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Fix handling of preset entities. closes #2779

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This commit is contained in:
Matthew Honnibal 2018-12-19 02:13:31 +01:00
parent faa8656582
commit e24f94ce39
1 changed files with 15 additions and 3 deletions
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18
spacy/syntax/nn_parser.pyx
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@ -241,8 +241,12 @@ cdef class Parser:
def greedy_parse(self, docs, drop=0.): def greedy_parse(self, docs, drop=0.):
cdef vector[StateC*] states cdef vector[StateC*] states
cdef StateClass state cdef StateClass state
model = self.model(docs)
batch = self.moves.init_batch(docs) batch = self.moves.init_batch(docs)
# This is pretty dirty, but the NER can resize itself in init_batch,
# if labels are missing. We therefore have to check whether we need to
# expand our model output.
self.model.resize_output(self.moves.n_moves)
model = self.model(docs)
weights = get_c_weights(model) weights = get_c_weights(model)
for state in batch: for state in batch:
if not state.is_final(): if not state.is_final():
@ -257,8 +261,12 @@ cdef class Parser:
cdef Beam beam cdef Beam beam
cdef Doc doc cdef Doc doc
cdef np.ndarray token_ids cdef np.ndarray token_ids
model = self.model(docs)
beams = self.moves.init_beams(docs, beam_width, beam_density=beam_density) beams = self.moves.init_beams(docs, beam_width, beam_density=beam_density)
# This is pretty dirty, but the NER can resize itself in init_batch,
# if labels are missing. We therefore have to check whether we need to
# expand our model output.
self.model.resize_output(self.moves.n_moves)
model = self.model(docs)
token_ids = numpy.zeros((len(docs) * beam_width, self.nr_feature), token_ids = numpy.zeros((len(docs) * beam_width, self.nr_feature),
dtype='i', order='C') dtype='i', order='C')
cdef int* c_ids cdef int* c_ids
@ -420,10 +428,14 @@ cdef class Parser:
if self._rehearsal_model is None: if self._rehearsal_model is None:
return None return None
losses.setdefault(self.name, 0.) losses.setdefault(self.name, 0.)
states = self.moves.init_batch(docs)
# This is pretty dirty, but the NER can resize itself in init_batch,
# if labels are missing. We therefore have to check whether we need to
# expand our model output.
self.model.resize_output(self.moves.n_moves)
# Prepare the stepwise model, and get the callback for finishing the batch # Prepare the stepwise model, and get the callback for finishing the batch
tutor = self._rehearsal_model(docs) tutor = self._rehearsal_model(docs)
model, finish_update = self.model.begin_update(docs, drop=0.0) model, finish_update = self.model.begin_update(docs, drop=0.0)
states = self.moves.init_batch(docs)
n_scores = 0. n_scores = 0.
loss = 0. loss = 0.
non_zeroed_classes = self._rehearsal_model.upper.W.any(axis=1) non_zeroed_classes = self._rehearsal_model.upper.W.any(axis=1)