---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fine_tuned_XLMROBERTA_cs_wikann
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: default
      split: validation
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.920336
    - name: Recall
      type: recall
      value: 0.934218
    - name: F1
      type: f1
      value: 0.927225	
    - name: Accuracy
      type: accuracy
      value: 0.973202
---

<!-- 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. -->

# fine_tuned_XLMROBERTA_cs_wikann

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on a czech wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1543
- Precision: 0.9203
- Recall: 0.9342
- F1: 0.9272
- Accuracy: 0.9732

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.51          | 0.27  | 500  | 0.1995          | 0.7873    | 0.8274 | 0.8069 | 0.9435   |
| 0.2164        | 0.53  | 1000 | 0.2216          | 0.7743    | 0.8430 | 0.8072 | 0.9407   |
| 0.1963        | 0.8   | 1500 | 0.1673          | 0.8465    | 0.8849 | 0.8653 | 0.9534   |
| 0.1478        | 1.07  | 2000 | 0.1612          | 0.8850    | 0.9    | 0.8925 | 0.9629   |
| 0.1316        | 1.33  | 2500 | 0.1508          | 0.8765    | 0.9081 | 0.8920 | 0.9615   |
| 0.1156        | 1.6   | 3000 | 0.1561          | 0.9028    | 0.9081 | 0.9054 | 0.9656   |
| 0.1069        | 1.87  | 3500 | 0.1544          | 0.9009    | 0.9091 | 0.9050 | 0.9651   |
| 0.0925        | 2.13  | 4000 | 0.1724          | 0.9008    | 0.9216 | 0.9111 | 0.9662   |
| 0.0791        | 2.4   | 4500 | 0.1385          | 0.9096    | 0.9201 | 0.9148 | 0.9705   |
| 0.0739        | 2.67  | 5000 | 0.1309          | 0.9130    | 0.9254 | 0.9192 | 0.9701   |
| 0.0732        | 2.93  | 5500 | 0.1593          | 0.9035    | 0.9190 | 0.9112 | 0.9679   |
| 0.0538        | 3.2   | 6000 | 0.1550          | 0.9193    | 0.9309 | 0.9251 | 0.9722   |
| 0.0529        | 3.47  | 6500 | 0.1451          | 0.9112    | 0.9330 | 0.9220 | 0.9710   |
| 0.0521        | 3.73  | 7000 | 0.1510          | 0.9185    | 0.9323 | 0.9253 | 0.9721   |
| 0.0526        | 4.0   | 7500 | 0.1378          | 0.9173    | 0.9325 | 0.9249 | 0.9727   |
| 0.0377        | 4.27  | 8000 | 0.1501          | 0.9164    | 0.9344 | 0.9253 | 0.9728   |
| 0.0382        | 4.53  | 8500 | 0.1541          | 0.9213    | 0.9352 | 0.9282 | 0.9729   |
| 0.0358        | 4.8   | 9000 | 0.1543          | 0.9203    | 0.9342 | 0.9272 | 0.9732   |


### Framework versions

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