---
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: 0.8093464273620048
    - name: Recall
      type: recall
      value: 0.8547925608011445
    - name: F1
      type: f1
      value: 0.8314489476430683
    - name: Accuracy
      type: accuracy
      value: 0.9446311123820418
---

<!-- 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: 0.3352
- Precision: 0.8093
- Recall: 0.8548
- F1: 0.8314
- Accuracy: 0.9446

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5496        | 2.22  | 500  | 0.2782          | 0.7301    | 0.7750 | 0.7519 | 0.9275   |
| 0.2133        | 4.44  | 1000 | 0.2487          | 0.7811    | 0.8219 | 0.8010 | 0.9399   |
| 0.144         | 6.67  | 1500 | 0.2580          | 0.7737    | 0.8290 | 0.8004 | 0.9396   |
| 0.1029        | 8.89  | 2000 | 0.2576          | 0.7997    | 0.8480 | 0.8231 | 0.9446   |
| 0.0776        | 11.11 | 2500 | 0.2849          | 0.7990    | 0.8516 | 0.8244 | 0.9444   |
| 0.0601        | 13.33 | 3000 | 0.2971          | 0.8021    | 0.8523 | 0.8264 | 0.9450   |
| 0.0494        | 15.56 | 3500 | 0.3077          | 0.8014    | 0.8473 | 0.8237 | 0.9440   |
| 0.0408        | 17.78 | 4000 | 0.3145          | 0.8131    | 0.8555 | 0.8337 | 0.9448   |
| 0.0353        | 20.0  | 4500 | 0.3260          | 0.8097    | 0.8569 | 0.8327 | 0.9445   |
| 0.0311        | 22.22 | 5000 | 0.3356          | 0.8076    | 0.8541 | 0.8302 | 0.9441   |
| 0.0281        | 24.44 | 5500 | 0.3352          | 0.8093    | 0.8548 | 0.8314 | 0.9446   |


### Framework versions

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