| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: deepvk/RuModernBERT-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: rumodernbert_small_distill |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # rumodernbert_small_distill |
|
|
| This model is a fine-tuned version of [deepvk/RuModernBERT-small](https://huggingface.co/deepvk/RuModernBERT-small) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4484 |
| - F1: 0.8670 |
| - Precision: 0.8632 |
| - Recall: 0.8708 |
|
|
| ## 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: 3e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 32 |
| - 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: linear |
| - lr_scheduler_warmup_steps: 0.1 |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:| |
| | 4.8701 | 0.32 | 200 | 2.0232 | 0.4841 | 0.4288 | 0.5558 | |
| | 2.6246 | 0.64 | 400 | 1.0239 | 0.6799 | 0.6425 | 0.7219 | |
| | 2.0708 | 0.96 | 600 | 0.9332 | 0.7623 | 0.7271 | 0.8010 | |
| | 1.5292 | 1.28 | 800 | 0.7393 | 0.7809 | 0.7670 | 0.7953 | |
| | 1.1811 | 1.6 | 1000 | 0.6396 | 0.8126 | 0.8101 | 0.8150 | |
| | 1.2625 | 1.92 | 1200 | 0.6523 | 0.8085 | 0.7990 | 0.8182 | |
| | 0.8249 | 2.24 | 1400 | 0.5636 | 0.8322 | 0.8294 | 0.8351 | |
| | 0.8175 | 2.56 | 1600 | 0.5814 | 0.8361 | 0.8258 | 0.8467 | |
| | 0.7697 | 2.88 | 1800 | 0.5125 | 0.8446 | 0.8333 | 0.8563 | |
| | 0.5251 | 3.2 | 2000 | 0.5297 | 0.8374 | 0.8268 | 0.8483 | |
| | 0.5485 | 3.52 | 2200 | 0.4821 | 0.8542 | 0.8513 | 0.8571 | |
| | 0.5174 | 3.84 | 2400 | 0.4683 | 0.8614 | 0.8549 | 0.8680 | |
| | 0.4142 | 4.16 | 2600 | 0.4663 | 0.8614 | 0.8564 | 0.8664 | |
| | 0.3958 | 4.48 | 2800 | 0.4507 | 0.8622 | 0.8560 | 0.8684 | |
| | 0.4127 | 4.8 | 3000 | 0.4406 | 0.8576 | 0.8490 | 0.8664 | |
| | 0.2867 | 5.12 | 3200 | 0.4370 | 0.8677 | 0.8592 | 0.8764 | |
| | 0.3076 | 5.44 | 3400 | 0.4394 | 0.8612 | 0.8502 | 0.8724 | |
| | 0.2746 | 5.76 | 3600 | 0.4325 | 0.8664 | 0.8624 | 0.8704 | |
| | 0.2701 | 6.08 | 3800 | 0.4484 | 0.8670 | 0.8632 | 0.8708 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.3.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.7.0 |
| - Tokenizers 0.22.2 |
| |
