1.2.0

Release Notes

1. Adds support for dataset filtering (#460)(@AbhinavTuli)
2. Greatly improves to_tensorflow performance (#481) (@AbhinavTuli)
3. Benchmarks added for Hub 1.x (#486) (@benchislett)
4. Fixes a bug that caused issues on windows machines (#472)(@FayazRahman)
5. Fixes a bug that caused issues with TF 2.4.0 (#478) (@DebadityaPal)
6. Fixes docker build issue (#463) (@Darkborderman)
7. Added Chinese readme (#458) (@EYH0602)
8. Better automatic determination of Dataset mode depending on permissions (#466)(@edogrigqv2)
9. CoLA dataset uploaded to Hub, upload script added to examples (#487)(@mynameisvinn)
10. Fixes a bug with dataset slicing (#480) (@AbhinavTuli)
11. Adds support for custom s3 endpoints (including MinIO) (#482) (@AbhinavTuli)
12. Adds the ability to set a name to a dataset so it appears better on the visualizer (#468) (@AbhinavTuli)

1.1.3

Fixes an issue in to_pytorch when using a dataset that the user doesn't own.

1.1.0

Release Notes

• Custom s3storage with 5-10x faster than S3FS
• Faster pytorch dataset with current chunk logic
• Fixed caching with in-memory per process without LMDB
• Better Exception handling for loading a dataset, shape and type checks, casting
• Added examples, tutorials, and better GitHub issue handling
• Add the opportunity to fill in additional information about the dataset such as description, license, citation
• Native support with .compute() in the middle for nested tensors

Contributors include. @edogrigqv2 @AbhinavTuli @mynameisvinn @Anselmoo @sparkingdark @sanchitvj @Atom-101

Release v1.0.7

Private dataset support
Improved error handling and exceptions
Test coverage reached 73%->80%
Various bug fixes
Transform speedup ~2x, hence from_x convertors work faster

version 1.0.6

Fixes some issues with segmentation and RAM issues in transform

Version 1.0.6

Fixes some issues with segmentation and RAM issues in transform