metadata

library_name: transformers
base_model: ai-forever/ruBert-large
tags:
  - generated_from_trainer
datasets:
  - universal_dependencies
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ruBert-large-upos
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: universal_dependencies
          type: universal_dependencies
          config: ru_syntagrus
          split: validation
          args: ru_syntagrus
        metrics:
          - name: Precision
            type: precision
            value: 0.8307441967265208
          - name: Recall
            type: recall
            value: 0.7502322735093846
          - name: F1
            type: f1
            value: 0.783084706036028
          - name: Accuracy
            type: accuracy
            value: 0.868562326706389

ruBert-large-upos

This model is a fine-tuned version of ai-forever/ruBert-large on the universal_dependencies dataset. It achieves the following results on the evaluation set:

Loss: 0.4344
Precision: 0.8307
Recall: 0.7502
F1: 0.7831
Accuracy: 0.8686

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: 5e-05
train_batch_size: 16
eval_batch_size: 8
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
No log	1.0	338	0.4759	0.7967	0.7249	0.7532	0.8557
No log	2.0	676	0.4344	0.8307	0.7502	0.7831	0.8686
No log	3.0	1014	0.6906	0.7842	0.7480	0.7563	0.8674
No log	4.0	1352	0.4757	0.8185	0.7578	0.7777	0.8816
No log	5.0	1690	0.6291	0.7791	0.7721	0.7670	0.8792
No log	6.0	2028	0.6466	0.7967	0.7677	0.7721	0.8863
No log	7.0	2366	0.7072	0.7751	0.7700	0.7704	0.8809
No log	8.0	2704	0.7623	0.7957	0.7678	0.7749	0.8838
No log	9.0	3042	0.7458	0.7922	0.7716	0.7773	0.8873
No log	10.0	3380	0.7560	0.7916	0.7709	0.7767	0.8869

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1