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Tag: Duplicate
Artificial Intelligence
The natural way to classify duplicate questions on Quora using deep learning techniques involves building a neural network model that identifies patterns in the data and makes predictions based on these patterns. To do this, we will use the Keras library in Python, which provides an interface for building and training neural networks. “`python from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical from sklearn.model_selection import train_test_split import numpy as np # Load your data here train_questions = […] test_questions = […] # Create a tokenizer to split the text into words tokenizer = Tokenizer(num_words=5000) tokenizer.fit_on_texts(train_questions) # Convert the questions into sequences of tokens train_sequences = [] for question in train_questions: sequence = tokenizer.texts_to_sequences([question])[0] if len(sequence) > 1: for i in range(1, len(sequence)): train_sequences.append(list(sequence[:i])) test_sequences = [] for question in test_questions: sequence = tokenizer.texts_to_sequences([question])[0] if len(sequence) > 1: for i in range(1, len(sequence)): test_sequences.append(list(sequence[:i])) # Pad the sequences to have the same length max_length = 10 padded_train = pad_sequences(train_sequences, maxlen=max_length) padded_test = pad_sequences(test_sequences, maxlen=max_length) # One-hot encode the labels for training and testing num_classes = 2 train_labels = […] test_labels = […] one_hot_train = to_categorical(train_labels, num_classes) one_hot_test = to_categorical(test_labels, num_classes) # Split data into training set and validation set train_data, val_data, train_labels, val_labels = train_test_split(padded_train, one_hot_train, test_size=0.2, random_state=42) # Define the model architecture from keras.models import Sequential from keras.layers import Embedding, Dropout, Flatten model = Sequential() model.add(Embedding(input_dim=5000, output_dim=128, input_length=max_length)) model.add(Flatten()) model.add(Dropout(0.2)) model.add(Dense(num_classes, activation=’softmax’)) # Compile the model model.compile(loss=’categorical_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’]) # Train the model model.fit(train_data, train_labels, epochs=5, batch_size=32, validation_data=(val_data, val_labels)) # Evaluate the model loss, accuracy = model.evaluate(test_data, test_labels) print(‘Test loss:’, loss) print(‘Test accuracy:’, accuracy)
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November 17, 2024
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![The natural way to classify duplicate questions on Quora using deep learning techniques involves building a neural network model that identifies patterns in the data and makes predictions based on these patterns. To do this, we will use the Keras library in Python, which provides an interface for building and training neural networks. “`python from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical from sklearn.model_selection import train_test_split import numpy as np # Load your data here train_questions = […] test_questions = […] # Create a tokenizer to split the text into words tokenizer = Tokenizer(num_words=5000) tokenizer.fit_on_texts(train_questions) # Convert the questions into sequences of tokens train_sequences = [] for question in train_questions: sequence = tokenizer.texts_to_sequences([question])[0] if len(sequence) > 1: for i in range(1, len(sequence)): train_sequences.append(list(sequence[:i])) test_sequences = [] for question in test_questions: sequence = tokenizer.texts_to_sequences([question])[0] if len(sequence) > 1: for i in range(1, len(sequence)): test_sequences.append(list(sequence[:i])) # Pad the sequences to have the same length max_length = 10 padded_train = pad_sequences(train_sequences, maxlen=max_length) padded_test = pad_sequences(test_sequences, maxlen=max_length) # One-hot encode the labels for training and testing num_classes = 2 train_labels = […] test_labels = […] one_hot_train = to_categorical(train_labels, num_classes) one_hot_test = to_categorical(test_labels, num_classes) # Split data into training set and validation set train_data, val_data, train_labels, val_labels = train_test_split(padded_train, one_hot_train, test_size=0.2, random_state=42) # Define the model architecture from keras.models import Sequential from keras.layers import Embedding, Dropout, Flatten model = Sequential() model.add(Embedding(input_dim=5000, output_dim=128, input_length=max_length)) model.add(Flatten()) model.add(Dropout(0.2)) model.add(Dense(num_classes, activation=’softmax’)) # Compile the model model.compile(loss=’categorical_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’]) # Train the model model.fit(train_data, train_labels, epochs=5, batch_size=32, validation_data=(val_data, val_labels)) # Evaluate the model loss, accuracy = model.evaluate(test_data, test_labels) print(‘Test loss:’, loss) print(‘Test accuracy:’, accuracy)](https://i0.wp.com/blogs.rstudio.com/tensorflow/posts/2018-01-09-keras-duplicate-questions-quora/keras-duplicate-questions-quora.png?w=768&resize=768,0&ssl=1 "The natural way to classify duplicate questions on Quora using deep learning techniques involves building a neural network model that identifies patterns in the data and makes predictions based on these patterns. To do this, we will use the Keras library in Python, which provides an interface for building and training neural networks. “`python from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical from sklearn.model_selection import train_test_split import numpy as np # Load your data here train_questions = […] test_questions = […] # Create a tokenizer to split the text into words tokenizer = Tokenizer(num_words=5000) tokenizer.fit_on_texts(train_questions) # Convert the questions into sequences of tokens train_sequences = [] for question in train_questions: sequence = tokenizer.texts_to_sequences([question])[0] if len(sequence) > 1: for i in range(1, len(sequence)): train_sequences.append(list(sequence[:i])) test_sequences = [] for question in test_questions: sequence = tokenizer.texts_to_sequences([question])[0] if len(sequence) > 1: for i in range(1, len(sequence)): test_sequences.append(list(sequence[:i])) # Pad the sequences to have the same length max_length = 10 padded_train = pad_sequences(train_sequences, maxlen=max_length) padded_test = pad_sequences(test_sequences, maxlen=max_length) # One-hot encode the labels for training and testing num_classes = 2 train_labels = […] test_labels = […] one_hot_train = to_categorical(train_labels, num_classes) one_hot_test = to_categorical(test_labels, num_classes) # Split data into training set and validation set train_data, val_data, train_labels, val_labels = train_test_split(padded_train, one_hot_train, test_size=0.2, random_state=42) # Define the model architecture from keras.models import Sequential from keras.layers import Embedding, Dropout, Flatten model = Sequential() model.add(Embedding(input_dim=5000, output_dim=128, input_length=max_length)) model.add(Flatten()) model.add(Dropout(0.2)) model.add(Dense(num_classes, activation=’softmax’)) # Compile the model model.compile(loss=’categorical_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’]) # Train the model model.fit(train_data, train_labels, epochs=5, batch_size=32, validation_data=(val_data, val_labels)) # Evaluate the model loss, accuracy = model.evaluate(test_data, test_labels) print(‘Test loss:’, loss) print(‘Test accuracy:’, accuracy)")