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

This model is a fine-tuned version of [deepvk/RuModernBERT-base](https://huggingface.co/deepvk/RuModernBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2121
- F1: 0.8730
- Precision: 0.8596
- Recall: 0.8868

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
| 0.5866        | 0.32  | 200  | 0.2772          | 0.7188 | 0.6785    | 0.7640 |
| 0.4701        | 0.64  | 400  | 0.2204          | 0.7867 | 0.7535    | 0.8230 |
| 0.4275        | 0.96  | 600  | 0.1895          | 0.8191 | 0.7932    | 0.8467 |
| 0.3282        | 1.28  | 800  | 0.1970          | 0.8339 | 0.8155    | 0.8531 |
| 0.2545        | 1.6   | 1000 | 0.1765          | 0.8553 | 0.8427    | 0.8684 |
| 0.2703        | 1.92  | 1200 | 0.1528          | 0.8626 | 0.8512    | 0.8744 |
| 0.1658        | 2.24  | 1400 | 0.1862          | 0.8610 | 0.8476    | 0.8748 |
| 0.1571        | 2.56  | 1600 | 0.1882          | 0.8617 | 0.8464    | 0.8776 |
| 0.1604        | 2.88  | 1800 | 0.1715          | 0.8760 | 0.8638    | 0.8884 |
| 0.0633        | 3.2   | 2000 | 0.2327          | 0.8656 | 0.8443    | 0.8880 |
| 0.0925        | 3.52  | 2200 | 0.2032          | 0.8757 | 0.8641    | 0.8876 |
| 0.0730        | 3.84  | 2400 | 0.2121          | 0.8730 | 0.8596    | 0.8868 |


### Framework versions

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