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Remove Example.doc property
Remove Example.doc Remove Example.doc Remove Example.doc Remove Example.doc
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3cc267d600
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@ -335,7 +335,7 @@ def create_evaluation_callback(nlp, optimizer, corpus, cfg):
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)
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)
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)
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)
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n_words = sum(len(ex.doc) for ex in dev_examples)
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n_words = sum(len(ex.predicted) for ex in dev_examples)
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start_time = timer()
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start_time = timer()
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if optimizer.averages:
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if optimizer.averages:
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@ -466,7 +466,7 @@ def train_while_improving(
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def subdivide_batch(batch, accumulate_gradient):
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def subdivide_batch(batch, accumulate_gradient):
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batch = list(batch)
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batch = list(batch)
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batch.sort(key=lambda eg: len(eg.doc))
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batch.sort(key=lambda eg: len(eg.predicted))
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sub_len = len(batch) // accumulate_gradient
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sub_len = len(batch) // accumulate_gradient
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start = 0
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start = 0
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for i in range(accumulate_gradient):
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for i in range(accumulate_gradient):
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@ -192,10 +192,6 @@ cdef class Example:
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def __get__(self):
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def __get__(self):
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return self.x.text
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return self.x.text
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property doc:
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def __get__(self):
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return self.x
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def __str__(self):
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def __str__(self):
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return str(self.to_dict())
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return str(self.to_dict())
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@ -731,7 +731,7 @@ class Language(object):
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scorer = Scorer(pipeline=self.pipeline)
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scorer = Scorer(pipeline=self.pipeline)
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if component_cfg is None:
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if component_cfg is None:
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component_cfg = {}
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component_cfg = {}
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docs = (eg.predicted for eg in examples)
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docs = list(eg.predicted for eg in examples)
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for name, pipe in self.pipeline:
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for name, pipe in self.pipeline:
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kwargs = component_cfg.get(name, {})
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kwargs = component_cfg.get(name, {})
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kwargs.setdefault("batch_size", batch_size)
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kwargs.setdefault("batch_size", batch_size)
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@ -739,7 +739,7 @@ class Language(object):
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docs = _pipe(docs, pipe, kwargs)
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docs = _pipe(docs, pipe, kwargs)
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else:
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else:
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docs = pipe.pipe(docs, **kwargs)
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docs = pipe.pipe(docs, **kwargs)
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for doc, eg in zip(docs, examples):
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for i, (doc, eg) in enumerate(zip(docs, examples)):
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if verbose:
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if verbose:
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print(doc)
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print(doc)
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eg.predicted = doc
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eg.predicted = doc
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@ -1107,7 +1107,7 @@ class EntityLinker(Pipe):
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for eg in examples:
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for eg in examples:
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kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
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kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
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for ent in eg.doc.ents:
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for ent in eg.predicted.ents:
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kb_id = kb_ids[ent.start] # KB ID of the first token is the same as the whole span
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kb_id = kb_ids[ent.start] # KB ID of the first token is the same as the whole span
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if kb_id:
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if kb_id:
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try:
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try:
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@ -1138,7 +1138,7 @@ class EntityLinker(Pipe):
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entity_encodings = []
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entity_encodings = []
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for eg in examples:
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for eg in examples:
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kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
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kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
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for ent in eg.doc.ents:
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for ent in eg.predicted.ents:
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kb_id = kb_ids[ent.start]
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kb_id = kb_ids[ent.start]
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if kb_id:
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if kb_id:
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entity_encoding = self.kb.get_vector(kb_id)
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entity_encoding = self.kb.get_vector(kb_id)
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@ -1158,7 +1158,7 @@ class EntityLinker(Pipe):
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cats = []
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cats = []
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for eg in examples:
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for eg in examples:
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kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
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kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
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for ent in eg.doc.ents:
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for ent in eg.predicted.ents:
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kb_id = kb_ids[ent.start]
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kb_id = kb_ids[ent.start]
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if kb_id:
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if kb_id:
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cats.append([1.0])
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cats.append([1.0])
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@ -93,7 +93,7 @@ class Tok2Vec(Pipe):
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"""
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"""
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if losses is None:
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if losses is None:
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losses = {}
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losses = {}
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docs = [eg.doc for eg in examples]
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docs = [eg.predicted for eg in examples]
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if isinstance(docs, Doc):
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if isinstance(docs, Doc):
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docs = [docs]
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docs = [docs]
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set_dropout_rate(self.model, drop)
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set_dropout_rate(self.model, drop)
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