---
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_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: 0.8261421319796954
    - name: Recall
      type: recall
      value: 0.8622516556291391
    - name: F1
      type: f1
      value: 0.8438107582631237
    - name: Accuracy
      type: accuracy
      value: 0.9410182516810759
---

<!-- 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_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: 0.3330
- Precision: 0.8261
- Recall: 0.8623
- F1: 0.8438
- Accuracy: 0.9410

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5787        | 1.7   | 500  | 0.3008          | 0.7659    | 0.7943 | 0.7798 | 0.9262   |
| 0.2266        | 3.4   | 1000 | 0.2606          | 0.8026    | 0.8437 | 0.8226 | 0.9374   |
| 0.1443        | 5.1   | 1500 | 0.2565          | 0.8189    | 0.8525 | 0.8354 | 0.9407   |
| 0.1004        | 6.8   | 2000 | 0.2807          | 0.8129    | 0.8539 | 0.8329 | 0.9400   |
| 0.0759        | 8.5   | 2500 | 0.2989          | 0.8255    | 0.8627 | 0.8437 | 0.9411   |
| 0.0563        | 10.2  | 3000 | 0.3181          | 0.8251    | 0.8578 | 0.8411 | 0.9402   |
| 0.0475        | 11.9  | 3500 | 0.3279          | 0.8204    | 0.8609 | 0.8402 | 0.9404   |
| 0.0378        | 13.61 | 4000 | 0.3330          | 0.8261    | 0.8623 | 0.8438 | 0.9410   |


### Framework versions

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