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rubert-finetuned-ner – AI Model by smi-egor | AlphaNeural AI
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rubert-finetuned-ner
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transformers
safetensors
bert
token-classification
generated_from_trainer
DeepPavlov/rubert-base-cased
finetune
autotrain_compatible
endpoints_compatible
us
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rubert-finetuned-ner
This model is a fine-tuned version of
DeepPavlov/rubert-base-cased
on an unknown dataset. It achieves the following results on the evaluation set:
eval_loss: 0.3835
eval_precision: 0.8432
eval_recall: 0.8607
eval_f1: 0.8519
eval_accuracy: 0.9448
eval_runtime: 18.2998
eval_samples_per_second: 546.453
eval_steps_per_second: 4.317
epoch: 0.5008
step: 313
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: 0.0001
train_batch_size: 32
eval_batch_size: 128
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.05
num_epochs: 2
Framework versions
Transformers 5.5.0
Pytorch 2.10.0+cu128
Datasets 4.8.4
Tokenizers 0.22.2
