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The Simbolo's Myanmarsar-GPT symbol is trained on a dataset of 1 million Burmese data and pre-trained using the GPT-2 architecture. Its purpose is to serve as a foundational pre-trained model for the Burmese language, facilitating fine-tuning for specific applications of different tasks such as creative writing, chatbot, machine translation etc.
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 models for their invaluable contribution to this project, significantly impacting the field of Burmese NLP.
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We want to thank everyone who has worked on the related works, especially [Minsithu](https://huggingface.co/jojo-ai-mst/MyanmarGPTT) and [Dr. Wai Yan Nyein Naing](WYNN747/Burmese-GPT, https://huggingface.co/WYNN747/Burmese-GPT)who initiated the work of gpt-2 model.
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And We would like to thank Simbolo:Servico which is a
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### Limitations and bias
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We have yet to investigate the potential bias inherent in this model thoroughly. Regarding transparency, it's important to note that the model is primarily trained on data from the Unicode Burmese(Myanmar) language.
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The Simbolo's Myanmarsar-GPT symbol is trained on a dataset of 1 million Burmese data and pre-trained using the GPT-2 architecture. Its purpose is to serve as a foundational pre-trained model for the Burmese language, facilitating fine-tuning for specific applications of different tasks such as creative writing, chatbot, machine translation etc.
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### How to use
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### Acknowledgment
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We extend our gratitude to the creators of the [mGPT-XL](ai-forever/mGPT) models for their invaluable contribution to this project, significantly impacting the field of Burmese NLP.
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We want to thank everyone who has worked on the related works, especially [Minsithu](https://huggingface.co/jojo-ai-mst/MyanmarGPTT) and [Dr. Wai Yan Nyein Naing](WYNN747/Burmese-GPT, https://huggingface.co/WYNN747/Burmese-GPT)who initiated the work of gpt-2 model.
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And We would like to thank Simbolo:Servico which is a branch of Simbolo under the company of Intello Tech for providing financial support.
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### Limitations and bias
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We have yet to investigate the potential bias inherent in this model thoroughly. Regarding transparency, it's important to note that the model is primarily trained on data from the Unicode Burmese(Myanmar) language.
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