Create README.md

README.md

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+---
+language:
+- tl
+tags:
+- RoBERTa
+- Tagalog
+- Taglish
+---
+
+## Model Description
+
+
+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.
+This  RoBERTa model was used in the original paper for sentiment analysis encompassing three tags (positive, negative, neutral) for code-switched Tagalog.
+
+
+
+## Training Details
+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/
+
+### Training Data
+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
+
+
+## Citation
+Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language
+
+
+Bibtex:
+```
+@inproceedings{visperas-etal-2023-itanong,
+    title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages",
+    author = "Visperas, Moses L.  and
+      Borjal, Christalline Joie  and
+      Adoptante, Aunhel John M  and
+      Abacial, Danielle Shine R.  and
+      Decano, Ma. Miciella  and
+      Peramo, Elmer C",
+    editor = "Abbas, Mourad  and
+      Freihat, Abed Alhakim",
+    booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)",
+    month = dec,
+    year = "2023",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.icnlsp-1.34",
+    pages = "316--323",
+}
+```
+
+