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Word2Vec Keras Text Classifier

Project description

Word2Vec-Keras Text Classifier

Word2Vec-Keras is a simple Word2Vec and LSTM wrapper for text classification.

It combines Gensim Word2Vec model with Keras neural network trhough an Embedding layer as input. The Neural Network contains with LSTM layer

How install

pip3 install git+https://github.com/paoloripamonti/word2vec-keras

Usage

from word2vec_keras import Word2VecKeras

model = Word2VecKeras()

model.train(x_train, y_train)

Train Word2Vec and Keras models. The Keras model has EralyStopping callback for stopping training after 6 epochs that not improve accuracy.

Train parameters:

  • x_train: list of raw sentences, no text cleaning will be perfomed
  • y_train: list of labels
  • w2v_size: (Default: 300) Word2Vec - Dimensionality of the word vectors
  • w2v_window: (Default: 5) Word2Vec - Maximum distance between the current and predicted word within a sentence.
  • w2v_min_count: (Default: 1) Word2Vec - Ignores all words with total frequency lower than this.
  • w2v_epochs: (Default: 100) Word2Vec - Number of iterations (epochs) over the corpus.
  • k_max_sequence_len: (Default: 500) Keras - Maximum length of all sequences
  • k_batch_size:(Default: 128) Keras - Number of samples per gradient update
  • k_epochs:(Default: 32) Keras - Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided
  • k_lstm_neurons: (Default: 128) Keras - LSTM neurons per layer
  • k_hidden_layer_neurons: (Default: [128, 64, 32]) Keras - Number of Dense layers after LSTM layer.
  • verbose: (Default: 1) Keras- 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch

Evaluate

model.evaluate(x_test, y_test)

Evaluate model

Evaluate parameters:

  • x_test: list of raw sentences, no text cleaning will be perfomed
  • y_test: list of labels

Evaluate result:

  • Return a dictionary with ACCURAY, CLASSIFICATION_REPORT and CONFUSION_MATRIX

Predict

model.predict('lorem ipsum dolor sit amet consectetur adipiscing elit...', threshold=0.6)

Make prediction of give text

Predict parameters:

  • x_text: Raw text, no text cleaning will be perfomed
  • threshold: (Default: 0.0) Cut-off threshold, if confidence il less than given value return OTHER as label

Predict result:

  • Return a dictionary with LABEL, CONFIDENCE and ELAPSED_TIME, i.e. {label: LABEL, confidence: CONFIDENCE, elapsed_time: TIME}

Save & load model

model.save('/path/model.tar.gz')

Save model as compressed tar.gz file that contains several utility pickles, keras model and Word2Vec model

model = Word2VecKeras()

model.load('/path/model.tar.gz')

Load model from saved tar.gz file

Example

from sklearn.datasets import fetch_20newsgroups
from word2vec_keras import Word2VecKeras
from pprint import pprint

# fetch the dataset using scikit-learn
categories = ['alt.atheism', 'soc.religion.christian',
              'comp.graphics', 'sci.med']

train_b = fetch_20newsgroups(subset='train',
                             categories=categories, shuffle=True, random_state=42)
test_b = fetch_20newsgroups(subset='test',
                            categories=categories, shuffle=True, random_state=42)

print('size of training set: %s' % (len(train_b['data'])))
print('size of validation set: %s' % (len(test_b['data'])))
print('classes: %s' % (train_b.target_names))

x_train = train_b.data
y_train = [train_b.target_names[idx] for idx in train_b.target]
x_test = test_b.data
y_test = [train_b.target_names[idx] for idx in test_b.target]

model = Word2VecKeras()
model.train(x_train, y_train)

pprint(model.evaluate(x_test, y_test))

model.save('./model.tar.gz')

Open In Colab

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