finetuning-pysentimiento-war-tweets
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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