Sentiment Analysis for Tigrinya with TiRoBERTa
This model is a fine-tuned version of TiRoBERTa on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).
Basic usage
from transformers import pipeline
ti_sent = pipeline("sentiment-analysis", model="fgaim/tiroberta-sentiment")
ti_sent("ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር")
Training
Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Results
It achieves the following results on the evaluation set:
- F1: 0.8477
- Precision: 0.7607
- Recall: 0.957
- Accuracy: 0.828
- Loss: 0.6796
Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1
Citation
If you use this model in your product or research, please cite as follows:
@article{Fitsum2021TiPLMs,
author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
title={Monolingual Pre-trained Language Models for Tigrinya},
year=2021,
publisher={WiNLP 2021/EMNLP 2021}
}
References
Tela, A., Woubie, A. and Hautamäki, V. 2020.
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya.
ArXiv, abs/2006.07698.
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Evaluation results
- Accuracyself-reported0.828
- F1self-reported0.848
- Precisionself-reported0.761
- Recallself-reported0.957