---
base_model: UWB-AIR/Czert-B-base-cased
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC_1_1_ext_Czert-B-base-cased
  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: 0.8383838383838383
    - name: Recall
      type: recall
      value: 0.8872260823089257
    - name: F1
      type: f1
      value: 0.8621137366917683
    - name: Accuracy
      type: accuracy
      value: 0.9569787813899163
---

<!-- 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_1_1_ext_Czert-B-base-cased

This model is a fine-tuned version of [UWB-AIR/Czert-B-base-cased](https://huggingface.co/UWB-AIR/Czert-B-base-cased) on the cnec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2513
- Precision: 0.8384
- Recall: 0.8872
- F1: 0.8621
- Accuracy: 0.9570

## 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: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3012        | 3.42  | 500  | 0.1677          | 0.8115    | 0.8626 | 0.8363 | 0.9518   |
| 0.1081        | 6.85  | 1000 | 0.1869          | 0.8218    | 0.8749 | 0.8475 | 0.9548   |
| 0.0654        | 10.27 | 1500 | 0.2132          | 0.8311    | 0.8813 | 0.8555 | 0.9559   |
| 0.0449        | 13.7  | 2000 | 0.2284          | 0.8296    | 0.8797 | 0.8540 | 0.9559   |
| 0.0341        | 17.12 | 2500 | 0.2353          | 0.8348    | 0.8856 | 0.8594 | 0.9575   |
| 0.0267        | 20.55 | 3000 | 0.2413          | 0.8397    | 0.8872 | 0.8628 | 0.9581   |
| 0.0227        | 23.97 | 3500 | 0.2513          | 0.8384    | 0.8872 | 0.8621 | 0.9570   |


### Framework versions

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