wygladzanie
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21038
dev-0/out.tsv
21038
dev-0/out.tsv
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128
run.py
128
run.py
@ -1,80 +1,80 @@
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from nltk import tris, word_tokenize
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from re import T
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import pandas as pd
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import csv
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import regex as re
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from collections import Counter, defaultdict
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from nltk.tokenize import RegexpTokenizer
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from nltk import trigrams
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import regex as re
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import lzma
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train = pd.read_csv(
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'train/in.tsv.xz',
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sep='\t',
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on_bad_lines='skip',
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header=None,
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quoting=csv.QUOTE_NONE,
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nrows=40000)
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class GapEssa:
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def __init__(self):
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self.alpha = 0.0001
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self.vocab = set()
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self.model = defaultdict(lambda: defaultdict(lambda: 0))
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self.tokenizer = RegexpTokenizer(r"\w+")
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labels = pd.read_csv(
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'train/expected.tsv',
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sep='\t',
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on_bad_lines='skip',
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header=None,
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quoting=csv.QUOTE_NONE,
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nrows=40000)
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def data_preprocessing(text):
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return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' '))
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def predict(before, after):
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prediction = dict(Counter(dict(tri[before, after])).most_common(5))
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result = ''
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prob = 0.0
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for key, value in prediction.items():
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prob += value
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result += f'{key}:{value} '
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if prob == 0.0:
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return 'to:0.015 be:0.015 the:0.015 not:0.01 and:0.02 a:0.02 :0.9'
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result += f':{max(1 - prob, 0.01)}'
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return result
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def make_prediction(file):
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data = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
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with open(f'{file}/out.tsv', 'w', encoding='utf-8') as file_out:
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for _, row in data.iterrows():
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before, after = word_tokenize(data_preprocessing(str(row[6]))), word_tokenize(data_preprocessing(str(row[7])))
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if len(before) < 3 or len(after) < 3:
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prediction = 'to:0.015 be:0.015 the:0.015 not:0.01 and:0.02 a:0.02 :0.9'
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def read_file(self, f, mode=0):
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for line in f:
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text = line.split("\t")
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if(mode==0):
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yield re.sub(r"[^\w\d'\s]+", '', re.sub(' +', ' ', ' '.join([text[6], text[7]]).replace("\\n"," ").replace("\n","").lower()))
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else:
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prediction = predict(before[-1], after[0])
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file_out.write(prediction + '\n')
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yield re.sub(r"[^\w\d'\s]+", '', re.sub(' +', ' ', text[7].replace("\\n"," ").replace("\n","").lower()))
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def train(self, f):
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with lzma.open(f, mode='rt') as file:
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for index, text in enumerate(self.read_file(file)):
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tokens = self.tokenizer.tokenize(text)
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for w1, w2, w3 in trigrams(tokens, pad_right=True, pad_left=True):
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if w1 and w2 and w3:
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self.model[(w2, w3)][w1] += 1
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self.vocab.add(w1)
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self.vocab.add(w2)
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self.vocab.add(w3)
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if index == 40000:
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break
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train = train[[6, 7]]
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train = pd.concat([train, labels], axis=1)
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train['line'] = train[6] + train[0] + train[7]
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for pair in self.model:
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num_n_grams = float(sum(self.model[pair].values()))
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for word in self.model[pair]:
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self.model[pair][word] = (self.model[pair][word] + self.alpha) / (num_n_grams + self.alpha*len(self.vocab))
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def out(self, input_f, output_f):
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with open(output_f, 'w') as out_f:
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with lzma.open(input_f, mode='rt') as in_f:
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for _, text in enumerate(self.read_file(in_f, mode=1)):
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t = self.tokenizer.tokenize(text)
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if len(t) < 4:
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# p = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
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p = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'
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else:
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p = self.pred(t[0], t[1])
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out_f.write(p + '\n')
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tri = defaultdict(lambda: defaultdict(lambda: 0))
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def pred(self, w1, w2):
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total = 0.0
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line = ''
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rows = train.iterrows()
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rows_len = len(train)
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for index, (_, row) in enumerate(rows):
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text = data_preprocessing(str(row['line']))
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words = word_tokenize(text)
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for word_1, word_2, word_3 in tris(words, pad_right=True, pad_left=True):
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if word_1 and word_2 and word_3:
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tri[(word_1, word_3)][word_2] += 1
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p = dict(self.model[w1, w2])
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m = dict(Counter(p).most_common(6))
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model_len = len(tri)
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for index, words_1_3 in enumerate(tri):
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count = sum(tri[words_1_3].values())
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for word_2 in tri[words_1_3]:
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tri[words_1_3][word_2] += 0.25
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tri[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
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for word, prob in m.items():
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total += prob
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line += f'{word}:{prob} '
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if total == 0.0:
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return 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'
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if 1 - total >= 0.01:
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line += f":{1-total}"
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else:
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line += f":0.01"
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make_prediction('test-A')
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make_prediction('dev-0')
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return line
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wp = GapEssa()
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wp.train('train/in.tsv.xz')
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wp.out('dev-0/in.tsv.xz', 'dev-0/out.tsv')
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wp.out('test-A/in.tsv.xz', 'test-A/out.tsv')
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81
run_old.py
Normal file
81
run_old.py
Normal file
@ -0,0 +1,81 @@
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from nltk import trigram as tris
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from nltk import word_tokenize
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import pandas as pd
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import csv
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import regex as re
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from collections import Counter, defaultdict
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train = pd.read_csv(
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'train/in.tsv.xz',
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sep='\t',
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on_bad_lines='skip',
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header=None,
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quoting=csv.QUOTE_NONE,
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nrows=30000)
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labels = pd.read_csv(
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'train/expected.tsv',
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sep='\t',
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on_bad_lines='skip',
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header=None,
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quoting=csv.QUOTE_NONE,
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nrows=30000)
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def data_preprocessing(text):
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return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' '))
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def predict(before, after):
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prediction = dict(Counter(dict(tri[before, after])).most_common(5))
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result = ''
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prob = 0.0
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for key, value in prediction.items():
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prob += value
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result += f'{key}:{value} '
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if prob == 0.0:
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return 'to:0.015 be:0.015 the:0.015 not:0.01 and:0.02 a:0.02 :0.9'
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result += f':{max(1 - prob, 0.01)}'
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return result
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def make_prediction(file):
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data = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
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with open(f'{file}/out.tsv', 'w', encoding='utf-8') as file_out:
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for _, row in data.iterrows():
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before, after = word_tokenize(data_preprocessing(str(row[6]))), word_tokenize(data_preprocessing(str(row[7])))
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if len(before) < 3 or len(after) < 3:
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prediction = 'to:0.015 be:0.015 the:0.015 not:0.01 and:0.02 a:0.02 :0.9'
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else:
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prediction = predict(before[-1], after[0])
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file_out.write(prediction + '\n')
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train = train[[6, 7]]
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train = pd.concat([train, labels], axis=1)
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train['line'] = train[6] + train[0] + train[7]
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tri = defaultdict(lambda: defaultdict(lambda: 0))
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rows = train.iterrows()
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rows_len = len(train)
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for index, (_, row) in enumerate(rows):
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text = data_preprocessing(str(row['line']))
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words = word_tokenize(text)
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for word_1, word_2, word_3 in tris(words, pad_right=True, pad_left=True):
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if word_1 and word_2 and word_3:
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tri[(word_1, word_3)][word_2] += 1
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model_len = len(tri)
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for index, words_1_3 in enumerate(tri):
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count = sum(tri[words_1_3].values())
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for word_2 in tri[words_1_3]:
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tri[words_1_3][word_2] += 0.25
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tri[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
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make_prediction('test-A')
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make_prediction('dev-0')
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14828
test-A/out.tsv
14828
test-A/out.tsv
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