ipnyb2py
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do_inject.sh
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7
do_inject.sh
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@ -0,0 +1,7 @@
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#!/bin/bash
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source gpu/bin/activate
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python scripts/lemmatize_glossary.py
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python scripts/lemmatize_in.py
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python scripts/inject.py
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@ -1,130 +0,0 @@
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import spacy
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import copy
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import pandas as pd
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import rapidfuzz
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from rapidfuzz.fuzz import partial_ratio
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import time
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from rapidfuzz.utils import default_process
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import sys
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spacy.require_gpu()
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spacy_nlp_en = spacy.load('en_core_web_sm')
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spacy_nlp_pl = spacy.load("pl_core_news_sm")
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def read_arguments():
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try:
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corpus_path, glossary_path = sys.argv
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return corpus_path, glossary_path
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except:
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print("ERROR: Wrong argument amount.")
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sys.exit(1)
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glossary = pd.read_csv('mt-summit-corpora/glossary.tsv', sep='\t', header=None, names=['source', 'result'])
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source_lemmatized = []
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for word in glossary['source']:
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temp = []
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for token in spacy_nlp_en(word):
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temp.append(token.lemma_)
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source_lemmatized.append(' '.join(temp).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))
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result_lemmatized = []
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for word in glossary['result']:
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temp = []
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for token in spacy_nlp_pl(word):
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temp.append(token.lemma_)
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result_lemmatized.append(' '.join(temp).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))
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glossary['source_lem'] = source_lemmatized
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glossary['result_lem'] = result_lemmatized
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glossary = glossary[['source', 'source_lem', 'result', 'result_lem']]
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glossary.to_csv('kompendium_lem.tsv', sep='\t')
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corpus_path = 'mt-summit-corpora/train/'
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skip_chars = ''',./!?'''
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with open(corpus_path + 'in.tsv', 'r') as file:
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file_lemmatized = []
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for line in file:
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if len(file_lemmatized) % 10000 == 0:
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print(len(file_lemmatized), end='\r')
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temp = []
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for token in spacy_nlp_en(line):
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temp.append(token.lemma_)
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file_lemmatized.append(' '.join([x for x in temp if x not in skip_chars]).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))
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with open(corpus_path + 'expected.tsv', 'r') as file:
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file_pl_lemmatized = []
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for line in file:
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if len(file_pl_lemmatized) % 10000 == 0:
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print(len(file_lemmatized), end='\r')
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temp = []
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for token in spacy_nlp_pl(line):
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temp.append(token.lemma_)
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file_pl_lemmatized.append(' '.join([x for x in temp if x not in skip_chars]).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))
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THRESHOLD = 88
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def is_injectable(sentence_pl, sequence):
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sen = sentence_pl.split()
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window_size = len(sequence.split())
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maxx = 0
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for i in range(len(sen) - window_size):
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current = rapidfuzz.fuzz.partial_ratio(' '.join(sen[i:i + window_size]), sequence)
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if current > maxx:
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maxx = current
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return maxx
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def inject(sentence, sequence):
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sen = sentence.split()
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window_size = len(sequence.split())
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maxx = 0
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maxxi = 0
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for i in range(len(sen) - window_size):
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current = rapidfuzz.fuzz.partial_ratio(' '.join(sen[i:i + window_size]), sequence)
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if current > maxx:
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maxx = current
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maxxi = i
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return ' '.join(sen[:maxxi + window_size]) + ' ' \
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+ glossary.loc[lambda df: df['source_lem'] == sequence]['result'].astype(str).values.flatten() \
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+ ' ' + ' '.join(sen[maxxi + window_size:])
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glossary = pd.read_csv('../kompendium_lem_cleaned.tsv', sep='\t', header=0, index_col=0)
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glossary['source_lem'] = [default_process(x) for x in glossary['source_lem']]
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start_time = time.time_ns()
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en = []
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translation_line_counts = []
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for line, line_pl in zip(file_lemmatized, file_pl_lemmatized):
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if len(translation_line_counts) % 50000 == 0:
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print(str(len(translation_line_counts)) + '/' + str(len(file_lemmatized), end='\r'))
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line = default_process(line)
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line_pl = default_process(line_pl)
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matchez = rapidfuzz.process.extract(query=line, choices=glossary['source_lem'], limit=5, score_cutoff=THRESHOLD, scorer=partial_ratio)
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translation_line_counts.append(len(matchez))
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for match in matchez:
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# if is_injectable(line_pl, match[0]):
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en.append(inject(line, match[0])[0])
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stop = time.time_ns()
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timex = (stop - start_time) / 1000000000
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print(timex)
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tlcs = copy.deepcopy(translation_line_counts)
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translations = pd.read_csv(corpus_path + 'expected.tsv', sep='\t', header=None, names=['text'])
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with open(corpus_path + 'extected.tsv.injected.crossvalidated.pl', 'w') as file_pl:
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for line, translation_line_ct in zip(translations, tlcs):
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for i in range(translation_line_ct):
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file_pl.write(line)
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with open(corpus_path + 'in.tsv.injected.crossvalidated.en', 'w') as file_en:
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for e in en:
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file_en.write(e + '\n')
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@ -1,136 +0,0 @@
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import copy
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import pandas as pd
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import spacy
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from spaczz.matcher import FuzzyMatcher
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# spacy.require_gpu()
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spacy_nlp_en = spacy.load('en_core_web_sm')
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spacy_nlp_pl = spacy.load('pl_core_news_sm')
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print('lemmatizing glossary')
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glossary = pd.read_csv('glossary.tsv', sep='\t', header=None, names=['source', 'result'])
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source_lemmatized = []
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for word in glossary['source']:
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temp = []
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for token in spacy_nlp_en(word):
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temp.append(token.lemma_)
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source_lemmatized.append(' '.join(temp).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))
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result_lemmatized = []
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for word in glossary['result']:
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temp = []
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for token in spacy_nlp_pl(word):
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temp.append(token.lemma_)
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result_lemmatized.append(' '.join(temp).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))
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glossary['source_lem'] = source_lemmatized
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glossary['result_lem'] = result_lemmatized
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glossary = glossary[['source', 'source_lem', 'result', 'result_lem']]
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glossary.set_index('source_lem')
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glossary.to_csv('glossary_lem.tsv', sep='\t')
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dev_path = 'dev-0/'
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print('lemmatizing corpus ' + dev_path)
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skip_chars = ''',./!?'''
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with open(dev_path + 'in.tsv', 'r') as file:
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file_lemmatized = []
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for line in file:
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temp = []
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for token in spacy_nlp_en(line):
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temp.append(token.lemma_)
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file_lemmatized.append(' '.join([x for x in temp if x not in skip_chars])
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.replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))
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with open(dev_path + 'expected.tsv', 'r') as file:
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file_pl_lemmatized = []
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for line in file:
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temp = []
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for token in spacy_nlp_pl(line):
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temp.append(token.lemma_)
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file_pl_lemmatized.append(' '.join([x for x in temp if x not in skip_chars])
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.replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))
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# glossary
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glossary = pd.read_csv('glossary_lem.tsv', sep='\t', header=0, index_col=0)
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train_glossary = glossary.iloc[[x for x in range(len(glossary)) if x % 6 != 0]]
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# add rules to English matcher
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nlp = spacy.blank("en")
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matcher = FuzzyMatcher(nlp.vocab)
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for word in train_glossary['source_lem']:
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matcher.add(word, [nlp(word)])
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# add rules to Polish matcher
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nlp_pl = spacy.blank("pl")
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matcher_pl = FuzzyMatcher(nlp_pl.vocab)
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for word, word_id in zip(train_glossary['result_lem'], train_glossary['source_lem']):
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matcher_pl.add(word, [nlp_pl(word)])
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en = []
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translation_line_counts = []
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for line_id in range(len(file_lemmatized)):
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if line_id % 100 == 0:
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print('injecting glossary: ' + str(line_id) + "/" + str(len(file_lemmatized)), end='\r')
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doc = nlp(file_lemmatized[line_id])
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matches = matcher(doc)
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line_counter = 0
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for match_id, start, end, ratio in matches:
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if ratio > 90:
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doc_pl = nlp_pl(file_pl_lemmatized[line_id])
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matches_pl = matcher_pl(doc_pl)
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for match_id_pl, start_pl, end_pl, ratio_pl in matches_pl:
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if match_id_pl == glossary[glossary['source_lem'] == match_id].values[0][3]:
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line_counter += 1
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en.append(''.join(doc[:end].text + ' ' + train_glossary.loc[lambda df: df['source_lem'] == match_id]['result'].astype(str).values.flatten() + ' ' + doc[end:].text))
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if line_counter == 0:
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line_counter = 1
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en.append(file_lemmatized[line_id])
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translation_line_counts.append(line_counter)
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print('saving files')
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tlcs = copy.deepcopy(translation_line_counts)
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translations = pd.read_csv(dev_path + 'expected.tsv', sep='\t', header=None, names=['text'])
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translations['id'] = [x for x in range(len(translations))]
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ctr = 0
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sentence = ''
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with open(dev_path + 'in.tsv.injected.crossvalidated', 'w') as file_en:
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with open(dev_path + 'expected.tsv.injected.crossvalidated', 'w') as file_pl:
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for i in range(len(en)):
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if i > 0:
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if en[i-1] != en[i]:
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if ctr == 0:
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sentence = translations.iloc[0]
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translations.drop(sentence['id'], inplace=True)
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sentence = sentence['text']
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try:
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ctr = tlcs.pop(0)
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except:
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pass
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file_en.write(en[i])
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file_pl.write(sentence + '\n')
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ctr = ctr - 1
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else:
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try:
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ctr = tlcs.pop(0) - 1
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except:
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pass
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sentence = translations.iloc[0]
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translations.drop(sentence['id'], inplace=True)
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sentence = sentence['text']
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file_en.write(en[i])
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file_pl.write(sentence + '\n')
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import time
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import nltk
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from nltk.stem import WordNetLemmatizer
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# nltk.download('omw-1.4')
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# nltk.download('punkt')
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nltk.download('wordnet')
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wl = WordNetLemmatizer()
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start_time = time.time_ns()
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filex = []
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with open('mt-summit-corpora/train/in.tsv', 'r') as file:
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for line in file:
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if len(filex) % 50000 == 0:
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print(len(filex), end='\r')
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line = nltk.word_tokenize(line)
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filex.append(' '.join([wl.lemmatize(x) for x in line]))
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stop = time.time_ns()
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timex = (stop - start_time) / 1000000000
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print(timex)
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f = open('temp', 'w')
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for line in filex:
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f.write(line + '\n')
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@ -6,5 +6,4 @@ virtualenv -p python3.8 gpu
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source gpu/bin/activate
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pip install pandas ipython
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pip install nltk
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python "nltk.download('omw-1.4')"
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pip install rapidfuzz
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@ -2,8 +2,18 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"execution_count": 2,
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"outputs": [
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{
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"data": {
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"text/plain": " source \\\nsource_lem \naaofi aaofi \naca aca \nacca acca \nabacus abacus \nabandonment cost abandonment costs \n... ... \nytd ytd \nyear-end year-end \nyear-to-date year-to-date \nzog zog \nzero overhead growth zero overhead growth \n\n result \nsource_lem \naaofi organizacja rachunkowości i audytu dla islamsk... \naca członek stowarzyszenia dyplomowanych biegłych ... \nacca stowarzyszenie dyplomowanych biegłych rewidentów \nabacus liczydło \nabandonment cost koszty zaniechania \n... ... \nytd od początku roku \nyear-end koniec roku \nyear-to-date od początku roku \nzog zero wzrostu kosztów ogólnych \nzero overhead growth zero wzrostu kosztów ogólnych \n\n[1197 rows x 2 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>source</th>\n <th>result</th>\n </tr>\n <tr>\n <th>source_lem</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>aaofi</th>\n <td>aaofi</td>\n <td>organizacja rachunkowości i audytu dla islamsk...</td>\n </tr>\n <tr>\n <th>aca</th>\n <td>aca</td>\n <td>członek stowarzyszenia dyplomowanych biegłych ...</td>\n </tr>\n <tr>\n <th>acca</th>\n <td>acca</td>\n <td>stowarzyszenie dyplomowanych biegłych rewidentów</td>\n </tr>\n <tr>\n <th>abacus</th>\n <td>abacus</td>\n <td>liczydło</td>\n </tr>\n <tr>\n <th>abandonment cost</th>\n <td>abandonment costs</td>\n <td>koszty zaniechania</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>ytd</th>\n <td>ytd</td>\n <td>od początku roku</td>\n </tr>\n <tr>\n <th>year-end</th>\n <td>year-end</td>\n <td>koniec roku</td>\n </tr>\n <tr>\n <th>year-to-date</th>\n <td>year-to-date</td>\n <td>od początku roku</td>\n </tr>\n <tr>\n <th>zog</th>\n <td>zog</td>\n <td>zero wzrostu kosztów ogólnych</td>\n </tr>\n <tr>\n <th>zero overhead growth</th>\n <td>zero overhead growth</td>\n <td>zero wzrostu kosztów ogólnych</td>\n </tr>\n </tbody>\n</table>\n<p>1197 rows × 2 columns</p>\n</div>"
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"import nltk\n",
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@ -15,6 +25,8 @@
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"from rapidfuzz.fuzz import partial_ratio\n",
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"from rapidfuzz.utils import default_process\n",
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"\n",
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"nltk.download('wordnet')\n",
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"\n",
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"\n",
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"wl = WordNetLemmatizer()\n",
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"\n",
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 12,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.194806501\n"
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"0.187306436\n"
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]
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}
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],
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@ -80,13 +91,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"execution_count": 19,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"6.915366953\n"
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"6.592824061\n"
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]
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}
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],
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@ -94,6 +105,7 @@
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"\n",
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"THRESHOLD = 70\n",
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"\n",
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"\n",
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"def is_injectable(sentence_pl, sequence):\n",
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" sen = sentence_pl.split()\n",
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" window_size = len(sequence.split())\n",
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@ -102,24 +114,24 @@
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" current = rapidfuzz.fuzz.ratio(' '.join(sen[i:i + window_size]), sequence)\n",
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" if current > maxx:\n",
|
||||
" maxx = current\n",
|
||||
" if maxx >= THRESHOLD:\n",
|
||||
" return True\n",
|
||||
" else:\n",
|
||||
" return False\n",
|
||||
" return maxx >= THRESHOLD\n",
|
||||
"\n",
|
||||
"def get_injected(sentence, sentence_en, sequence, inject):\n",
|
||||
" sen = sentence.split()\n",
|
||||
" sen_en = sentence_en.split()\n",
|
||||
" window_size = len(sequence.split())\n",
|
||||
" maxx = 0\n",
|
||||
" maxx_prv = 0\n",
|
||||
" maxxi = 0\n",
|
||||
" for i in range(len(sen) - window_size + 1):\n",
|
||||
" current = rapidfuzz.fuzz.ratio(' '.join(sen[i:i + window_size]), sequence)\n",
|
||||
" if current >= maxx:\n",
|
||||
" maxx_prv = maxx\n",
|
||||
" maxx = current\n",
|
||||
" maxxi = i\n",
|
||||
" temp = ' '.join(sen_en[:maxxi + window_size]) + ' $' + inject + '$ ' + ' '.join(sen_en[maxxi + window_size:])\n",
|
||||
" return temp\n",
|
||||
" if maxx_prv != maxx:\n",
|
||||
" return ' '.join(sen_en[:maxxi + window_size]) + ' $' + inject + '$ ' + ' '.join(sen_en[maxxi + window_size:])\n",
|
||||
" return sentence_en\n",
|
||||
"\n",
|
||||
"glossary['source_lem'] = [str(default_process(x)) for x in glossary['source_lem']]\n",
|
||||
"file_pl = pd.read_csv(train_expected_path, sep='\\t', header=None, names=['text'])\n",
|
||||
@ -162,14 +174,17 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"execution_count": 21,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"def full_strip(line):\n",
|
||||
" return ' '.join(line.split())\n",
|
||||
"\n",
|
||||
"with open(train_expected_path + '.injected', 'w') as file_pl_write:\n",
|
||||
" for line, translation_line_ct in zip(file_pl['text'].values.tolist(), translation_line_counts):\n",
|
||||
" for i in range(translation_line_ct):\n",
|
||||
" file_pl_write.write(line + '\\n')\n",
|
||||
" file_pl_write.write(full_strip(line) + '\\n')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"with open(train_in_path + '.injected', 'w') as file_en_write:\n",
|
||||
@ -185,7 +200,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 16,
|
||||
"outputs": [],
|
||||
"source": [],
|
||||
"metadata": {
|
||||
|
||||
95
scripts/inject.py
Normal file
95
scripts/inject.py
Normal file
@ -0,0 +1,95 @@
|
||||
import pandas as pd
|
||||
import rapidfuzz
|
||||
|
||||
from rapidfuzz.fuzz import partial_ratio
|
||||
from rapidfuzz.utils import default_process
|
||||
|
||||
|
||||
def full_strip(line):
|
||||
return ' '.join(line.split())
|
||||
|
||||
|
||||
def is_injectable(sentence_pl, sequence):
|
||||
sen = sentence_pl.split()
|
||||
window_size = len(sequence.split())
|
||||
maxx = 0
|
||||
for i in range(len(sen) - window_size + 1):
|
||||
current = rapidfuzz.fuzz.ratio(' '.join(sen[i:i + window_size]), sequence)
|
||||
if current > maxx:
|
||||
maxx = current
|
||||
return maxx >= THRESHOLD
|
||||
|
||||
|
||||
def get_injected(sentence, sentence_en, sequence, inject):
|
||||
sen = sentence.split()
|
||||
sen_en = sentence_en.split()
|
||||
window_size = len(sequence.split())
|
||||
maxx = 0
|
||||
maxx_prv = 0
|
||||
maxxi = 0
|
||||
for i in range(len(sen) - window_size + 1):
|
||||
current = rapidfuzz.fuzz.ratio(' '.join(sen[i:i + window_size]), sequence)
|
||||
if current >= maxx:
|
||||
maxx_prv = maxx
|
||||
maxx = current
|
||||
maxxi = i
|
||||
if maxx_prv != maxx:
|
||||
return ' '.join(sen_en[:maxxi + window_size]) + ' $' + inject + '$ ' + ' '.join(sen_en[maxxi + window_size:])
|
||||
return sentence_en
|
||||
|
||||
|
||||
THRESHOLD = 70
|
||||
|
||||
# train_in_path = 'mt-summit-corpora/train/in.tsv'
|
||||
# train_expected_path = 'mt-summit-corpora/train/expected.tsv'
|
||||
|
||||
train_in_path = 'mt-summit-corpora/dev-0/in.tsv'
|
||||
train_expected_path = 'mt-summit-corpora/dev-0/expected.tsv'
|
||||
|
||||
glossary = pd.read_csv('mt-summit-corpora/glossary_lem.tsv', sep='\t')
|
||||
glossary['source_lem'] = [str(default_process(x)) for x in glossary['source_lem']]
|
||||
|
||||
file_pl = pd.read_csv(train_expected_path, sep='\t', header=None, names=['text'])
|
||||
file_pl['text'] = [default_process(text) for text in file_pl['text'].values.tolist()]
|
||||
file_pl = file_pl['text'].values.tolist()
|
||||
|
||||
file_en = pd.read_csv(train_in_path, sep='\t', header=None, names=['text'])
|
||||
file_en['text'] = [default_process(text) for text in file_en['text'].values.tolist()]
|
||||
file_en = file_en['text'].values.tolist()
|
||||
|
||||
file_en_lemmatized = pd.read_csv(train_in_path + '.lemmatized', sep='\t', header=None, names=['text'])
|
||||
file_en_lemmatized['text'] = [default_process(text) for text in file_en_lemmatized['text'].values.tolist()]
|
||||
file_en_lemmatized = file_en_lemmatized['text'].values.tolist()
|
||||
|
||||
en = []
|
||||
translation_line_counts = []
|
||||
for line, line_en, line_pl in zip(file_en_lemmatized, file_en, file_pl):
|
||||
line = default_process(line)
|
||||
matchez = rapidfuzz.process.extract(
|
||||
query=line, choices=glossary['source_lem'], limit=5, score_cutoff=THRESHOLD, scorer=partial_ratio)
|
||||
if len(matchez) > 0:
|
||||
lines_added = 0
|
||||
for match in matchez:
|
||||
polish_translation = \
|
||||
glossary.loc[lambda df: df['source_lem'] == match[0]]['result'].astype(str).values.flatten()[0]
|
||||
if is_injectable(line_pl, polish_translation):
|
||||
en.append(get_injected(line, line_en, match[0], polish_translation))
|
||||
lines_added += 1
|
||||
if lines_added == 0:
|
||||
en.append(line_en)
|
||||
lines_added = 1
|
||||
translation_line_counts.append(lines_added)
|
||||
else:
|
||||
translation_line_counts.append(1)
|
||||
en.append(line_en)
|
||||
|
||||
|
||||
with open(train_expected_path + '.injected', 'w') as file_pl_write:
|
||||
for line, translation_line_ct in zip(file_pl, translation_line_counts):
|
||||
for i in range(translation_line_ct):
|
||||
file_pl_write.write(full_strip(line) + '\n')
|
||||
|
||||
|
||||
with open(train_in_path + '.injected', 'w') as file_en_write:
|
||||
for e in en:
|
||||
file_en_write.write(e + '\n')
|
||||
19
scripts/lemmatize_glossary.py
Normal file
19
scripts/lemmatize_glossary.py
Normal file
@ -0,0 +1,19 @@
|
||||
import nltk
|
||||
import pandas as pd
|
||||
|
||||
from nltk.stem import WordNetLemmatizer
|
||||
|
||||
nltk.download('wordnet')
|
||||
|
||||
wl = WordNetLemmatizer()
|
||||
glossary = pd.read_csv('mt-summit-corpora/glossary.tsv', sep='\t', header=None, names=['source', 'result'])
|
||||
source_lemmatized = []
|
||||
for word in glossary['source']:
|
||||
word = nltk.word_tokenize(word)
|
||||
source_lemmatized.append(' '.join([wl.lemmatize(x) for x in word]))
|
||||
|
||||
glossary['source_lem'] = source_lemmatized
|
||||
glossary = glossary[['source', 'source_lem', 'result']]
|
||||
glossary.set_index('source_lem')
|
||||
|
||||
glossary.to_csv('mt-summit-corpora/glossary_lem.tsv', sep='\t', index=False)
|
||||
23
scripts/lemmatize_in.py
Normal file
23
scripts/lemmatize_in.py
Normal file
@ -0,0 +1,23 @@
|
||||
import nltk
|
||||
from nltk.stem import WordNetLemmatizer
|
||||
|
||||
|
||||
wl = WordNetLemmatizer()
|
||||
|
||||
# train_in_path = 'mt-summit-corpora/train/in.tsv'
|
||||
# train_expected_path = 'mt-summit-corpora/train/expected.tsv'
|
||||
|
||||
train_in_path = 'mt-summit-corpora/dev-0/in.tsv'
|
||||
train_expected_path = 'mt-summit-corpora/dev-0/expected.tsv'
|
||||
|
||||
file_lemmatized = []
|
||||
with open(train_in_path, 'r') as file:
|
||||
for line in file:
|
||||
if len(file_lemmatized) % 50000 == 0:
|
||||
print('lemmatizing file: ' + train_in_path + ': ' + str(len(file_lemmatized)), end='\r')
|
||||
line = nltk.word_tokenize(line)
|
||||
file_lemmatized.append(' '.join([wl.lemmatize(x) for x in line]))
|
||||
|
||||
with open(train_in_path + '.lemmatized', 'w') as file_write:
|
||||
for line in file_lemmatized:
|
||||
file_write.write(line + '\n')
|
||||
Loading…
Reference in New Issue
Block a user