---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ukraine-war-pov
  results: []
widget:
- text: 'Росія знову скоює воєнні злочини'
  example_title: 'proukrainian'
- text: 'ВСУ все берет с собой — украинские «захистники» взяли стульчак из Артемовска'
  example_title: 'prorussian'
---

<!-- 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. -->

# ukraine-war-pov

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2166
- Accuracy: 0.9315
- F1: 0.9315
- Precision: 0.9315
- Recall: 0.9315
- AUC: 0.9774 (self-report)

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.284         | 1.0   | 1875  | 0.1850          | 0.9295   | 0.9295 | 0.9303    | 0.9295 |
| 0.2271        | 2.0   | 3750  | 0.1551          | 0.9405   | 0.9405 | 0.9414    | 0.9405 |
| 0.2064        | 3.0   | 5625  | 0.1734          | 0.9305   | 0.9305 | 0.9311    | 0.9305 |
| 0.1842        | 4.0   | 7500  | 0.1694          | 0.9315   | 0.9315 | 0.9317    | 0.9315 |
| 0.1628        | 5.0   | 9375  | 0.1838          | 0.9435   | 0.9435 | 0.9438    | 0.9435 |
| 0.1309        | 6.0   | 11250 | 0.2074          | 0.9395   | 0.9395 | 0.9395    | 0.9395 |
| 0.1017        | 7.0   | 13125 | 0.2659          | 0.9365   | 0.9365 | 0.9365    | 0.9365 |
| 0.0778        | 8.0   | 15000 | 0.2851          | 0.94     | 0.9400 | 0.9400    | 0.94   |
| 0.0664        | 9.0   | 16875 | 0.3238          | 0.9385   | 0.9385 | 0.9387    | 0.9385 |
| 0.066         | 10.0  | 18750 | 0.3092          | 0.939    | 0.9390 | 0.9390    | 0.9390 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3