README.md

metadata

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 on a dataset of 15K social media posts from Ukraine manually annotated for pro-Ukrainian or pro-Russian point of view on the war. 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)

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

The model was trained in this notebook.

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