def load_set(fname, vocab=None, spad=spad):
s0, s1, y = loader.load_msrpara(fname)
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0 = vocab.vectorize(s0, spad=spad)
si1 = vocab.vectorize(s1, spad=spad)
f0, f1 = nlp.sentence_flags(s0, s1, spad, spad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
return (s0, s1, y, vocab, gr)
def load_set(fname, vocab=None, spad=spad):
s0, s1, y = loader.load_msrpara(fname)
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0 = vocab.vectorize(s0, spad=spad)
si1 = vocab.vectorize(s1, spad=spad)
f0, f1 = nlp.sentence_flags(s0, s1, spad, spad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
return (s0, s1, y, vocab, gr)
def load_set(self, fname):
s0, s1, y = loader.load_msrpara(fname)
if self.vocab is None:
vocab = Vocabulary(s0 + s1)
else:
vocab = self.vocab
si0 = vocab.vectorize(s0, spad=self.s0pad)
si1 = vocab.vectorize(s1, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
return (gr, y, vocab)
def load_set(self, fname):
s0, s1, y = loader.load_msrpara(fname)
if self.vocab is None:
vocab = Vocabulary(s0 + s1)
else:
vocab = self.vocab
si0 = vocab.vectorize(s0, spad=self.s0pad)
si1 = vocab.vectorize(s1, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
return (gr, y, vocab)
def load_set(self, fname, lists=None):
if lists:
s0, s1, y = lists
else:
s0, s1, y = loader.load_msrpara(fname)
if self.vocab is None:
vocab = Vocabulary(s0 + s1, prune_N=self.c['embprune'], icase=self.c['embicase'])
else:
vocab = self.vocab
si0, sj0 = vocab.vectorize(s0, self.emb, spad=self.s0pad)
si1, sj1 = vocab.vectorize(s1, self.emb, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, sj0, sj1, None, None, y, f0, f1, s0, s1)
return (gr, y, vocab)
def load_set(self, fname, lists=None):
if lists:
s0, s1, y = lists
else:
s0, s1, y = loader.load_msrpara(fname)
if self.vocab is None:
vocab = Vocabulary(s0 + s1,
prune_N=self.c['embprune'],
icase=self.c['embicase'])
else:
vocab = self.vocab
si0, sj0 = vocab.vectorize(s0, self.emb, spad=self.s0pad)
si1, sj1 = vocab.vectorize(s1, self.emb, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, sj0, sj1, None, None, y, f0, f1, s0,
s1)
return (gr, y, vocab)