def __init__(self, *args, **kwargs):
'''
field is 'sentence' by default, but can be e.g. pos_sentence.
'''
self.field = kwargs.pop('field', 'sentence')
super(LanguageModel, self).__init__(*args, **kwargs)
self.class_to_code = {'VBZ': 0, 'VBP': 1}
self.inflect_verb, _ = gen_inflect_from_vocab(self.vocab_file)
def __init__(self,
infile,
modes=('infreq_pos', ),
most_common=10000,
skip=0,
stop_after=None,
verbose=True,
criterion=None,
vocab_file=filenames.vocab_file):
'''
modes is a tuple of one or more of the following modes:
'word' - write actual words
'pos' - replace words with their part of speech
'infreq_pos' - replace infrequent words with their part of speech
or None, in which case all modes are produced.
most_common:
if mode is 'infreq_pos', only retain this number of words,
replace the rest with part of speech
skip:
number of sentences to skip after each sentence (to avoid all
sentences starting with the same words if the corpus is sorted)
criterion:
None, or function that take a dict representing a dependency and
returns True if the dependency should be kept
'''
self.infile = infile
self.skip = skip
self.most_common = most_common
self.stop_after = stop_after
self.load_freq_dict(vocab_file)
self.verbose = verbose
self.inflect_verb, self.inflect_noun = gen_inflect_from_vocab(
vocab_file)
self.criterion = criterion
allowed_modes = ('word', 'pos', 'infreq_pos')
self.modes = allowed_modes if modes is None else modes
if set(self.modes) - set(allowed_modes) != set():
raise ValueError('Only the following modes are allowed: %s' %
allowed_modes)
def __init__(self, *args, **kwargs):
RNNAcceptor.__init__(self, *args, **kwargs)
self.class_to_code = {'grammatical': 0, 'ungrammatical': 1}
self.code_to_class = {x: y for y, x in self.class_to_code.items()}
self.inflect_verb, _ = gen_inflect_from_vocab(self.vocab_file)
def __init__(self, *args, **kwargs):
super(InflectVerb, self).__init__(*args, **kwargs)
self.inflect_verb, _ = gen_inflect_from_vocab(self.vocab_file)
