Note : This datasets and the dataset card are works in progress. You can help contribute to the dataset here and join the community discussions in Discord! This is a collaborative, community-driven project that expands upon the FineWeb2 dataset. Our goal is to create high-quality educational content annotations across hundreds of languages. By enhancing web content with these annotations, we aim to improve the development of Large Language Models LLMs in all languages, making AI technology more accessible and effective globally. The annotations in this dataset will help train AI systems to automatically identify high-quality educational content in more languages and in turn help build better Large Language Models for all languages. Once a language reaches 1, annotations, the dataset will be included in this dataset! Alongside rating the educational quality of the content, different language communities are discussing other ways to improve the quality of data for their language in our Discord discussion channel. So far members of the Hugging Face community have submitted 48, annotations. The following languages have reached the 1, annotation threshold to be included in the dataset. We'll keep updating this dataset as more annotations are added! You can help contribute to the dataset here. There are many languages in the world where no high quality LLMs exist. Having high quality data is a central part of building high quality LLMs. FineWeb2 is a crucial step in improving the availability of high quality data for many languages. We plan to go a step further. FineWeb-Edu is a dataset built on the original FineWeb dataset. The dataset was constructed by developing an educational quality classifier using annotations generated by LLamaB-Instruct and using this classifier to retain only the most educational web pages. FineWeb-Edu outperforms FineWeb on popular benchmark. Crucially, using this approach reduces the amount of data needed to train a high quality LLM reducing the barrier to building a high quality LLM for many languages. We want to make it possible to build FineWeb-Edu datasets for all the worlds languages. To do this we need annotations in order to train an educational quality classifier. This in turn will allow us to build the next generation of Large Language Models for many languages. For high resources languages, using an LLM to generate educational quality annotations can be a good solution. However, for many languages LLMs are not able to generate high quality annotations — or we don't have enough data to validate whether the annotations are correct. You can help by contributing to the dataset here and join the community discussions in Discord! Break the AI language barrier: Most commercial AI companies focus on profitable languages, leaving many communities behind. Your work helps bring AI capabilities to more burgdorf-hannover k9 sextreffen. Keep it open: Unlike proprietary datasets locked away by companies, FineWeb2-C is an open dataset. This means anyone can use it to build AI systems that truly serve their community's needs. Through this open approach we also learn about which approaches work best for different languages. Be part of something bigger: Just burgdorf-hannover k9 sextreffen Wikipedia showed how volunteers can build invaluable resources, the Hugging Face community has created numerous open models and datasets. You're joining a movement to democratize AI technology. Every annotation counts. These are the top 10 contributors to this release of the dataset. Burgdorf-hannover k9 sextreffen sure to give them a follow on the Hub to show your appreciation! Data work is the under appreciated foundation of AI and ML. This dataset is built by the community for the community.
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