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# In[1]:
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import os
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import lzma
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folders = ["./challenging-america-word-gap-prediction/dev-0",
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"./challenging-america-word-gap-prediction/test-A",
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"./challenging-america-word-gap-prediction/train"]
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for folder in folders:
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for file in os.listdir(folder):
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if file.endswith(".tsv.xz"):
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file_path = os.path.join(folder, file)
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output_path = os.path.splitext(file_path)[0] # Remove the .xz extension
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with lzma.open(file_path, "rb") as compressed_file:
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with open(output_path, "wb") as output_file:
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output_file.write(compressed_file.read())
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# In[5]:
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import nltk
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nltk.download('punkt')
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# In[ ]:
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import pandas as pd
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in_df = pd.read_csv('./challenging-america-word-gap-prediction/train/in.tsv', sep='\t', header=None, on_bad_lines='warn')
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expected_df = pd.read_csv('./challenging-america-word-gap-prediction/train/expected.tsv', sep='\t', header=None, on_bad_lines='warn')
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print("in_df:")
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print(in_df.head())
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print("\nexpected_df:")
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print(expected_df.head())
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print("\nhate_speech_info_df:")
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print("\nin_df info:")
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print(in_df.info())
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print("\nexpected_df info:")
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print(expected_df.info())
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# In[2]:
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columns_to_drop = [0, 1, 2, 3, 4, 5]
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in_df.drop(columns_to_drop, axis=1, inplace=True)
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in_df.columns = ['text_1', 'text_2']
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# In[3]:
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print(in_df.head())
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# In[4]:
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in_df['text_1'].fillna('', inplace=True)
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in_df['text_2'].fillna('', inplace=True)
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# In[5]:
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from tqdm import tqdm
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def replace_newline(text):
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if isinstance(text, str):
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return text.replace('\\n', ' ')
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return text
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tqdm.pandas(desc="Replacing '\\n' in 'text_1'")
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in_df['text_1'] = in_df['text_1'].progress_apply(replace_newline)
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tqdm.pandas(desc="Replacing '\\n' in 'text_2'")
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in_df['text_2'] = in_df['text_2'].progress_apply(replace_newline)
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# In[6]:
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print(in_df.head())
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# In[ ]:
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import nltk
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from nltk.corpus import stopwords
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from nltk.stem import PorterStemmer
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from nltk.tokenize import word_tokenize
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import string
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from tqdm import tqdm
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nltk.download('punkt')
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nltk.download('stopwords')
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stop_words = set(stopwords.words('english'))
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stemmer = PorterStemmer()
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def preprocess_text(text):
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text = text.lower()
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# Remove punctuation
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text = text.translate(str.maketrans('', '', string.punctuation))
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words = word_tokenize(text)
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# Remove stopwords and stem the words
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words = [stemmer.stem(word) for word in words if word not in stop_words]
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text = ' '.join(words)
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return text
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tqdm.pandas(desc="Processing 'text_1'")
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in_df['text_1'] = in_df['text_1'].progress_apply(preprocess_text)
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tqdm.pandas(desc="Processing 'text_2'")
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in_df['text_2'] = in_df['text_2'].progress_apply(preprocess_text)
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# In[8]:
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in_df.to_csv('preprocessed_text1_text2.tsv', sep='\t', index=False)
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# In[9]:
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def concatenate_texts(row):
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return str(row['text_1']) + ' <MASK> ' + str(row['text_2'])
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tqdm.pandas(desc="Concatenating 'text_1' and 'text_2'")
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in_df['text'] = in_df.progress_apply(concatenate_texts, axis=1)
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in_df.drop(['text_1', 'text_2'], axis=1, inplace=True)
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# In[31]:
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from tqdm import tqdm
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tqdm.pandas()
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in_df = pd.read_csv('preprocessed_text1_text2.tsv', sep='\t')
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expected_df = pd.read_csv('./challenging-america-word-gap-prediction/train/expected.tsv', sep='\t', header=None, on_bad_lines='warn')
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expected_df.columns = ['expected_word']
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in_df = pd.concat([in_df, expected_df], axis=1)
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def concatenate_texts(row):
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return str(row['text_1']) + ' ' + str(row['expected_word']) + ' ' + str(row['text_2'])
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# Apply the function to each row and show progress
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in_df['unmasked_text'] = in_df.progress_apply(concatenate_texts, axis=1)
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# In[35]:
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in_df['unmasked_text'].to_csv('preprocessed_text_join_unmask.tsv', sep='\t', index=False)
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# In[10]:
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in_df.to_csv('preprocessed_text_join_mask.tsv', sep='\t', index=False)
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# In[5]:
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import pandas as pd
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in_df = pd.read_csv('preprocessed_text_join_mask.tsv', sep='\t')
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print(in_df.head())
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# In[6]:
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print(in_df.head())
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# In[1]:
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from abc import ABC, abstractmethod
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from collections import Counter
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from nltk.util import ngrams
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from nltk.tokenize import word_tokenize
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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import numpy as np
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class Model(ABC):
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def __init__(self, UNK_token= '<MASK>', smoothing_parameter=0.5):
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self.UNK_token = UNK_token
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self.smoothing_parameter = smoothing_parameter
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@abstractmethod
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def train(self, corpus):
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pass
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@abstractmethod
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def predict(self, text):
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pass
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class InterpolatedModel(Model):
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def train(self, corpus, smoothing_parameter=0.5, vocab=15000):
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self.smoothing_parameter=smoothing_parameter
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if corpus.empty:
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raise ValueError("The corpus is empty.")
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corpus = list(set(corpus))
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tokens = []
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for i, sentence in enumerate(corpus):
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sentence_tokens = word_tokenize(sentence)
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# Add padding to sentences that are less than three words long
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while len(sentence_tokens) < 3:
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sentence_tokens.append(self.UNK_token)
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tokens.append(sentence_tokens)
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if i % 1000 == 0:
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print(f'Tokenizing sentence {i} of {len(corpus)}')
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self.unigram_counts = Counter()
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self.bigram_counts = Counter()
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self.trigram_counts = Counter()
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for i, sentence in enumerate(tokens):
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self.unigram_counts.update(sentence)
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self.bigram_counts.update(list(ngrams(sentence, 2, pad_left=True, pad_right=True)))
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self.trigram_counts.update(list(ngrams(sentence, 3, pad_left=True, pad_right=True)))
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if i % 1000 == 0:
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print(f'Counting ngrams in sentence {i} of {len(tokens)}')
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self.unigram_counts = Counter(dict(self.unigram_counts.most_common(vocab)))
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self.vocab_size = len(self.unigram_counts)
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def probability(self, text, word):
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tokens = word_tokenize(text)
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if len(tokens) < 2:
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# Use a unigram model if the input text is only one word
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return self.unigram_counts[word] / self.vocab_size if word in self.unigram_counts else 0
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else:
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# Use a bigram/trigram model if the input text is at least two words
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previous_bigram = tuple(tokens[-2:])
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unigram_prob = self.unigram_counts[word] / self.vocab_size if word in self.unigram_counts else 0
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bigram_prob = self.bigram_counts.get((previous_bigram[1], word), 0) / self.unigram_counts.get(previous_bigram[1], 1)
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trigram_prob = self.trigram_counts.get((*previous_bigram, word), 0) / self.bigram_counts.get(previous_bigram, 1)
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return self.smoothing_parameter * unigram_prob + (1 - 2 * self.smoothing_parameter) * bigram_prob + (1 - self.smoothing_parameter) * trigram_prob
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def perplexity(self, sentence):
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tokens = word_tokenize(sentence)
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probabilities = [self.probability(' '.join(tokens[:i]), tokens[i]) for i in range(1, len(tokens))]
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log_probabilities = [np.log2(p) if p > 0 else float('-inf') for p in probabilities]
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average_log_probability = np.mean(log_probabilities)
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return np.power(2, -average_log_probability)
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def predict(self, text):
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tokens = word_tokenize(text)
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if len(tokens) < 1:
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return None
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elif len(tokens) < 2:
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# Use a unigram model if the input text is only one word
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probabilities = {word: self.unigram_counts[word] / self.vocab_size if word in self.unigram_counts else 0 for word in self.unigram_counts.keys()}
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else:
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# Use a bigram/trigram model if the input text is at least two words
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previous_bigram = tuple(tokens[-2:])
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probabilities = {}
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for word in self.unigram_counts.keys():
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unigram_prob = self.unigram_counts[word] / self.vocab_size if word in self.unigram_counts else 0
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bigram_prob = self.bigram_counts.get((previous_bigram[1], word), 0) / self.unigram_counts.get(previous_bigram[1], 1)
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trigram_prob = self.trigram_counts.get((*previous_bigram, word), 0) / self.bigram_counts.get(previous_bigram, 1)
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probabilities[word] = self.smoothing_parameter * unigram_prob + (1 - 2 * self.smoothing_parameter) * bigram_prob + (1 - self.smoothing_parameter) * trigram_prob
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return max(probabilities, key=probabilities.get)
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# In[32]:
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in_df = pd.read_csv('preprocessed_text_join_unmask.tsv', sep='\t')
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expected_df = pd.read_csv('./challenging-america-word-gap-prediction/train/expected.tsv', sep='\t', header=None, on_bad_lines='warn')
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df = pd.concat([in_df, expected_df], axis=1)
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df.columns = ['text', 'expected_word']
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quarter = len(df) // 4
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df = df.iloc[:quarter]
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train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)
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model = InterpolatedModel()
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model.train(train_df['text'], smoothing_parameter=0.4, vocab=60000)
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# In[33]:
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import pickle
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with open('trained_model_quarter_sp04_vocab60k.pkl', 'wb') as file:
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pickle.dump(model, file)
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# In[ ]:
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import pickle
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with open('trained_model_quarter_sp04_vocab60k.pkl', 'rb') as file:
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model = pickle.load(file)
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# Smoothing = 0.4
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# In[14]:
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import pandas as pd
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df = pd.read_csv('preprocessed_text_join_mask.tsv', sep='\t')
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expected_df = pd.read_csv('./challenging-america-word-gap-prediction/train/expected.tsv', sep='\t', header=None)
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expected_df.columns = ['expected_word']
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for index, row in df.head(100).iterrows():
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sentence = row['text']
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expected_word = expected_df.loc[index, 'expected_word']
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words = sentence.split()
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if '<MASK>' in words:
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mask_index = words.index('<MASK>')
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start_index = max(0, mask_index - 2)
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context = ' '.join(words[start_index:mask_index])
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predicted_word = model.predict(context)
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if predicted_word == expected_word:
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print(f'Index: {index} Predicted Word: {predicted_word}, Expected Word: {expected_word}')
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# In[21]:
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import pandas as pd
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import csv
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# Load the data
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in_df = pd.read_csv('./challenging-america-word-gap-prediction/dev-0/in.tsv', sep='\t', header=None, on_bad_lines='warn')
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expected_df = pd.read_csv('./challenging-america-word-gap-prediction/dev-0/expected.tsv', sep='\t', header=None, on_bad_lines='warn', quoting=csv.QUOTE_NONE)
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columns_to_drop = [0, 1, 2, 3, 4, 5]
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in_df.drop(columns_to_drop, axis=1, inplace=True)
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in_df.columns = ['text_1', 'text_2']
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in_df['text_2'].fillna('', inplace=True)
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# In[22]:
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from tqdm import tqdm
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def replace_newline(text):
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if isinstance(text, str):
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return text.replace('\\n', ' ')
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return text
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tqdm.pandas(desc="Replacing '\\n' in 'text_1'")
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in_df['text_1'] = in_df['text_1'].progress_apply(replace_newline)
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tqdm.pandas(desc="Replacing '\\n' in 'text_2'")
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in_df['text_2'] = in_df['text_2'].progress_apply(replace_newline)
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# In[23]:
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import nltk
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from nltk.corpus import stopwords
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from nltk.stem import PorterStemmer
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from nltk.tokenize import word_tokenize
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import string
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from tqdm import tqdm
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nltk.download('punkt')
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nltk.download('stopwords')
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stop_words = set(stopwords.words('english'))
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stemmer = PorterStemmer()
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def preprocess_text(text):
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text = text.lower()
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text = text.translate(str.maketrans('', '', string.punctuation))
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words = word_tokenize(text)
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words = [stemmer.stem(word) for word in words if word not in stop_words]
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text = ' '.join(words)
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return text
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tqdm.pandas(desc="Processing 'text_1'")
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in_df['text_1'] = in_df['text_1'].progress_apply(preprocess_text)
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tqdm.pandas(desc="Processing 'text_2'")
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in_df['text_2'] = in_df['text_2'].progress_apply(preprocess_text)
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# In[24]:
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in_df.to_csv('preprocessed_dev_text1_text2.tsv', sep='\t', index=False)
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# In[25]:
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def concatenate_texts(row):
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return str(row['text_1']) + ' <MASK> ' + str(row['text_2'])
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tqdm.pandas(desc="Concatenating 'text_1' and 'text_2'")
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in_df['text'] = in_df.progress_apply(concatenate_texts, axis=1)
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in_df.drop(['text_1', 'text_2'], axis=1, inplace=True)
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# In[26]:
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in_df.to_csv('preprocessed_dev_text_join_mask.tsv', sep='\t', index=False)
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# In[34]:
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import csv
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df = pd.read_csv('preprocessed_dev_text_join_mask.tsv', sep='\t')
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expected_df = pd.read_csv('./challenging-america-word-gap-prediction/dev-0/expected.tsv', sep='\t', header=None, quoting=csv.QUOTE_NONE)
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expected_df.columns = ['expected_word']
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for index, row in df.head(100).iterrows():
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sentence = row['text']
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expected_word = expected_df.loc[index, 'expected_word']
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words = sentence.split()
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if '<MASK>' in words:
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mask_index = words.index('<MASK>')
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start_index = max(0, mask_index - 2)
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context = ' '.join(words[start_index:mask_index])
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predicted_word = model.predict(context)
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if predicted_word == expected_word:
|
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print(f'Index: {index} Predicted Word: {predicted_word}, Expected Word: {expected_word}')
|
||||
|
||||
|
||||
# In[36]:
|
||||
|
||||
|
||||
import csv
|
||||
df = pd.read_csv('preprocessed_dev_text_join_mask.tsv', sep='\t')
|
||||
|
||||
expected_df = pd.read_csv('./challenging-america-word-gap-prediction/dev-0/expected.tsv', sep='\t', header=None, quoting=csv.QUOTE_NONE)
|
||||
expected_df.columns = ['expected_word']
|
||||
|
||||
df['Word'] = None
|
||||
|
||||
for index, row in df.iterrows():
|
||||
sentence = row['text']
|
||||
words = sentence.split()
|
||||
if '<MASK>' in words:
|
||||
expected_word = expected_df.loc[index, 'expected_word']
|
||||
mask_index = words.index('<MASK>')
|
||||
start_index = max(0, mask_index - 2)
|
||||
context = ' '.join(words[start_index:mask_index])
|
||||
predicted_word = model.predict(context)
|
||||
df.loc[index, 'Word'] = predicted_word
|
||||
if predicted_word == expected_word:
|
||||
print(f'Index: {index} Predicted Word: {predicted_word}, Expected Word: {expected_word}')
|
||||
|
||||
df['Word'].to_csv('./challenging-america-word-gap-prediction/dev-0/out.tsv', sep='\t', index=False)
|
||||
|
||||
|
||||
# In[37]:
|
||||
|
||||
|
||||
import pandas as pd
|
||||
|
||||
in_df = pd.read_csv('./challenging-america-word-gap-prediction/test-A/in.tsv', sep='\t', header=None, on_bad_lines='warn')
|
||||
|
||||
columns_to_drop = [0, 1, 2, 3, 4, 5]
|
||||
in_df.drop(columns_to_drop, axis=1, inplace=True)
|
||||
|
||||
in_df.columns = ['text_1', 'text_2']
|
||||
in_df['text_2'].fillna('', inplace=True)
|
||||
|
||||
|
||||
# In[38]:
|
||||
|
||||
|
||||
from tqdm import tqdm
|
||||
|
||||
def replace_newline(text):
|
||||
if isinstance(text, str):
|
||||
return text.replace('\\n', ' ')
|
||||
return text
|
||||
|
||||
tqdm.pandas(desc="Replacing '\\n' in 'text_1'")
|
||||
in_df['text_1'] = in_df['text_1'].progress_apply(replace_newline)
|
||||
|
||||
tqdm.pandas(desc="Replacing '\\n' in 'text_2'")
|
||||
in_df['text_2'] = in_df['text_2'].progress_apply(replace_newline)
|
||||
|
||||
|
||||
# In[39]:
|
||||
|
||||
|
||||
import nltk
|
||||
from nltk.corpus import stopwords
|
||||
from nltk.stem import PorterStemmer
|
||||
from nltk.tokenize import word_tokenize
|
||||
import string
|
||||
from tqdm import tqdm
|
||||
|
||||
nltk.download('punkt')
|
||||
nltk.download('stopwords')
|
||||
|
||||
stop_words = set(stopwords.words('english'))
|
||||
stemmer = PorterStemmer()
|
||||
|
||||
def preprocess_text(text):
|
||||
text = text.lower()
|
||||
text = text.translate(str.maketrans('', '', string.punctuation))
|
||||
words = word_tokenize(text)
|
||||
words = [stemmer.stem(word) for word in words if word not in stop_words]
|
||||
text = ' '.join(words)
|
||||
|
||||
return text
|
||||
|
||||
tqdm.pandas(desc="Processing 'text_1'")
|
||||
in_df['text_1'] = in_df['text_1'].progress_apply(preprocess_text)
|
||||
|
||||
|
||||
tqdm.pandas(desc="Processing 'text_2'")
|
||||
in_df['text_2'] = in_df['text_2'].progress_apply(preprocess_text)
|
||||
|
||||
|
||||
# In[40]:
|
||||
|
||||
|
||||
in_df.to_csv('preprocessed_test_text1_text2.tsv', sep='\t', index=False)
|
||||
|
||||
|
||||
# In[41]:
|
||||
|
||||
|
||||
def concatenate_texts(row):
|
||||
return str(row['text_1']) + ' <MASK> ' + str(row['text_2'])
|
||||
|
||||
tqdm.pandas(desc="Concatenating 'text_1' and 'text_2'")
|
||||
in_df['text'] = in_df.progress_apply(concatenate_texts, axis=1)
|
||||
|
||||
in_df.drop(['text_1', 'text_2'], axis=1, inplace=True)
|
||||
|
||||
|
||||
# In[42]:
|
||||
|
||||
|
||||
in_df.to_csv('preprocessed_test_text_join_mask.tsv', sep='\t', index=False)
|
||||
|
||||
|
||||
# In[43]:
|
||||
|
||||
|
||||
df = pd.read_csv('preprocessed_dev_text_join_mask.tsv', sep='\t')
|
||||
|
||||
df['Word'] = None
|
||||
|
||||
for index, row in df.iterrows():
|
||||
sentence = row['text']
|
||||
words = sentence.split()
|
||||
if '<MASK>' in words:
|
||||
mask_index = words.index('<MASK>')
|
||||
start_index = max(0, mask_index - 2)
|
||||
context = ' '.join(words[start_index:mask_index])
|
||||
predicted_word = model.predict(context)
|
||||
df.loc[index, 'Word'] = predicted_word
|
||||
|
||||
|
||||
df['Word'].to_csv('./challenging-america-word-gap-prediction/test-A/out.tsv', sep='\t', index=False)
|
||||
|
||||
File diff suppressed because it is too large
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Reference in New Issue