---
library_name: transformers
license: apache-2.0
base_model: deepvk/RuModernBERT-small
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: rumodernbert_ner_ft_small
  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_ner_ft_small

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: 0.2842
- F1: 0.8257
- Precision: 0.8080
- Recall: 0.8443

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
| 1.3906        | 0.32  | 200  | 0.6057          | 0.4393 | 0.3840    | 0.5132 |
| 0.8110        | 0.64  | 400  | 0.3561          | 0.6269 | 0.5835    | 0.6774 |
| 0.6350        | 0.96  | 600  | 0.3008          | 0.7213 | 0.6812    | 0.7665 |
| 0.4992        | 1.28  | 800  | 0.2633          | 0.7803 | 0.7699    | 0.7909 |
| 0.3823        | 1.6   | 1000 | 0.2300          | 0.8084 | 0.7936    | 0.8238 |
| 0.3995        | 1.92  | 1200 | 0.2162          | 0.8009 | 0.7918    | 0.8102 |
| 0.2860        | 2.24  | 1400 | 0.2195          | 0.8109 | 0.7988    | 0.8234 |
| 0.2833        | 2.56  | 1600 | 0.2065          | 0.8140 | 0.7955    | 0.8335 |
| 0.2660        | 2.88  | 1800 | 0.2374          | 0.8145 | 0.8020    | 0.8274 |
| 0.1466        | 3.2   | 2000 | 0.2693          | 0.8079 | 0.7838    | 0.8335 |
| 0.1809        | 3.52  | 2200 | 0.2504          | 0.8299 | 0.8210    | 0.8391 |
| 0.1685        | 3.84  | 2400 | 0.2267          | 0.8315 | 0.8134    | 0.8503 |
| 0.0860        | 4.16  | 2600 | 0.2938          | 0.8273 | 0.8106    | 0.8447 |
| 0.0884        | 4.48  | 2800 | 0.2980          | 0.8183 | 0.7959    | 0.8419 |
| 0.1116        | 4.8   | 3000 | 0.2842          | 0.8257 | 0.8080    | 0.8443 |


### Framework versions

- Transformers 5.1.0
- Pytorch 2.10.0+cu128
- Datasets 4.7.0
- Tokenizers 0.22.2