explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-07-19 20:52:23 +03:00
Code Issues Packages Projects Releases Wiki Activity

Run black

Browse Source
This commit is contained in:
Paul O'Leary McCann 2021-07-18 20:20:22 +09:00
parent bc081c24fa
commit 8bd0474730
3 changed files with 33 additions and 29 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

22
spacy/ml/models/coref.py
View File

@ -24,7 +24,7 @@ def build_coref(
# the token count. # the token count.
mention_limit_ratio: float = 0.4, mention_limit_ratio: float = 0.4,
max_span_width: int = 20, max_span_width: int = 20,
antecedent_limit: int = 50 antecedent_limit: int = 50,
): ):
dim = tok2vec.get_dim("nO") * 3 dim = tok2vec.get_dim("nO") * 3
@ -90,15 +90,14 @@ def build_width_scorer(max_span_width, hidden_size, feature_embed_size=20):
>> Linear(nI=hidden_size, nO=1) >> Linear(nI=hidden_size, nO=1)
) )
span_width_prior.initialize() span_width_prior.initialize()
model = Model( model = Model("WidthScorer", forward=width_score_forward, layers=[span_width_prior])
"WidthScorer",
forward=width_score_forward,
layers=[span_width_prior])
model.set_ref("width_prior", span_width_prior) model.set_ref("width_prior", span_width_prior)
return model return model
def width_score_forward(model, embeds: SpanEmbeddings, is_train) -> Tuple[Floats1d, Callable]: def width_score_forward(
model, embeds: SpanEmbeddings, is_train
) -> Tuple[Floats1d, Callable]:
# calculate widths, subtracting 1 so it's 0-index # calculate widths, subtracting 1 so it's 0-index
w_ffnn = model.get_ref("width_prior") w_ffnn = model.get_ref("width_prior")
idxs = embeds.indices idxs = embeds.indices
@ -115,6 +114,7 @@ def width_score_forward(model, embeds: SpanEmbeddings, is_train) -> Tuple[Floats
return wscores, width_score_backward return wscores, width_score_backward
# model converting a Doc/Mention to span embeddings # model converting a Doc/Mention to span embeddings
# get_mentions: Callable[Doc, Pairs[int]] # get_mentions: Callable[Doc, Pairs[int]]
def build_span_embedder( def build_span_embedder(
@ -123,8 +123,9 @@ def build_span_embedder(
) -> Model[Tuple[List[Floats2d], List[Doc]], SpanEmbeddings]: ) -> Model[Tuple[List[Floats2d], List[Doc]], SpanEmbeddings]:
with Model.define_operators({">>": chain, "|": concatenate}): with Model.define_operators({">>": chain, "|": concatenate}):
span_reduce = (extract_spans() >> span_reduce = extract_spans() >> (
(reduce_first() | reduce_last() | reduce_mean())) reduce_first() | reduce_last() | reduce_mean()
)
model = Model( model = Model(
"SpanEmbedding", "SpanEmbedding",
forward=span_embeddings_forward, forward=span_embeddings_forward,
@ -290,6 +291,7 @@ def build_take_vecs() -> Model[SpanEmbeddings, Floats2d]:
def take_vecs_forward(model, inputs: SpanEmbeddings, is_train) -> Floats2d: def take_vecs_forward(model, inputs: SpanEmbeddings, is_train) -> Floats2d:
idxs = inputs.indices idxs = inputs.indices
lens = inputs.vectors.lengths lens = inputs.vectors.lengths
def backprop(dY: Floats2d) -> SpanEmbeddings: def backprop(dY: Floats2d) -> SpanEmbeddings:
vecs = Ragged(dY, lens) vecs = Ragged(dY, lens)
return SpanEmbeddings(idxs, vecs) return SpanEmbeddings(idxs, vecs)
@ -350,10 +352,10 @@ def ant_scorer_forward(
# This will take the log of 0, which causes a warning, but we're doing # This will take the log of 0, which causes a warning, but we're doing
# it on purpose so we can just ignore the warning. # it on purpose so we can just ignore the warning.
with warnings.catch_warnings(): with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning) warnings.filterwarnings("ignore", category=RuntimeWarning)
mask = xp.log( mask = xp.log(
(xp.expand_dims(ant_range, 1) - xp.expand_dims(ant_range, 0)) >= 1 (xp.expand_dims(ant_range, 1) - xp.expand_dims(ant_range, 0)) >= 1
).astype('f') ).astype("f")
scores = pw_prod + pw_sum + mask scores = pw_prod + pw_sum + mask

6
spacy/pipeline/coref.py
View File

@ -79,7 +79,9 @@ def make_coref(
) -> "CoreferenceResolver": ) -> "CoreferenceResolver":
"""Create a CoreferenceResolver component.""" """Create a CoreferenceResolver component."""
return CoreferenceResolver(nlp.vocab, model, name, span_cluster_prefix=span_cluster_prefix) return CoreferenceResolver(
nlp.vocab, model, name, span_cluster_prefix=span_cluster_prefix
)
class CoreferenceResolver(TrainablePipe): class CoreferenceResolver(TrainablePipe):
@ -308,7 +310,7 @@ class CoreferenceResolver(TrainablePipe):
top_gscores = ops.asarray2f(top_gscores) top_gscores = ops.asarray2f(top_gscores)
with warnings.catch_warnings(): with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning) warnings.filterwarnings("ignore", category=RuntimeWarning)
log_marg = ops.softmax(cscores + ops.xp.log(top_gscores), axis=1) log_marg = ops.softmax(cscores + ops.xp.log(top_gscores), axis=1)
log_norm = ops.softmax(cscores, axis=1) log_norm = ops.softmax(cscores, axis=1)
grad = log_norm - log_marg grad = log_norm - log_marg