Russian-BERT – AI Model by Priyanka-Balivada | AlphaNeural AI

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Priyanka-Balivada/
Russian-BERT

like

0

text-classification

generated_from_trainer

google-bert/bert-base-uncased

10.57967/hf/5030

autotrain_compatible

endpoints_compatible

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russian-BERT

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0126
Accuracy: 0.8813
Precision: 0.8813
Recall: 0.8813
Micro-avg-recall: 0.8813
Micro-avg-precision: 0.8813

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	Micro-avg-recall	Micro-avg-precision
0.0795	1.0	750	0.8896	0.8637	0.8665	0.8637	0.8637	0.8637
0.073	2.0	1500	0.8294	0.8633	0.8640	0.8633	0.8633	0.8633
0.0001	3.0	2250	0.9873	0.8757	0.8779	0.8757	0.8757	0.8757
0.0432	4.0	3000	0.9801	0.8813	0.8817	0.8813	0.8813	0.8813
0.0002	5.0	3750	1.0126	0.8813	0.8813	0.8813	0.8813	0.8813

Framework versions

Transformers 4.44.2
Pytorch 2.4.0
Datasets 3.0.0
Tokenizers 0.19.1