def test_spacy_model_with_regex_example_tokenize(tokenizers):
text = "In 1096, Crusaders passing by the siege of Amalfi were joined by Bohemond of Taranto and his nephew Tancred with an army of Italo-Normans. Bohemond was the de facto leader of the Crusade during its passage through Asia Minor. After the successful Siege of Antioch in 1097, Bohemond began carving out an independent principality around that city. Tancred was instrumental in the conquest of Jerusalem and he worked for the expansion of the Crusader kingdom in Transjordan and the region of Galilee.[citation needed]"
sent_tokenizer = SentTokenizer("punkt")
word_tokenizer = WordTokenizer("spacy_en",
sent_tokenizer,
split_with_regex=True)
disables = ["vectors", "textcat", "parser"]
spacy_model = spacy.load("en_core_web_sm", disable=disables)
spacy_model.tokenizer = load_spacy_model_for_tokenizer(
word_tokenizer.extra_split_chars_re)
sentences = sent_tokenizer.tokenize(text)
spacy_model_results = []
for sentence in sentences:
spacy_model_results += [token.text for token in spacy_model(sentence)]
assert word_tokenizer.tokenize(text) == spacy_model_results
text = "20th Century Fox, Lionsgate, Paramount Pictures, Universal Studios and Walt Disney Studios paid for movie trailers to be aired during the Super Bowl. Fox paid for Deadpool, X-Men: Apocalypse, Independence Day: Resurgence and Eddie the Eagle, Lionsgate paid for Gods of Egypt, Paramount paid for Teenage Mutant Ninja Turtles: Out of the Shadows and 10 Cloverfield Lane, Universal paid for The Secret Life of Pets and the debut trailer for Jason Bourne and Disney paid for Captain America: Civil War, The Jungle Book and Alice Through the Looking Glass.[citation needed]"
sentences = sent_tokenizer.tokenize(text)
spacy_model_results = []
for sentence in sentences:
spacy_model_results += [token.text for token in spacy_model(sentence)]
assert word_tokenizer.tokenize(text) == spacy_model_results
def __init__(
self,
file_paths,
lang_code,
tokenizers,
max_seq_length=384,
context_stride=128,
max_question_length=64,
):
super(SQuADBertReader, self).__init__(file_paths, SQuADBertDataset)
self.lang_code = lang_code
self.max_seq_length = max_seq_length
self.context_stride = context_stride
self.max_question_length = max_question_length
self.text_columns = ["bert_input", "context", "question"]
if "subword" not in tokenizers:
raise ValueError("WordTokenizer and SubwordTokenizer is required.")
sent_tokenizer = SentTokenizer("punkt", {})
if lang_code == "ko":
self.word_tokenizer = WordTokenizer("mecab_ko",
sent_tokenizer,
split_with_regex=True)
else:
self.word_tokenizer = WordTokenizer("treebank_en",
sent_tokenizer,
split_with_regex=True)
self.subword_tokenizer = tokenizers["subword"]
def tokenizers(request):
sent_name, sent_config, word_name, word_config, \
subword_name, subword_config, char_name, char_config = request.param
sent_tokenizer = SentTokenizer(sent_name, config=sent_config)
word_tokenizer = WordTokenizer(word_name,
sent_tokenizer,
config=word_config)
subword_tokenizer = SubwordTokenizer(subword_name,
word_tokenizer,
config=subword_config)
char_tokenizer = CharTokenizer(char_name,
word_tokenizer,
config=char_config)
return {
"sent": sent_tokenizer,
"word": word_tokenizer,
"subword": subword_tokenizer,
"char": char_tokenizer,
}
def __init__(
self,
file_paths,
lang_code,
tokenizers,
max_seq_length=384,
context_stride=128,
max_question_length=64,
cls_token="[CLS]",
sep_token="[SEP]",
):
super(SQuADBertReader, self).__init__(file_paths, SQuADBertDataset)
self.lang_code = lang_code
self.max_seq_length = max_seq_length
self.context_stride = context_stride
self.max_question_length = max_question_length
self.cls_token = cls_token
self.sep_token = sep_token
self.text_columns = ["bert_input", "context", "question"]
sent_tokenizer = SentTokenizer("punkt", {})
if lang_code == "ko":
self.word_tokenizer = WordTokenizer("mecab_ko",
sent_tokenizer,
split_with_regex=True)
else:
self.word_tokenizer = WordTokenizer("treebank_en",
sent_tokenizer,
split_with_regex=True)
if tokenizers["bpe"] is not None:
self.sub_level_tokenizer = tokenizers["bpe"] # RoBERTa
elif tokenizers["subword"] is not None:
self.sub_level_tokenizer = tokenizers["subword"] # BERT
else:
raise ValueError("'bpe' or 'subword' tokenizer is required.")