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