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allenai avatar

allenai/natural-instructions

0
View on GitHub↗
1,047 stars·199 forks·Python·Apache-2.0·19 viewsinstructions.apps.allenai.org↗

Natural Instructions

Expanding natural instructions

Features

  • Evaluation and Benchmark Datasets - Benchmark containing over 1,600 diverse tasks for model evaluation.

Star history

Star history chart for allenai/natural-instructionsStar history chart for allenai/natural-instructions

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does allenai/natural-instructions do?

Expanding natural instructions

What are the main features of allenai/natural-instructions?

The main features of allenai/natural-instructions are: Evaluation and Benchmark Datasets.

Which projects share features with allenai/natural-instructions?

Projects with overlapping indexed features include: hexiangnan/neural_collaborative_filtering — Neural collaborative filtering is a recommendation system framework that predicts user item preferences from implicit… google-research/flan — Original Flan (2021) | The Flan Collection (2022) | Flan 2021 Citation | License.

Projects sharing features with Natural Instructions

These projects share indexed features with Natural Instructions. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • hexiangnan/neural_collaborative_filteringhexiangnan avatar

    hexiangnan/neural_collaborative_filtering

    1,885View on GitHub↗

    Neural collaborative filtering is a recommendation system framework that predicts user item preferences from implicit feedback by combining generalized matrix factorization and multi-layer perceptron networks through a shared final embedding layer. It captures both linear and non-linear interactions to model user preferences from historical data. The framework executes training and evaluation runs through a configuration-driven pipeline accessible via command-line interfaces, parsing hyperparameters such as learning rates, batch sizes, and latent dimensions. It optimizes implicit feedback mod

    Pythoncollaborative-filteringdeep-learningrecommender-system
    View on GitHub↗1,885
  • google-research/flangoogle-research avatar

    google-research/FLAN

    1,566View on GitHub↗

    Original Flan (2021) | The Flan Collection (2022) | Flan 2021 Citation | License

    Python
    View on GitHub↗1,566