mindspore.dataset.text.Truncate
- class mindspore.dataset.text.Truncate(max_seq_len)[source]
Truncate the input sequence so that it does not exceed the maximum length.
- Parameters
max_seq_len (int) – Maximum allowable length.
- Raises
TypeError – If max_length_len is not of type int.
ValueError – If value of max_length_len is not greater than or equal to 0.
RuntimeError – If the input tensor is not of dtype bool, int, float, double or str.
- Supported Platforms:
CPU
Examples
>>> import mindspore.dataset as ds >>> import mindspore.dataset.text as text >>> >>> # Use the transform in dataset pipeline mode >>> numpy_slices_dataset = ds.NumpySlicesDataset(data=[['a', 'b', 'c', 'd', 'e']], column_names=["text"], ... shuffle=False) >>> # Data before >>> # | col1 | >>> # +---------------------------+ >>> # | ['a', 'b', 'c', 'd', 'e'] | >>> # +---------------------------+ >>> truncate = text.Truncate(4) >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=truncate, input_columns=["text"]) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["text"]) ['a' 'b' 'c' 'd'] >>> # Data after >>> # | col1 | >>> # +------------------------+ >>> # | ['a', 'b', 'c', 'd'] | >>> # +------------------------+ >>> >>> # Use the transform in eager mode >>> data = ["happy", "birthday", "to", "you"] >>> output = text.Truncate(2)(data) >>> print(output) ['happy' 'birthday']
- Tutorial Examples: