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---
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
language:
- uk
- ru
---
# ukraine-war-pov
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a dataset of 30K social media posts (a balanced set of 15K for each label) from Ukraine manually annotated for pro-Ukrainian or pro-Russian point of view on the war after the 2022 invasion.
It achieves the following results on a balanced test set (2K):
- Loss: 0.2166
- Accuracy: 0.9315
- F1: 0.9315
- Precision: 0.9315
- Recall: 0.9315
- AUC: 0.9774 (self-report)
## Training and evaluation data
The training and evaluation data was compiled and labeled by the Center for Content Analysis in Ukraine: Artem Zakharchenko and his team, including Yevhen Luzan, Olena Zakharchenko, Olexiy Rogalyov, Olena Zinenko, Yuliia Maksymtsova, Maryna Fursenko, Valeriia Molotsiian, and Anhelika Machula.
## Training procedure
The model was trained in this [notebook](https://drive.google.com/file/d/1RnT3fJTneFSczS_G_JLVqe4MydkTFiO0/view?usp=sharing).
### 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 |