---
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_2_0_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: 0.8427043808209728
    - name: Recall
      type: recall
      value: 0.8737482117310443
    - name: F1
      type: f1
      value: 0.8579455662862159
    - name: Accuracy
      type: accuracy
      value: 0.9552753162160115
---

<!-- 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. -->

# CNEC_2_0_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: 0.3354
- Precision: 0.8427
- Recall: 0.8737
- F1: 0.8579
- Accuracy: 0.9553

## 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: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.288         | 2.22  | 1000  | 0.2461          | 0.7705    | 0.7926 | 0.7814 | 0.9413   |
| 0.1551        | 4.44  | 2000  | 0.2270          | 0.8116    | 0.8444 | 0.8277 | 0.9503   |
| 0.0963        | 6.67  | 3000  | 0.2220          | 0.8181    | 0.8623 | 0.8396 | 0.9533   |
| 0.0619        | 8.89  | 4000  | 0.2520          | 0.8202    | 0.8598 | 0.8395 | 0.9507   |
| 0.044         | 11.11 | 5000  | 0.2613          | 0.8332    | 0.8680 | 0.8502 | 0.9535   |
| 0.0283        | 13.33 | 6000  | 0.2734          | 0.8377    | 0.8673 | 0.8522 | 0.9546   |
| 0.0227        | 15.56 | 7000  | 0.2908          | 0.8390    | 0.8687 | 0.8536 | 0.9546   |
| 0.0173        | 17.78 | 8000  | 0.3083          | 0.8393    | 0.8670 | 0.8529 | 0.9528   |
| 0.013         | 20.0  | 9000  | 0.3238          | 0.8333    | 0.8673 | 0.8500 | 0.9522   |
| 0.0103        | 22.22 | 10000 | 0.3352          | 0.8325    | 0.8712 | 0.8515 | 0.9539   |
| 0.0091        | 24.44 | 11000 | 0.3299          | 0.8400    | 0.8655 | 0.8526 | 0.9542   |
| 0.0073        | 26.67 | 12000 | 0.3376          | 0.8387    | 0.8666 | 0.8524 | 0.9535   |
| 0.0065        | 28.89 | 13000 | 0.3354          | 0.8427    | 0.8737 | 0.8579 | 0.9553   |


### Framework versions

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