From 6548e0e282aeeda7bfb18beafbc65ebecd780c63 Mon Sep 17 00:00:00 2001 From: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com> Date: Wed, 5 Jun 2024 14:43:26 +0200 Subject: [PATCH] Fix typos in docs (#6957) * Fix typo in comparisions * Fix two consecutive sentences ending in colon * Split into two sentences --- docs/source/use_with_jax.mdx | 3 ++- docs/source/use_with_pytorch.mdx | 3 ++- docs/source/use_with_tensorflow.mdx | 6 ++++-- 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/docs/source/use_with_jax.mdx b/docs/source/use_with_jax.mdx index 7b48549dfde..7c601e7be37 100644 --- a/docs/source/use_with_jax.mdx +++ b/docs/source/use_with_jax.mdx @@ -100,7 +100,8 @@ If your dataset consists of N-dimensional arrays, you will see that by default t {'data': [Array([1, 2], dtype=int32), Array([3], dtype=int32)]} ``` -However this logic often requires slow shape comparisions and data copies, to avoid this you must explicitly use the [`Array`] feature type and specify the shape of your tensors: +However this logic often requires slow shape comparisons and data copies. +To avoid this, you must explicitly use the [`Array`] feature type and specify the shape of your tensors: ```py >>> from datasets import Dataset, Features, Array2D diff --git a/docs/source/use_with_pytorch.mdx b/docs/source/use_with_pytorch.mdx index c3603d019b9..dae29557694 100644 --- a/docs/source/use_with_pytorch.mdx +++ b/docs/source/use_with_pytorch.mdx @@ -61,7 +61,8 @@ If your dataset consists of N-dimensional arrays, you will see that by default t {'data': [tensor([1, 2]), tensor([3])]} ``` -However this logic often requires slow shape comparisions and data copies, to avoid this you must explicitly use the [`Array`] feature type and specify the shape of your tensors: +However this logic often requires slow shape comparisons and data copies. +To avoid this, you must explicitly use the [`Array`] feature type and specify the shape of your tensors: ```py >>> from datasets import Dataset, Features, Array2D diff --git a/docs/source/use_with_tensorflow.mdx b/docs/source/use_with_tensorflow.mdx index 6e6b10bd999..ef16945024b 100644 --- a/docs/source/use_with_tensorflow.mdx +++ b/docs/source/use_with_tensorflow.mdx @@ -42,7 +42,6 @@ array([[1, 2], ## N-dimensional arrays If your dataset consists of N-dimensional arrays, you will see that by default they are considered as the same tensor if the shape is fixed: -Otherwise, a TensorFlow formatted dataset outputs a `RaggedTensor` instead of a single tensor: ```py >>> from datasets import Dataset @@ -55,6 +54,8 @@ Otherwise, a TensorFlow formatted dataset outputs a `RaggedTensor` instead of a [3, 4]])>} ``` +Otherwise, a TensorFlow formatted dataset outputs a `RaggedTensor` instead of a single tensor: + ```py >>> from datasets import Dataset >>> data = [[[1, 2],[3]],[[4, 5, 6],[7, 8]]] # varying shape @@ -64,7 +65,8 @@ Otherwise, a TensorFlow formatted dataset outputs a `RaggedTensor` instead of a {'data': } ``` -However this logic often requires slow shape comparisions and data copies, to avoid this you must explicitly use the [`Array`] feature type and specify the shape of your tensors: +However this logic often requires slow shape comparisons and data copies. +To avoid this, you must explicitly use the [`Array`] feature type and specify the shape of your tensors: ```py >>> from datasets import Dataset, Features, Array2D