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<!-- Provide a longer summary of what this model is. -->
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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### Direct Use
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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license: creativeml-openrail-m
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language:
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# MyanmarGPT-Big
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Burmese Text with 1.42 Billion Parameters
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Everyone can fine-tune this model.
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Designed primarily for Burmese text completion, this model serves as a foundational framework for fine-tuning various natural language processing tasks specific to the Burmese language context.
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## About the project
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> Everyone has the right to create AI in Myanmar.
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As Myanmar embarks on its journey towards AI democratization, a strategic and collaborative approach is crucial. Addressing challenges and seizing opportunities in sectors such as agriculture, healthcare, and education can position Myanmar as a regional leader in harnessing the benefits of AI for the betterment of its people and the advancement of its economy. With careful planning and investment, Myanmar has the potential to create a vibrant AI ecosystem that empowers individuals, businesses, and the nation as a whole.
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## MyanmarGPT
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There is already MyanmarGPT which 125 M parameters. But people in Myanmar has been asking me for More precise model with more weights,
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Here is the MyanmarGPT-Big Model now.
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You can use the [MyanmarGPT model 125 M](https://huggingface.co/jojo-ai-mst/MyanmarGPT) which is lightweight and free to use.
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### Model Description
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- **Developed by:** [Min Si Thu](https://www.linkedin.com/in/min-si-thu/)
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- **Model type:** [GPT2]
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- **Language(s) (NLP):** MultiLanguage, But especially Burmese Language
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- **License:** CreativeML-OpenRail-M
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- **Finetuned from model [optional]:** [mGPT]
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## Applied Uses
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MyanmarGPT-Big can be used for the following use cases.
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Text generation, Chatbots and Virtual Assistants, Content Summarization, Translations, Question-Answering System, and Sentiment Analysis.
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### Direct Use
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Originally crafted for text completion in Burmese, this model functions as a fundamental asset for various Natural Language Processing (NLP) tasks. Although its primary role is presently centered on aiding in text generation and completion, it harbors considerable potential for broader applications. Researchers and developers have the option to refine this model using specialized datasets, thereby expanding its utility to other NLP domains, including summarization and instruction-based tasks. Nevertheless, it is crucial to acknowledge that when dealing with high-stakes decisions or comprehending domain-specific terminology, additional specialized training for the model is advised to ensure optimal accuracy and reliability.
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### Out-of-Scope Use
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Users need to recognize the inherent limitations and biases present in language models.
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Responsible usage is crucial, particularly in sensitive contexts, as this model is not designed to generate misleading or harmful content.
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## Bias, Risks, and Limitations
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While the MyanmarGPT-Big excels in handling general Burmese text, its effectiveness might be limited when dealing with daily-life spoken burmese words.
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Users are encouraged to perform comprehensive testing tailored to their specific use cases.
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## mGPT
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Special thanks to [mGPT](https://huggingface.co/ai-forever/mGPT) Project by [ai-forever](https://huggingface.co/ai-forever).
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Without mGPT, MyanmarGPT-Big would have taken a long time to move on from building from scratch.
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## Contact
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Reach me via
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- LinkedIn - [Min Si Thu](https://www.linkedin.com/in/min-si-thu/)
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- GitHub - [Min Si Thu](http://github.com/MinSiThu)
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- Medium - [Min Si Thu](https://medium.com/@minsithu_53495)
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- Hashnode - [Min Si Thu](https://hashnode.com/@MinSiThu)
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