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21038
dev-0/out.tsv
21038
dev-0/out.tsv
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245
run.py
245
run.py
@ -1,7 +1,21 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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# In[25]:
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import tensorflow as tf
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from tensorflow.keras.preprocessing.text import Tokenizer
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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from tensorflow.keras.models import Model
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from tensorflow.keras.layers import Input, Embedding, Dense, Lambda
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from gensim.models import Word2Vec
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import numpy as np
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from sklearn.model_selection import train_test_split
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from tensorflow.keras.callbacks import EarlyStopping
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# In[26]:
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import lzma
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@ -14,123 +28,179 @@ def read_xz_file(file_path):
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return data
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# In[2]:
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# In[27]:
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def read_tsv_file(file_path):
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data = []
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with open(file_path, 'r', encoding='utf-8') as file:
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for line in file:
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line = line.strip().split('\t') # Rozdziel linie na elementy za pomocą tabulatora
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data.append(line) # Dodaj elementy do listy danych
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line = line.strip().split('\t')
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data.append(line)
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return data
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# In[3]:
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# In[28]:
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file_path = "train\\in.tsv.xz"
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# In[4]:
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# In[29]:
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data = read_xz_file(file_path)
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# In[5]:
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# In[30]:
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expected = read_tsv_file("train\\expected.tsv")
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# In[6]:
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# In[31]:
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corpus_before=[]
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corpus_after=[]
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for i in range(len(data)):
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for i in range(20000):
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corpus_before.append(str(data[i].split("\t")[6]))
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corpus_after.append(str(data[i].split("\t")[7]))
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# In[7]:
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# In[32]:
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for i in range(len(expected)):
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for i in range(20000):
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expected[i] = str(expected[i]).lower()
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# In[8]:
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# In[33]:
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corpus = []
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for i in range(len(expected)):
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for i in range(20000):
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corpus.append(corpus_before[i] + " " + expected[i] + " " + corpus_after[i])
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# In[9]:
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# In[34]:
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from collections import defaultdict
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from nltk import trigrams
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from nltk.tokenize import word_tokenize
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model_trigram = defaultdict(lambda: defaultdict(float))
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dictionary_trigram = set()
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for line in corpus[:200000]:
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tokens = word_tokenize(line)
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for word1, word2, word3 in trigrams(tokens, pad_right=True, pad_left=True):
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if word1 and word2 and word3:
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model_trigram[(word2, word3)][word1] += 1
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model_trigram[(word1, word2)][word3] += 1
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dictionary_trigram.update([word1, word2, word3])
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sentences = [text.split() for text in corpus]
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# In[10]:
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# In[35]:
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from collections import defaultdict
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from nltk import bigrams
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from nltk.tokenize import word_tokenize
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model_bigram = defaultdict(lambda: defaultdict(float))
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dictionary_bigram = set()
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for line in corpus[:200000]:
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tokens = word_tokenize(line)
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for word1, word2 in bigrams(tokens, pad_right=True, pad_left=True):
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if word1 and word2:
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model_bigram[word2][word1] += 1
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model_bigram[word1][word2] += 1
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dictionary_bigram.update([word1, word2])
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word2vec_model = Word2Vec(sentences, vector_size=70, window=5, min_count=1, workers=4)
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word2vec_model.train(sentences, total_examples=word2vec_model.corpus_count, epochs=10)
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# In[11]:
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# In[36]:
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smoothing = 0.0001
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for trio in model_trigram:
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count_sum = sum(model_trigram[trio].values()) + smoothing * len(dictionary_trigram)
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for token in model_trigram[trio]:
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model_trigram[trio][token] = (model_trigram[trio][token] + smoothing) / count_sum
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tokenizer = Tokenizer()
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tokenizer.fit_on_texts(corpus)
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total_words = len(tokenizer.word_index) + 1
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# In[12]:
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# In[37]:
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smoothing = 0.0001
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for trio in model_bigram:
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count_sum = sum(model_bigram[trio].values()) + smoothing * len(dictionary_bigram)
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for token in model_bigram[trio]:
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model_bigram[trio][token] = (model_bigram[trio][token] + smoothing) / count_sum
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input_sequences = []
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output_words = []
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for before, word, after in zip(corpus_before, expected, corpus_after):
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before_tokens = tokenizer.texts_to_sequences([before])[0]
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after_tokens = tokenizer.texts_to_sequences([after])[0]
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word_token = tokenizer.texts_to_sequences([word])[0][0]
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for i in range(1, 6):
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input_seq = before_tokens[-(5-i):] + [word_token] + after_tokens[:i]
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input_sequences.append(pad_sequences([input_seq], maxlen=5, padding='pre')[0])
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output_words.append(word_token)
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# In[19]:
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input_sequences = np.array(input_sequences)
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output_words = np.array(output_words)
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# In[38]:
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embedding_matrix = np.zeros((total_words, 70))
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for word, i in tokenizer.word_index.items():
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if word in word2vec_model.wv:
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embedding_matrix[i] = word2vec_model.wv[word]
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# In[39]:
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X_train, X_val, y_train, y_val = train_test_split(input_sequences, output_words, test_size=0.2, random_state=33)
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# In[40]:
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input_layer = Input(shape=(5,))
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embedding_layer = Embedding(total_words, 70, trainable=False)(input_layer)
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sum_layer = Lambda(lambda x: tf.reduce_sum(x, axis=1))(embedding_layer)
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dense_layer1 = Dense(128, activation='relu')(sum_layer)
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dense_layer2 = Dense(64, activation='relu')(dense_layer1)
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linear_layer = Dense(70, activation='relu')(dense_layer2)
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output_layer = Dense(total_words, activation='softmax')(linear_layer)
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# In[41]:
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model = Model(inputs=input_layer, outputs=output_layer)
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model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
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# In[42]:
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model.fit(X_train, y_train, epochs=15, verbose=2, validation_data=(X_val, y_val), callbacks=[early_stopping])
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# In[43]:
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model.save('ngram_model_20k.keras')
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# In[44]:
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def predict_next_words(model, tokenizer, text, top_n=1):
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sequence = tokenizer.texts_to_sequences([text])[0]
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padded_sequence = pad_sequences([sequence], maxlen=5, padding='pre')
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predictions = model.predict(padded_sequence)[0]
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top_indices = np.argsort(predictions)[-top_n:][::-1]
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top_words = [tokenizer.index_word[index] for index in top_indices]
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top_probabilities = [predictions[index] for index in top_indices]
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return list(zip(top_words, top_probabilities))
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# In[45]:
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from collections import Counter
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default = "the:0.10000 of:0.05000 and:0.01000 to:0.01000 in:0.01000 a:0.01000 :0.81000"
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data = read_xz_file("dev-0\\in.tsv.xz")
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corpus_before=[]
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@ -141,68 +211,11 @@ for i in range(len(data)):
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with open("dev-0\\out.tsv", "w", encoding="utf-8") as output:
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for text in corpus_before:
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tokens = word_tokenize(text)
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prediction = ""
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if len(tokens) >= 3:
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results = dict(model_trigram[(tokens[0], tokens[1])])
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if results:
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prediction = ' '.join(f"{term}:{round(prob, 5)}" for term, prob in Counter(results).most_common(6))
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if prediction == "":
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bigram_results = dict(model_bigram[tokens[0]])
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if bigram_results:
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prediction = ' '.join(f"{term}:{round(prob, 5)}" for term, prob in Counter(bigram_results).most_common(6))
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if prediction == "":
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prediction = default
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output.write(str(prediction.replace("\n", "").strip() + "\n"))
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# In[ ]:
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predictions = predict_next_words(model, tokenizer, text)
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result = " ".join([f"{word}:{round(probability,5)}" for word, probability in predictions])
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output.write(str(result.replace("\n", "").strip() + "\n"))
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# In[22]:
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from collections import Counter
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default = "the:0.10000 of:0.05000 and:0.01000 to:0.01000 in:0.01000 a:0.01000 :0.81000"
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data = read_xz_file("test-A\\in.tsv.xz")
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corpus_before=[]
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corpus_after=[]
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for i in range(len(data)):
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corpus_before.append(str(data[i].split("\t")[6]))
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corpus_after.append(str(data[i].split("\t")[7]))
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with open("test-A\\out.tsv", "w", encoding="utf-8") as output:
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for text in corpus_before:
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tokens = word_tokenize(text)
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prediction = ""
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if len(tokens) >= 3:
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results = dict(model_trigram[(tokens[0], tokens[1])])
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if results:
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prediction = ' '.join(f"{term}:{round(prob, 5)}" for term, prob in Counter(results).most_common(6))
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if prediction == "":
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bigram_results = dict(model_bigram[tokens[0]])
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if bigram_results:
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prediction = ' '.join(f"{term}:{round(prob, 5)}" for term, prob in Counter(bigram_results).most_common(6))
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if prediction == "":
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prediction = default
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output.write(str(prediction.replace("\n", "").strip() + "\n"))
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# In[ ]:
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