Predictive Model Development Using Machine Learning
Budget: $3,000 – $5,000 USD
I'm seeking a talented data scientist to help Unlock Global Communication with Gemma, our team (every one will give his try) is working in ML To participate in this competition, you must create and share a public Kaggle Notebook that demonstrates how to effectively fine-tune Gemma for various languages and/or cultural contexts
You must create your Gemma model variant to others models, and provide steps to run inference with their model. All team members must be listed as collaborators on the notebook, everyone should send me an email with his final work (notebook)
You’re challenged to create notebooks that demonstrate the complete process of adapting Gemma 2, including:
Dataset Creation/Curation: Explain how you crafted or curated the dataset used for fine-tuning. This includes details about data sources, preprocessing steps, and any considerations related to data quality and cultural sensitivity.
Fine-tuning Gemma: Provide a detailed explanation of your fine-tuning approach, including hyperparameter choices, training procedures, and any techniques used to enhance performance (e.g., few-shot prompting, retrieval-augmented generation).
Inference and Evaluation: Demonstrate how to run inference with your fine-tuned model and discuss how you evaluated its performance.
Your notebooks should be designed to be easily understood and replicated by others, enabling them to adapt Gemma 2 for even more languages and cultural contexts. Consider exploring areas like:
Language Fluency: Fine-tune Gemma to generate fluent and accurate text in the target language, potentially for tasks like translation, dialogue generation, or storytelling.
Literary Traditions: Adapt Gemma for generating or analysing poetry, folklore, or other traditional literary forms.
Historical Texts: Fine-tune Gemma to understand and process historical documents or scripts.
Participants will also need to publish their trained models on Kaggle Models.
Ready to contribute to a more inclusive and interconnected world? Join the competition today and help us unlock the potential of language AI for everyone!
deadline:
This competition does not require use of a dataset.
You must create your Gemma model variant to others models, and provide steps to run inference with their model. All team members must be listed as collaborators on the notebook, everyone should send me an email with his final work (notebook)
You’re challenged to create notebooks that demonstrate the complete process of adapting Gemma 2, including:
Dataset Creation/Curation: Explain how you crafted or curated the dataset used for fine-tuning. This includes details about data sources, preprocessing steps, and any considerations related to data quality and cultural sensitivity.
Fine-tuning Gemma: Provide a detailed explanation of your fine-tuning approach, including hyperparameter choices, training procedures, and any techniques used to enhance performance (e.g., few-shot prompting, retrieval-augmented generation).
Inference and Evaluation: Demonstrate how to run inference with your fine-tuned model and discuss how you evaluated its performance.
Your notebooks should be designed to be easily understood and replicated by others, enabling them to adapt Gemma 2 for even more languages and cultural contexts. Consider exploring areas like:
Language Fluency: Fine-tune Gemma to generate fluent and accurate text in the target language, potentially for tasks like translation, dialogue generation, or storytelling.
Literary Traditions: Adapt Gemma for generating or analysing poetry, folklore, or other traditional literary forms.
Historical Texts: Fine-tune Gemma to understand and process historical documents or scripts.
Participants will also need to publish their trained models on Kaggle Models.
Ready to contribute to a more inclusive and interconnected world? Join the competition today and help us unlock the potential of language AI for everyone!
deadline:
This competition does not require use of a dataset.
Related categories:
Data Processing
Machine Learning (ML)
Data Mining
Statistical Analysis
Data Science