Let N be the size of source (typically N will be the batch size). Split each
element of source based on delimiter and return a SparseTensor
or RaggedTensor containing the split tokens. Empty tokens are ignored.
If sep is an empty string, each element of the source is split
into individual strings, each containing one byte. (This includes splitting
multibyte sequences of UTF-8.) If delimiter contains multiple bytes, it is
treated as a set of delimiters with each considered a potential split point.
Examples:
print(tf.compat.v1.string_split(['hello world','a b c']))SparseTensor(indices=tf.Tensor([[00][01][10][11][12]],...),values=tf.Tensor([b'hello'b'world'b'a'b'b'b'c'],...),dense_shape=tf.Tensor([23],shape=(2,),dtype=int64))
print(tf.compat.v1.string_split(['hello world','a b c'],result_type="RaggedTensor"))<tf.RaggedTensor[[b'hello',b'world'],[b'a',b'b',b'c']]>
Args
source
1-D string Tensor, the strings to split.
sep
0-D string Tensor, the delimiter character, the string should
be length 0 or 1. Default is ' '.
skip_empty
A bool. If True, skip the empty strings from the result.
delimiter
deprecated alias for sep.
result_type
The tensor type for the result: one of "RaggedTensor" or
"SparseTensor".
name
A name for the operation (optional).
Raises
ValueError
If delimiter is not a string.
Returns
A SparseTensor or RaggedTensor of rank 2, the strings split according
to the delimiter. The first column of the indices corresponds to the row
in source and the second column corresponds to the index of the split
component in this row.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]