miniF2F VS tensor_annotations

Compare miniF2F vs tensor_annotations and see what are their differences.


Formal to Formal Mathematics Benchmark (by openai)


Annotating tensor shapes using Python types (by google-deepmind)
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miniF2F tensor_annotations
4 2
258 158
3.5% -0.6%
0.0 5.8
9 months ago 11 months ago
Objective-C++ Python
- Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of miniF2F. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-11.


Posts with mentions or reviews of tensor_annotations. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-11.

What are some alternatives?

When comparing miniF2F and tensor_annotations you can also consider the following projects:

einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

dex-lang - Research language for array processing in the Haskell/ML family

torchtyping - Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.

FL - FL language specification and reference implementations

TablaM - The practical relational programing language for data-oriented applications

tiny-cuda-nn - Lightning fast C++/CUDA neural network framework

jaxtyping - Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays.

MindsDB - The platform for customizing AI from enterprise data

hasktorch - Tensors and neural networks in Haskell