def from_config(cls, config, tensorizers):
embedding = create_module(config.embedding,
tensorizer=tensorizers["tokens"])
representation = create_module(config.representation,
embed_dim=embedding.embedding_dim)
slots = tensorizers["slots"].vocab
decoder = create_module(config.decoder,
in_dim=representation.representation_dim,
out_dim=len(slots))
output_layer = MyTaggingOutputLayer(slots, CrossEntropyLoss(None))
return cls(embedding, representation, decoder, output_layer)
def __init__(
self,
is_binary: bool = True,
label_weights: Optional[Dict[str, float]] = None,
loss=None,
):
super().__init__()
if is_binary:
self.loss = loss or BinaryCrossEntropyLoss(BinaryCrossEntropyLoss.Config())
else:
self.loss = loss or CrossEntropyLoss(CrossEntropyLoss.Config())
def from_config(cls, config, tensorizers):
labels = tensorizers["labels"].vocab
embedding = cls.create_embedding(config, tensorizers)
representation = create_module(config.representation,
embed_dim=embedding.embedding_dim)
decoder = create_module(
config.decoder,
in_dim=representation.representation_dim,
out_dim=len(labels),
)
# TODO after migration: create_module(config.output_layer, tensorizers=tensorizers)
output_layer = WordTaggingOutputLayer(labels, CrossEntropyLoss(None))
return cls(embedding, representation, decoder, output_layer)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
labels = tensorizers["labels"].labels
embedding = cls.create_embedding(config, tensorizers)
representation = create_module(config.representation,
embed_dim=embedding.embedding_dim)
decoder = create_module(
config.decoder,
in_dim=representation.representation_dim,
out_dim=len(labels),
)
output_layer = ClassificationOutputLayer(labels,
CrossEntropyLoss(None))
return cls(embedding, representation, decoder, output_layer)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
vocab = tensorizers["tokens"].vocab
labels = tensorizers["labels"].labels
embedding = WordEmbedding(len(vocab), config.embedding.embed_dim, None,
None, vocab.idx[UNK], [])
representation = create_module(config.representation,
embed_dim=embedding.embedding_dim)
decoder = create_module(
config.decoder,
in_dim=representation.representation_dim,
out_dim=len(labels),
)
output_layer = ClassificationOutputLayer(labels,
CrossEntropyLoss(None))
return cls(embedding, representation, decoder, output_layer)
def __init__(self, loss: Loss = None):
super().__init__()
self.loss = loss or CrossEntropyLoss(CrossEntropyLoss.Config())
