---
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_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: 0.8513220632856524
    - name: Recall
      type: recall
      value: 0.8671081677704194
    - name: F1
      type: f1
      value: 0.8591426071741033
    - name: Accuracy
      type: accuracy
      value: 0.9509352959214965
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# CNEC_1_1_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: 0.3720
- Precision: 0.8513
- Recall: 0.8671
- F1: 0.8591
- Accuracy: 0.9509

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3658        | 0.85  | 1000  | 0.2671          | 0.8101    | 0.8172 | 0.8136 | 0.9366   |
| 0.227         | 1.7   | 2000  | 0.2624          | 0.8190    | 0.8172 | 0.8181 | 0.9380   |
| 0.141         | 2.56  | 3000  | 0.2474          | 0.8317    | 0.8424 | 0.8370 | 0.9448   |
| 0.092         | 3.41  | 4000  | 0.2498          | 0.8412    | 0.8534 | 0.8472 | 0.9460   |
| 0.0839        | 4.26  | 5000  | 0.2689          | 0.8438    | 0.8583 | 0.8510 | 0.9489   |
| 0.0698        | 5.11  | 6000  | 0.2830          | 0.8420    | 0.8539 | 0.8479 | 0.9473   |
| 0.0507        | 5.96  | 7000  | 0.2902          | 0.8359    | 0.8503 | 0.8431 | 0.9468   |
| 0.0344        | 6.81  | 8000  | 0.3221          | 0.8310    | 0.8512 | 0.8410 | 0.9478   |
| 0.0249        | 7.67  | 9000  | 0.3262          | 0.8444    | 0.8508 | 0.8476 | 0.9478   |
| 0.0185        | 8.52  | 10000 | 0.3214          | 0.8458    | 0.8525 | 0.8492 | 0.9502   |
| 0.0151        | 9.37  | 11000 | 0.3399          | 0.8382    | 0.8578 | 0.8479 | 0.9499   |
| 0.01          | 10.22 | 12000 | 0.3348          | 0.8385    | 0.8574 | 0.8478 | 0.9492   |
| 0.0086        | 11.07 | 13000 | 0.3636          | 0.8395    | 0.8543 | 0.8468 | 0.9479   |
| 0.0092        | 11.93 | 14000 | 0.3644          | 0.8419    | 0.8578 | 0.8498 | 0.9485   |
| 0.0058        | 12.78 | 15000 | 0.3624          | 0.8450    | 0.8618 | 0.8533 | 0.9503   |
| 0.0032        | 13.63 | 16000 | 0.3703          | 0.8483    | 0.8614 | 0.8548 | 0.9507   |
| 0.003         | 14.48 | 17000 | 0.3720          | 0.8513    | 0.8671 | 0.8591 | 0.9509   |


### Framework versions

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