'0:last_2_char': word, # Take last 1 characters '0:last_3_char': word, # Take last 2 characters '0:syllable' : word, # just take the entire syllable # First, create features about the current word NB: A word here is used for each syllable!įeatures: a set of features for the inspected word/syllable """Given a sentence and the word index, add a feature dictionary toĮach word that can help with predicting its label. """Given a sentence, this function will add features to each of its words List: of sentence, now with each word accompanied by a feature dictionary Sent (string): of a sentence with words and labels """Given a sentence, this function will return a list of all labels in the given sentence. Lists: X and y sets to train/test the model on Syllable_label_list (list): of syllables and their labels Two sets, one with syllables and their features (called X) and one with labels (called y). Given is a list with a syllable and its label, returned are """Intermediate function to turn a dataframe with syllables and labels into proper Y, y_pred, labels = sorted_labels, output_dict = True)ĭef convert_text_to_feature_sets( self, syllable_label_list): X (numpy array): with training or testing examples """ Predicts labels y given the model and examples X. predict_model( crf_model, predictee_X, predictee_y) convert_text_to_feature_sets( predictee_text) convert_text_to_feature_sets( predictor_text)Ĭrf_model = self. remove_space_from_syllable_sequence( predictee_text) remove_space_from_syllable_sequence( predictor_text) get( 'Pickle', 'path_sequence_labels'), predictee_pickle) get( 'Pickle', 'path_sequence_labels'), predictor_pickle) Out_tup = != 'space']ĭef custom_prediction( self, predictor_pickle, predictee_pickle): kfold_model( text, X, y, 5)ĭef remove_space_from_syllable_sequence( self, given_list): # Perform kfold to check if we don't have any overfitting remove_space_from_syllable_sequence( text) get( 'Pickle', 'path_sequence_labels'), FLAGS. # self.custom_prediction('HEX_ELE-all.pickle', 'SEN-aga.pickle') # feature-based sequence labelling: conditional random fields ''' This class handels Conditional Random Fields sequence labeling. model_selection import KFoldįrom sklearn_crfsuite import metrics as crf_metricsįrom pedecerto. model_selection import RandomizedSearchCVįrom sklearn. model_selection import train_test_splitįrom sklearn. You will see that the title of the quiz is the same as the one you see on this quiz except the word Copy has been added to the end of the title.From sklearn. You will see a copy of this quiz in your Socrative account.A new browser tab will open to your Socrative quizzes list in your account.The quiz on this website as well as the original author's Socrative account will not be affected by any changes you make to the copy of this quiz in your own Socrative account. Please note that this is an actual duplicate copy of this quiz that is put in your account and yours to make futher edits. Click the above button titled Copy Quiz to my Socrative Account to initiate a copy of this quiz into your Socrative teacher account.Note: Don't have a Socrative teacher account? Sign up for free!.
How the Copy Quiz to my Socrative account works (Video tutorial):
Activity Type(s): Entrance Ticket, Exit Ticket, Homework, Practice Add/Change.
Keywords: Poetic Meter, Scansion, Elision Add/Change.Grades: Grade 6, Grade 7, Grade 8, Grade 9, Grade 10, Grade 11, Grade 12 Add/Change.
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