def build_legacy_torchtext_vocab_pipeline(vocab_file):
tokenizer = get_tokenizer("basic_english")
from torchtext.vocab import build_vocab_from_iterator
def token_iterator(vocab_file):
f = open(vocab_file, 'r')
for line in f:
for token in line:
yield token
vocab = build_vocab_from_iterator(token_iterator(vocab_file))
pipeline = sequential_transforms(tokenizer_func(tokenizer), vocab_func(vocab))
return iterate_batch(pipeline), None, None
def build_legacy_pytext_vocab_pipeline(vocab_file):
from pytext.data.utils import Vocabulary
tokenizer = get_tokenizer("basic_english")
f = open(vocab_file, 'r')
vocab_counter = Counter([token for line in f for token in line.rstrip()])
sorted_by_freq_tuples = sorted(vocab_counter.items(), key=lambda x: x[1], reverse=True)
vocab_list = [pair[0] for pair in sorted_by_freq_tuples]
vocab_list.insert(0, "<unk>")
pipeline = sequential_transforms(tokenizer_func(tokenizer),
PyTextVocabTransform(Vocabulary(vocab_list, unk_token="<unk>")))
return pipeline, None, None
def build_legacy_pytext_script_vocab_pipeline(vocab_file):
from pytext.torchscript.vocab import ScriptVocabulary
tokenizer = basic_english_normalize()
f = open(vocab_file, 'r')
vocab_counter = Counter([token for line in f for token in line.rstrip()])
sorted_by_freq_tuples = sorted(vocab_counter.items(), key=lambda x: x[1], reverse=True)
vocab_list = [pair[0] for pair in sorted_by_freq_tuples]
vocab_list.insert(0, "<unk>")
pipeline = TextSequentialTransforms(tokenizer_func(tokenizer),
PyTextScriptVocabTransform(ScriptVocabulary(vocab_list)))
jit_pipeline = torch.jit.script(pipeline.to_ivalue())
print('jit legacy PyText pipeline success!')
return pipeline, pipeline.to_ivalue(), jit_pipeline
def build_legacy_fasttext_vector_pipeline():
tokenizer = get_tokenizer("basic_english")
vector = FastText()
pipeline = sequential_transforms(tokenizer_func(tokenizer), vector_func(vector))
return pipeline, None, None
