---
library_name: transformers
base_model: ai-forever/ruBert-large
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ruBert-large-upos
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: universal_dependencies
      type: universal_dependencies
      config: ru_syntagrus
      split: validation
      args: ru_syntagrus
    metrics:
    - name: Precision
      type: precision
      value: 0.8307441967265208
    - name: Recall
      type: recall
      value: 0.7502322735093846
    - name: F1
      type: f1
      value: 0.783084706036028
    - name: Accuracy
      type: accuracy
      value: 0.868562326706389
---

<!-- 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. -->

# ruBert-large-upos

This model is a fine-tuned version of [ai-forever/ruBert-large](https://huggingface.co/ai-forever/ruBert-large) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4344
- Precision: 0.8307
- Recall: 0.7502
- F1: 0.7831
- Accuracy: 0.8686

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 338  | 0.4759          | 0.7967    | 0.7249 | 0.7532 | 0.8557   |
| No log        | 2.0   | 676  | 0.4344          | 0.8307    | 0.7502 | 0.7831 | 0.8686   |
| No log        | 3.0   | 1014 | 0.6906          | 0.7842    | 0.7480 | 0.7563 | 0.8674   |
| No log        | 4.0   | 1352 | 0.4757          | 0.8185    | 0.7578 | 0.7777 | 0.8816   |
| No log        | 5.0   | 1690 | 0.6291          | 0.7791    | 0.7721 | 0.7670 | 0.8792   |
| No log        | 6.0   | 2028 | 0.6466          | 0.7967    | 0.7677 | 0.7721 | 0.8863   |
| No log        | 7.0   | 2366 | 0.7072          | 0.7751    | 0.7700 | 0.7704 | 0.8809   |
| No log        | 8.0   | 2704 | 0.7623          | 0.7957    | 0.7678 | 0.7749 | 0.8838   |
| No log        | 9.0   | 3042 | 0.7458          | 0.7922    | 0.7716 | 0.7773 | 0.8873   |
| No log        | 10.0  | 3380 | 0.7560          | 0.7916    | 0.7709 | 0.7767 | 0.8869   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1