huseinzol05/electra-base-discriminator-bahasa-cased

The Electra-base-discriminator-bahasa-cased model was pretrained on ~1.8 Billion words . We trained on both standard and social media language structures . The model was trained using Google ELECTRA's Github repository on a single TESLA V100 32GB VRAM . You can use this model by installing torch or tensorflow and Huggingface library transformers . And you can use it directly by initializing it like this: Initializing it directly using ElectraTokenizer, AutoModelWithLMHead, pipeline . The output is, 'kerajaan sangat prihatin terhadap rakyat' and 'raky,