Differences with torchtext.data.functional.numericalize_tokens_from_iterator

torchtext.data.functional.numericalize_tokens_from_iterator

torchtext.data.functional.numericalize_tokens_from_iterator(
    vocab,
    iterator,
    removed_tokens=None
)

For more information, see torchtext.data.functional.numericalize_tokens_from_iterator.

mindspore.dataset.text.Lookup

class mindspore.dataset.text.Lookup(
    vocab,
    unknown_token=None,
    data_type=mstype.int32
)

For more information, see mindspore.dataset.text.Lookup.

Differences

PyTorch: Generate the id list corresponding to the vocabulary from the word segmentation iterator, input the mapping table corresponding to the vocabulary and the id, the vocabulary iterator, and return the created iterator object, from which the id of the corresponding vocabulary can be obtained.

MindSpore: Look up a word into an id according to the input vocabulary table.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameter	Parameter1	vocab	vocab	-
	Parameter2	iterator	-	Strings to be tokenized, see usage below in MindSpore
	Parameter3	removed_tokens	-	Removed tokens from output dataset, not support by MindSpore
	Parameter4	-	unknown_token	Word is used in case of the target word is out of vocabulary
	Parameter5	-	data_type	The data type output by lookup

Code Example

# PyTorch
from torchtext.data.functional import numericalize_tokens_from_iterator

def gen():
    yield ["Sentencepiece", "as", "encode"]

vocab = {'Sentencepiece' : 0, 'encode' : 1, 'as' : 2, 'pieces' : 3}
ids_iter = numericalize_tokens_from_iterator(vocab, gen())
for ids in ids_iter:
    print([num for num in ids])
# Out: [0, 2, 1]


# MindSpore
import mindspore.dataset.text as text

vocab = text.Vocab.from_dict({'Sentencepiece' : 0, 'encode' : 1, 'as' : 2, 'pieces' : 3})
result = text.Lookup(vocab)(["Sentencepiece", "as", "encode"])
print(result)
# Out: [0 2 1]