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language: ms |
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# t5-base-bahasa-cased |
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Pretrained T5 base on standard and local language model for Malay. |
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## Pretraining Corpus |
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`t5-base-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on, |
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1. Language masking task on bahasa news, bahasa Wikipedia, bahasa Academia.edu, bahasa parliament and translated The Pile. |
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2. News title prediction on bahasa news. |
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3. Next sentence prediction on bahasa news, bahasa Wikipedia, bahasa Academia.edu, bahasa parliament and translated The Pile. |
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4. Translated QA Natural. |
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5. Text Similarity task on translated SNLI and translated MNLI. |
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6. EN-MS translation. |
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7. MS-EN translation. |
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8. Abstractive Summarization. |
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9. Knowledge Graph triples generation. |
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10. Paraphrase. |
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11. Social media normalization. |
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12. Noisy EN-MS translation. |
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13. Noisy MS-EN translation. |
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Preparing steps can reproduce at https://github.com/huseinzol05/malaya/tree/master/pretrained-model/t5/prepare |
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## Pretraining details |
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- This model was trained using Google T5 repository https://github.com/google-research/text-to-text-transfer-transformer, on v3-8 TPU. |
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- All steps can reproduce from here, https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/t5 |
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## Supported prefix |
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1. `soalan: {string}`, trained using Natural QA. |
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2. `ringkasan: {string}`, for abstractive summarization. |
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3. `tajuk: {string}`, for abstractive title. |
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4. `parafrasa: {string}`, for abstractive paraphrase. |
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5. `terjemah Inggeris ke Melayu: {string}`, for EN-MS translation. |
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6. `terjemah Melayu ke Inggeris: {string}`, for MS-EN translation. |
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7. `grafik pengetahuan: {string}`, for MS text to EN Knowledge Graph triples format. |
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8. `ayat1: {string1} ayat2: {string2}`, semantic similarity. |
