---
license: cc-by-nc-sa-4.0
base_model: ufal/robeczech-base
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC_2_0_ext_robeczech-base
  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: 0.8633093525179856
    - name: Recall
      type: recall
      value: 0.8933002481389578
    - name: F1
      type: f1
      value: 0.8780487804878048
    - name: Accuracy
      type: accuracy
      value: 0.9703429462197973
---

<!-- 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_ext_robeczech-base

This model is a fine-tuned version of [ufal/robeczech-base](https://huggingface.co/ufal/robeczech-base) on the cnec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1663
- Precision: 0.8633
- Recall: 0.8933
- F1: 0.8780
- Accuracy: 0.9703

## 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: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2593        | 4.46  | 1000  | 0.1653          | 0.8195    | 0.8223 | 0.8209 | 0.9593   |
| 0.1209        | 8.93  | 2000  | 0.1355          | 0.8441    | 0.8789 | 0.8612 | 0.9679   |
| 0.0763        | 13.39 | 3000  | 0.1310          | 0.8591    | 0.8893 | 0.8739 | 0.9709   |
| 0.0539        | 17.86 | 4000  | 0.1383          | 0.8656    | 0.8953 | 0.8802 | 0.9719   |
| 0.0403        | 22.32 | 5000  | 0.1392          | 0.8626    | 0.8943 | 0.8782 | 0.9710   |
| 0.0316        | 26.79 | 6000  | 0.1539          | 0.8606    | 0.8948 | 0.8774 | 0.9712   |
| 0.0254        | 31.25 | 7000  | 0.1552          | 0.8660    | 0.8913 | 0.8785 | 0.9706   |
| 0.0211        | 35.71 | 8000  | 0.1621          | 0.8658    | 0.8968 | 0.8810 | 0.9701   |
| 0.0183        | 40.18 | 9000  | 0.1593          | 0.8688    | 0.8973 | 0.8828 | 0.9718   |
| 0.0161        | 44.64 | 10000 | 0.1638          | 0.8653    | 0.8993 | 0.8820 | 0.9714   |
| 0.015         | 49.11 | 11000 | 0.1663          | 0.8633    | 0.8933 | 0.8780 | 0.9703   |


### Framework versions

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