class TestSimpleWordSplitter(AllenNlpTestCase):
def setUp(self):
super(TestSimpleWordSplitter, self).setUp()
self.word_splitter = SimpleWordSplitter()
def test_tokenize_handles_complex_punctuation(self):
sentence = "this (sentence) has 'crazy' \"punctuation\"."
expected_tokens = [
"this", "(", "sentence", ")", "has", "'", "crazy", "'", '"',
"punctuation", '"', "."
]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_tokenize_handles_contraction(self):
sentence = "it ain't joe's problem; would've been yesterday"
expected_tokens = [
"it", "ai", "n't", "joe", "'s", "problem", ";", "would", "'ve",
"been", "yesterday"
]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_tokenize_handles_multiple_contraction(self):
sentence = "wouldn't've"
expected_tokens = ["would", "n't", "'ve"]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_tokenize_handles_final_apostrophe(self):
sentence = "the jones' house"
expected_tokens = ["the", "jones", "'", "house"]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_tokenize_handles_special_cases(self):
sentence = "mr. and mrs. jones, etc., went to, e.g., the store"
expected_tokens = [
"mr.", "and", "mrs.", "jones", ",", "etc.", ",", "went", "to", ",",
"e.g.", ",", "the", "store"
]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
class TestSimpleWordSplitter(AllenNlpTestCase):
def setUp(self):
super(TestSimpleWordSplitter, self).setUp()
self.word_splitter = SimpleWordSplitter()
def test_tokenize_handles_complex_punctuation(self):
sentence = "this (sentence) has 'crazy' \"punctuation\"."
expected_tokens = ["this", "(", "sentence", ")", "has", "'", "crazy", "'", '"',
"punctuation", '"', "."]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_tokenize_handles_contraction(self):
sentence = "it ain't joe's problem; would've been yesterday"
expected_tokens = ["it", "ai", "n't", "joe", "'s", "problem", ";", "would", "'ve", "been",
"yesterday"]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_batch_tokenization(self):
sentences = ["This is a sentence",
"This isn't a sentence.",
"This is the 3rd sentence."
"Here's the 'fourth' sentence."]
batch_split = self.word_splitter.batch_split_words(sentences)
separately_split = [self.word_splitter.split_words(sentence) for sentence in sentences]
assert len(batch_split) == len(separately_split)
for batch_sentence, separate_sentence in zip(batch_split, separately_split):
assert len(batch_sentence) == len(separate_sentence)
for batch_word, separate_word in zip(batch_sentence, separate_sentence):
assert batch_word.text == separate_word.text
def test_tokenize_handles_multiple_contraction(self):
sentence = "wouldn't've"
expected_tokens = ["would", "n't", "'ve"]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_tokenize_handles_final_apostrophe(self):
sentence = "the jones' house"
expected_tokens = ["the", "jones", "'", "house"]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_tokenize_handles_special_cases(self):
sentence = "mr. and mrs. jones, etc., went to, e.g., the store"
expected_tokens = ["mr.", "and", "mrs.", "jones", ",", "etc.", ",", "went", "to", ",",
"e.g.", ",", "the", "store"]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
class TestSimpleWordSplitter(AllenNlpTestCase):
def setUp(self):
super(TestSimpleWordSplitter, self).setUp()
self.word_splitter = SimpleWordSplitter()
def test_tokenize_handles_complex_punctuation(self):
sentence = "this (sentence) has 'crazy' \"punctuation\"."
expected_tokens = ["this", "(", "sentence", ")", "has", "'", "crazy", "'", '"',
"punctuation", '"', "."]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_tokenize_handles_contraction(self):
sentence = "it ain't joe's problem; would've been yesterday"
expected_tokens = ["it", "ai", "n't", "joe", "'s", "problem", ";", "would", "'ve", "been",
"yesterday"]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_batch_tokenization(self):
sentences = ["This is a sentence",
"This isn't a sentence.",
"This is the 3rd sentence."
"Here's the 'fourth' sentence."]
batch_split = self.word_splitter.batch_split_words(sentences)
separately_split = [self.word_splitter.split_words(sentence) for sentence in sentences]
assert len(batch_split) == len(separately_split)
for batch_sentence, separate_sentence in zip(batch_split, separately_split):
assert len(batch_sentence) == len(separate_sentence)
for batch_word, separate_word in zip(batch_sentence, separate_sentence):
assert batch_word.text == separate_word.text
def test_tokenize_handles_multiple_contraction(self):
sentence = "wouldn't've"
expected_tokens = ["would", "n't", "'ve"]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_tokenize_handles_final_apostrophe(self):
sentence = "the jones' house"
expected_tokens = ["the", "jones", "'", "house"]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
def test_tokenize_handles_special_cases(self):
sentence = "mr. and mrs. jones, etc., went to, e.g., the store"
expected_tokens = ["mr.", "and", "mrs.", "jones", ",", "etc.", ",", "went", "to", ",",
"e.g.", ",", "the", "store"]
tokens = [t.text for t in self.word_splitter.split_words(sentence)]
assert tokens == expected_tokens
with open(in_file, "r") as in_fp:
for line in tqdm(in_fp.readlines()):
struct = jsondecoder.decode(line)
hypothesis = struct["claim"]
premise_idx = 0
for sentence in struct["predicted_sentences"]:
underlined_title = sentence[0]
label = 0 # placeholder, but must be a valid index
premise = sentence[3]
# Prefix the premise sentence with [ TITLE ] (from source article)
title = underlined_title.replace("_", " ")
title_words = tokenizer.split_words(title)
tokenized_title = " ".join(map(lambda x: x.text, title_words))
premise = "[ " + tokenized_title + " ] " + premise
premise_words = premise.split(" ")
if (len(premise_words) > max_sent_len):
premise = " ".join(premise_words[0:max_sent_len])
info = str(struct["id"]) + "\t" + str(premise_idx) + "\t"
info = info + str(sentence[0]) + "\t" + str(sentence[1])
premise_fp.write(premise + "\n")
hypothesis_fp.write(hypothesis + "\n")
label_fp.write(str(label) + "\n")
index_fp.write(info + "\n")
# from retrieval.fever_doc_db import FeverDocDB
parser = argparse.ArgumentParser()
parser.add_argument("--in_file", type=str, required=True)
parser.add_argument("--out_file", type=str, required=True)
args = parser.parse_args()
in_file = args.in_file
out_file = args.out_file
if os.path.exists(out_file):
raise ValueError("Output already exists")
jsondecoder = json.JSONDecoder()
jsonencoder = json.JSONEncoder()
tokenizer = SimpleWordSplitter()
print("Tokenizing")
with open(in_file, "r") as in_fp:
with open(out_file, "w") as out_fp:
for line in tqdm(in_fp.readlines()):
struct = jsondecoder.decode(line)
tok = tokenizer.split_words(struct["claim"])
tokenized = " ".join(map(lambda x: x.text, tok))
struct["claim"] = tokenized
result = jsonencoder.encode(struct)
out_fp.write(result + "\n")