---
base_model: UWB-AIR/Czert-B-base-cased
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC_2_0_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: 0.8108016304347826
    - name: Recall
      type: recall
      value: 0.8537195994277539
    - name: F1
      type: f1
      value: 0.8317073170731707
    - name: Accuracy
      type: accuracy
      value: 0.9456677151844438
---

<!-- 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_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: 0.2818
- Precision: 0.8108
- Recall: 0.8537
- F1: 0.8317
- Accuracy: 0.9457

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5129        | 2.22  | 500  | 0.2615          | 0.7602    | 0.7858 | 0.7728 | 0.9315   |
| 0.1863        | 4.44  | 1000 | 0.2460          | 0.7845    | 0.8255 | 0.8045 | 0.9403   |
| 0.1221        | 6.67  | 1500 | 0.2474          | 0.7969    | 0.8380 | 0.8169 | 0.9441   |
| 0.0857        | 8.89  | 2000 | 0.2663          | 0.8028    | 0.8491 | 0.8253 | 0.9435   |
| 0.0645        | 11.11 | 2500 | 0.2814          | 0.8081    | 0.8480 | 0.8276 | 0.9441   |
| 0.052         | 13.33 | 3000 | 0.2818          | 0.8108    | 0.8537 | 0.8317 | 0.9457   |


### Framework versions

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