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Split elements of input
based on sep
.
tf.compat.v1.strings.split(
input=None,
sep=None,
maxsplit=-1,
result_type='SparseTensor',
source=None,
name=None
)
Let N be the size of input
(typically N will be the batch size). Split each
element of input
based on sep
and return a SparseTensor
or
RaggedTensor
containing the split tokens. Empty tokens are ignored.
Examples:
print(tf.compat.v1.strings.split(['hello world', 'a b c']))
SparseTensor(indices=tf.Tensor( [[0 0] [0 1] [1 0] [1 1] [1 2]], ...),
values=tf.Tensor([b'hello' b'world' b'a' b'b' b'c'], ...),
dense_shape=tf.Tensor([2 3], shape=(2,), dtype=int64))
print(tf.compat.v1.strings.split(['hello world', 'a b c'],
result_type="RaggedTensor"))
<tf.RaggedTensor [[b'hello', b'world'], [b'a', b'b', b'c']]>
If sep
is given, consecutive delimiters are not grouped together and are
deemed to delimit empty strings. For example, input
of "1<>2<><>3"
and
sep
of "<>"
returns ["1", "2", "", "3"]
. If sep
is None or an empty
string, consecutive whitespace are regarded as a single separator, and the
result will contain no empty strings at the start or end if the string has
leading or trailing whitespace.
Note that the above mentioned behavior matches python's str.split.
Raises | |
---|---|
ValueError
|
If sep is not a string. |
Returns | |
---|---|
A SparseTensor or RaggedTensor of rank N+1 , the strings split
according to the delimiter.
|