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spaCy
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Pass kwargs into pipeline components during begin_training

This commit is contained in:
Matthew Honnibal 2018-02-12 10:18:39 +01:00
parent ab35ac4e6f
commit d7c9b53120
1 changed files with 9 additions and 5 deletions
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14
spacy/pipeline.pyx
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@ -144,7 +144,8 @@ class Pipe(object):
return create_default_optimizer(self.model.ops, return create_default_optimizer(self.model.ops,
**self.cfg.get('optimizer', {})) **self.cfg.get('optimizer', {}))
def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None): def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None,
**kwargs):
"""Initialize the pipe for training, using data exampes if available. """Initialize the pipe for training, using data exampes if available.
If no model has been initialized yet, the model is added.""" If no model has been initialized yet, the model is added."""
if self.model is True: if self.model is True:
@ -344,7 +345,8 @@ class Tensorizer(Pipe):
loss = (d_scores**2).sum() loss = (d_scores**2).sum()
return loss, d_scores return loss, d_scores
def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None): def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None,
**kwargs):
"""Allocate models, pre-process training data and acquire an """Allocate models, pre-process training data and acquire an
optimizer. optimizer.
@ -467,7 +469,8 @@ class Tagger(Pipe):
d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs]) d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
return float(loss), d_scores return float(loss), d_scores
def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None): def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None,
**kwargs):
orig_tag_map = dict(self.vocab.morphology.tag_map) orig_tag_map = dict(self.vocab.morphology.tag_map)
new_tag_map = OrderedDict() new_tag_map = OrderedDict()
for raw_text, annots_brackets in gold_tuples: for raw_text, annots_brackets in gold_tuples:
@ -641,7 +644,7 @@ class MultitaskObjective(Tagger):
pass pass
def begin_training(self, gold_tuples=tuple(), pipeline=None, tok2vec=None, def begin_training(self, gold_tuples=tuple(), pipeline=None, tok2vec=None,
sgd=None): sgd=None, **kwargs):
gold_tuples = nonproj.preprocess_training_data(gold_tuples) gold_tuples = nonproj.preprocess_training_data(gold_tuples)
for raw_text, annots_brackets in gold_tuples: for raw_text, annots_brackets in gold_tuples:
for annots, brackets in annots_brackets: for annots, brackets in annots_brackets:
@ -766,7 +769,7 @@ class SimilarityHook(Pipe):
def update(self, doc1_doc2, golds, sgd=None, drop=0.): def update(self, doc1_doc2, golds, sgd=None, drop=0.):
sims, bp_sims = self.model.begin_update(doc1_doc2, drop=drop) sims, bp_sims = self.model.begin_update(doc1_doc2, drop=drop)
def begin_training(self, _=tuple(), pipeline=None, sgd=None): def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs):
"""Allocate model, using width from tensorizer in pipeline. """Allocate model, using width from tensorizer in pipeline.
gold_tuples (iterable): Gold-standard training data. gold_tuples (iterable): Gold-standard training data.
@ -887,6 +890,7 @@ cdef class DependencyParser(Parser):
self._multitasks.append(labeller) self._multitasks.append(labeller)
def init_multitask_objectives(self, gold_tuples, pipeline, sgd=None, **cfg): def init_multitask_objectives(self, gold_tuples, pipeline, sgd=None, **cfg):
self.add_multitask_objective('tag')
for labeller in self._multitasks: for labeller in self._multitasks:
tok2vec = self.model[0] tok2vec = self.model[0]
labeller.begin_training(gold_tuples, pipeline=pipeline, labeller.begin_training(gold_tuples, pipeline=pipeline,