---
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