---
base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC_1_1_ext_slavicbert
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: cnec
      type: cnec
      config: default
      split: validation
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8606811145510835
    - name: Recall
      type: recall
      value: 0.8915018706574025
    - name: F1
      type: f1
      value: 0.8758204253084799
    - name: Accuracy
      type: accuracy
      value: 0.9626885008032336
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# CNEC_1_1_ext_slavicbert

This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the cnec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2572
- Precision: 0.8607
- Recall: 0.8915
- F1: 0.8758
- Accuracy: 0.9627

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3946        | 1.72  | 500  | 0.1925          | 0.7835    | 0.8471 | 0.8141 | 0.9467   |
| 0.1653        | 3.44  | 1000 | 0.1627          | 0.8340    | 0.8675 | 0.8504 | 0.9572   |
| 0.1183        | 5.15  | 1500 | 0.1700          | 0.8378    | 0.8808 | 0.8588 | 0.9595   |
| 0.0869        | 6.87  | 2000 | 0.1901          | 0.8554    | 0.8728 | 0.8640 | 0.9589   |
| 0.0661        | 8.59  | 2500 | 0.2037          | 0.8482    | 0.8867 | 0.8670 | 0.9595   |
| 0.053         | 10.31 | 3000 | 0.2011          | 0.8460    | 0.8867 | 0.8659 | 0.9609   |
| 0.043         | 12.03 | 3500 | 0.2216          | 0.8555    | 0.8888 | 0.8718 | 0.9593   |
| 0.0358        | 13.75 | 4000 | 0.2245          | 0.8492    | 0.8878 | 0.8680 | 0.9603   |
| 0.0296        | 15.46 | 4500 | 0.2401          | 0.8513    | 0.8872 | 0.8689 | 0.9603   |
| 0.0264        | 17.18 | 5000 | 0.2415          | 0.8564    | 0.8862 | 0.8710 | 0.9610   |
| 0.0212        | 18.9  | 5500 | 0.2570          | 0.8557    | 0.8872 | 0.8712 | 0.9622   |
| 0.0205        | 20.62 | 6000 | 0.2540          | 0.8567    | 0.8883 | 0.8722 | 0.9616   |
| 0.0167        | 22.34 | 6500 | 0.2573          | 0.8568    | 0.8894 | 0.8728 | 0.9614   |
| 0.0161        | 24.05 | 7000 | 0.2572          | 0.8607    | 0.8915 | 0.8758 | 0.9627   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0