metadata

library_name: transformers
license: apache-2.0
base_model: cis-lmu/glot500-base
tags:
  - generated_from_trainer
datasets:
  - universal_dependencies
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: glot500_model_ru_taiga
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: universal_dependencies
          type: universal_dependencies
          config: ru_taiga
          split: test
          args: ru_taiga
        metrics:
          - name: Precision
            type: precision
            value: 0.8392572944297082
          - name: Recall
            type: recall
            value: 0.8245595746898781
          - name: F1
            type: f1
            value: 0.8318435166684194
          - name: Accuracy
            type: accuracy
            value: 0.8491576589736098

glot500_model_ru_taiga

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