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--- |
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language: |
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- tl |
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tags: |
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- Tagalog |
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- Taglish |
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--- |
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## Model Description |
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As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced our initial pre-trained language models for Philippine languages. Our model suite encompasses various BERT-based, GPT-based, and Sentence Transformers tailored for Tagalog,Taglish and Cebuano. |
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## Training Details |
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This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/ |
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### Training Data |
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The training dataset was compiled from both formal and informal sources, consisting of 5,159,917 instances from formal channels and 3,057,180 from informal sources. More information on pre-processing and training parameters on our paper |
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## Citation |
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Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language |
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Bibtex: |
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``` |
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@inproceedings{visperas-etal-2023-itanong, |
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title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages", |
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author = "Visperas, Moses L. and |
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Borjal, Christalline Joie and |
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Adoptante, Aunhel John M and |
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Abacial, Danielle Shine R. and |
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Decano, Ma. Miciella and |
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Peramo, Elmer C", |
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editor = "Abbas, Mourad and |
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Freihat, Abed Alhakim", |
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booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)", |
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month = dec, |
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year = "2023", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.icnlsp-1.34", |
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pages = "316--323", |
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} |
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``` |
