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Add break utility for long nowrap items (e.g. code)
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@ -112,6 +112,10 @@
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.u-nowrap
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white-space: nowrap
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.u-break.u-break
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word-wrap: break-word
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white-space: initial
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.u-no-border
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border: none
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@ -216,7 +216,7 @@ p
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+footrow
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+cell returns
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+cell #[code numpy.ndarray[ndim=2, dtype='int32']]
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+cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
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+cell
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| The exported attributes as a 2D numpy array, with one row per
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| token and one column per attribute.
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@ -245,7 +245,7 @@ p
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+row
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+cell #[code array]
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+cell #[code numpy.ndarray[ndim=2, dtype='int32']]
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+cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
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+cell The attribute values to load.
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+footrow
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@ -509,7 +509,7 @@ p
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell A 1D numpy array representing the document's semantics.
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+h(2, "vector_norm") Doc.vector_norm
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@ -129,7 +129,7 @@ p A real-valued meaning representation.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell A 1D numpy array representing the lexeme's semantics.
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+h(2, "vector_norm") Lexeme.vector_norm
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@ -37,7 +37,7 @@ p Create a Span object from the #[code slice doc[start : end]].
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+row
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+cell #[code vector]
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+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell A meaning representation of the span.
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+footrow
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@ -270,7 +270,7 @@ p
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell A 1D numpy array representing the span's semantics.
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+h(2, "vector_norm") Span.vector_norm
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@ -250,7 +250,7 @@ p A real-valued meaning representation.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell A 1D numpy array representing the token's semantics.
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+h(2, "vector_norm") Span.vector_norm
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