This model is a fine-tuned version of cis-lmu/glot500-base on the universal_dependencies dataset. It achieves the following results on the evaluation set:

Loss: 0.6914
Precision: 0.8393
Recall: 0.8246
F1: 0.8318
Accuracy: 0.8492

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: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	197	1.0680	0.7495	0.7185	0.7337	0.7598
No log	2.0	394	0.6914	0.8393	0.8246	0.8318	0.8492

Framework versions

Transformers 4.46.3
Pytorch 2.5.1
Datasets 3.1.0
Tokenizers 0.20.3

summerdevlin46/glot500_model_ru_taiga

glot500_model_ru_taiga

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

summerdevlin46/
glot500_model_ru_taiga