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fi = open("multinli_1.0_train.txt", "r")
output_file = open("multinli_const.txt", "w")
output_index_list = []
count_entailment = 0
count_neutral = 0
count_contradiction = 0
def parse_phrase_list(parse, phrases):
#print(parse)
if parse == "":
return phrases
phrase_list = phrases
words = parse.split()
this_phrase = []
next_level_parse = []
for index, word in enumerate(words):
if word == "(":
next_level_parse += this_phrase
this_phrase = ["("]
elif word == ")" and len(this_phrase) > 0 and this_phrase[0] == "(":
phrase_list.append(" ".join(this_phrase[1:]))
next_level_parse += this_phrase[1:]
this_phrase = []
elif word == ")":
next_level_parse += this_phrase
next_level_parse.append(")")
this_phrase = []
else:
this_phrase.append(word)
#next_level_parse.append(word)
#next_level_parse += this_phrase
#print(phrase_list, " ".join(next_level_parse))
return parse_phrase_list(" ".join(next_level_parse), phrase_list)
first = True
counter = 0
for line_index, line in enumerate(fi):
#if counter % 1000 == 0:
# print(counter)
counter += 1
if first:
first = False
continue
parts = line.strip().split("\t")
premise = parts[5]
hypothesis = parts[6]
label = parts[0]
parse = parts[1]
parse_new = []
for word in parse.split():
if word not in [".", "?", "!"]:
parse_new.append(word.lower())
all_phrases = parse_phrase_list(" ".join(parse_new), [])
prem_words = []
hyp_words = []
for word in premise.split():
if word not in [".", "?", "!"]:
prem_words.append(word.lower().replace(".", "").replace("?", "").replace("!", ""))
for word in hypothesis.split():
if word not in [".", "?", "!"]:
hyp_words.append(word.lower().replace(".", "").replace("?", "").replace("!", ""))
prem_filtered = " ".join(prem_words)
hyp_filtered = " ".join(hyp_words)
#print(hyp_filtered, all_phrases)
if hyp_filtered in all_phrases:
#print(premise, hypothesis, label)
#print(label)
if label == "entailment":
count_entailment += 1
if label == "neutral":
count_neutral += 1
print(premise, hypothesis, label)
if label == "contradiction":
count_contradiction += 1
print(premise, hypothesis, label)
# output_file.write(line)
output_index_list.append(line_index)
#print(premise, hypothesis, label)
#break
# open json file
with open('multinli_1.0_train.jsonl', 'r') as f:
for line_index, line in enumerate(f):
if line_index in output_index_list:
output_file.write(line)
print("Entailment:", count_entailment)
print("Contradiction:", count_contradiction)
print("Neutral:", count_neutral)
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