5407	0.2914	0.1294	0.1792	0.8572
0.5288	3.0	1431	0.5394	0.2944	0.1309	0.1812	0.8576
0.5212	4.0	1908	0.5346	0.3015	0.1324	0.1840	0.8581
0.5156	5.0	2385	0.5298	0.3131	0.1394	0.1929	0.8595
0.5103	6.0	2862	0.5301	0.3086	0.1419	0.1944	0.8595
0.5041	7.0	3339	0.5318	0.3083	0.1411	0.1936	0.8592
0.4981	8.0	3816	0.5308	0.3117	0.1421	0.1952	0.8595
0.4931	9.0	4293	0.5329	0.3062	0.1400	0.1922	0.8592
0.4885	10.0	4770	0.5337	0.3060	0.1406	0.1927	0.8591

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3