Edit model card

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")



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


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


If you use this model in your product or research, please cite as follows:

  author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
  title={Monolingual Pre-trained Language Models for Tigrinya},
  publisher={WiNLP 2021/EMNLP 2021}


Tela, A., Woubie, A. and Hautamäki, V. 2020.
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya.
ArXiv, abs/2006.07698.
Downloads last month
Hosted inference API
Text Classification
This model can be loaded on the Inference API on-demand.