---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: fine_tuned_XLMROBERTA_cs_wikann
  results: []
---

<!-- 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 [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1216
- Overall Precision: 0.8919
- Overall Recall: 0.9190
- Overall F1: 0.9053
- Overall Accuracy: 0.9672

## 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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3409        | 0.4   | 500  | 0.1931          | 0.7764            | 0.8465         | 0.8100     | 0.9495           |
| 0.1816        | 0.8   | 1000 | 0.1427          | 0.8405            | 0.8793         | 0.8595     | 0.9576           |
| 0.1401        | 1.2   | 1500 | 0.1273          | 0.8758            | 0.9068         | 0.8910     | 0.9651           |
| 0.1088        | 1.6   | 2000 | 0.1392          | 0.8868            | 0.9139         | 0.9001     | 0.9662           |
| 0.1027        | 2.0   | 2500 | 0.1096          | 0.8929            | 0.9233         | 0.9078     | 0.9699           |
| 0.0667        | 2.4   | 3000 | 0.1267          | 0.9030            | 0.9268         | 0.9148     | 0.9699           |
| 0.0601        | 2.8   | 3500 | 0.1203          | 0.9078            | 0.9326         | 0.9200     | 0.9712           |


### Framework versions

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