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🤗 Datasets and Metrics Library Heading Toward First Non-Beta Release

Led by ML Intern Quentin Lhoest (@qlhoest) and CSO Thomas Wolf (@Thom_Wolf), the 🤗 team has been hard at work on our newest library focusing on datasets and metrics.


  • One line access to 150+ datasets and metrics - very easy to add new datasets/metrics to the hub
  • Loading a 17GB+ dataset like English Wikipedia only takes 9MB in RAM and you can iterate over the data at 2-3 Gbit/s
  • Blazing-fast and reproducible data processing
  • Deep integration with numpy/pandas/pytorch/tensorflow

New this summer:

  • Brand new documentation
  • More tutorials to showcase simplicity of use

Short roadmap for the 1.0.0 release:

  • Deep integration and focus on knowledge-based models such as RAG/REALM/ORQA/MARGE/knn-LM using indexed datasets
  • Additional speed improvements (multiprocessing, instant shuffling)
  • Support for multi-modal datasets
  • Final community-voted name for the library will be "🤗 datasets" (a change from the original non-beta release 1.0.0)
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🔥Top Contributors 🔥

Every newsletter, we'll be highlighting some top contributors to the Hugging Face library! This week's top contributors:

  • Suraj Patil - Added the MBart model and diverse functionalities for Seq2Seq models.
  • Guillaume - Added the "ConversationalPipeline" and optimized "Bad token ids" for generation.
  • Stas Bekman - Fixed many inconsistencies in the documentation and cleaned up some tests.
  • Manuel Romero - Added multiple T5 models to the model hub, notably models for Question Generation.
  • Pradhy729 - Added "Feed forward chunking functionality" and added support for "IterableDataset" in Trainer.  

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