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This model is a fine-tuned version of finiteautomata/beto-sentiment-analysis on a dataset of 1500 tweets from Peruvian accounts. It achieves the following results on the evaluation set:

  • Loss: 1.7689
  • Accuracy: 0.7378
  • F1: 0.7456

Model description

This model in a fine-tuned version of finiteautomata/beto-sentiment-analysis using five labels: pro_russia, against_ukraine, neutral, against_russia, pro_ukraine.

Intended uses & limitations

This model shall be used to classify text (more specifically, Spanish tweets) as expressing a position concerning the Russo-Ukrainian war.

Training and evaluation data

We used an 80/20 training/test split on the aforementioned dataset.

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: 30

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.11.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1
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