mirror of https://github.com/tracel-ai/burn.git
04d7ff24f2
* initial commit to try implement from_dataframes for a burn dataset * added the beginnings of tests. removed ref to self in utility method * added unit test for dataframe module. added utility methods to convert polars rows to burn dataset values * putting polars and dataframe mod behind a fearure flag * testing both methods * added a if let OK so that it doesn't panic. if we can't convert serde map to json string. added comments * using polars serializer, renaming vars * removed prints. just unwrapping * setting feature flags back * return Value::Null rather than panic if we can't serialize list value. no longer convert to object before converting to string. no longer using serde_json to_string method * Use native deserializer instead of serde_json * added support for lazyframes. added support to deserialize a few more data. added a few more tests * Remove lazy, add more testing and other fixes * Update the book * Remove lazy feature * Put back lazy feature for polars --------- Co-authored-by: Dilshod Tadjibaev <939125+antimora@users.noreply.github.com> |
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| README.md | ||
README.md
Burn Dataset
Burn dataset library
The Burn Dataset library is designed to streamline your machine learning (ML) data pipeline creation process. It offers a variety of dataset implementations, transformation functions, and data sources.
Feature Flags
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audio- enables audio dataset (SpeechCommandsDataset). Run the following example to try it out:cargo run --example speech_commands --features audio