---
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: 0.1238
- Overall Precision: 0.8962
- Overall Recall: 0.9193
- Overall F1: 0.9076
- Overall Accuracy: 0.9684

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3625        | 0.4   | 500  | 0.1809          | 0.7915            | 0.8509         | 0.8201     | 0.9486           |
| 0.1807        | 0.8   | 1000 | 0.1373          | 0.8363            | 0.8785         | 0.8568     | 0.9583           |
| 0.1384        | 1.2   | 1500 | 0.1371          | 0.8758            | 0.9085         | 0.8918     | 0.9651           |
| 0.1079        | 1.6   | 2000 | 0.1467          | 0.8924            | 0.9168         | 0.9044     | 0.9659           |
| 0.0997        | 2.0   | 2500 | 0.1170          | 0.9018            | 0.9264         | 0.9139     | 0.9700           |
| 0.0644        | 2.4   | 3000 | 0.1344          | 0.9123            | 0.9285         | 0.9203     | 0.9706           |
| 0.0594        | 2.8   | 3500 | 0.1269          | 0.9138            | 0.9345         | 0.9240     | 0.9718           |


### Framework versions

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