def compute_loss(self, docs: Sequence[Doc], golds: Sequence[GoldParse],
outputs: torch.Tensor) -> None:
self.require_model()
targets = outputs.new_tensor(
[[gold.cats[label] for label in self.labels] for gold in golds])
loss = F.binary_cross_entropy_with_logits(outputs, targets)
add_loss_to_docs(docs, loss)
def compute_loss(self, docs: Sequence[Doc], golds: Sequence[GoldParse],
outputs: torch.Tensor) -> None:
target = self._create_target_from_docs_golds(docs, golds, outputs)
loss = F.cross_entropy(outputs.transpose(1, 2),
target,
ignore_index=self.ignore_label_index)
add_loss_to_docs(docs, loss)
def compute_loss(self, docs: Sequence[Doc], golds: Sequence[GoldParse],
outputs: torch.Tensor) -> None:
self.require_model()
targets = self.golds_to_tensor(golds)
weight = self.label_weights.to(device=self.device) # type: ignore
loss = F.cross_entropy(outputs, targets, weight=weight)
add_loss_to_docs(docs, loss)
def train():
docs, golds = nlp._format_docs_and_golds(texts, labels)
before = sum_param(pipe.optim_parameters())
nlp._update_pipes(docs, golds)
h = get_last_hidden_state_from_docs(docs)
loss = h.sum() + torch.tensor(0.0, requires_grad=True)
add_loss_to_docs(docs, loss)
nlp._update_params(docs, optimizer)
return abs(before - sum_param(pipe.optim_parameters()))
def update(self, docs: List[Doc], golds: List[GoldParse],
**kwargs): # type: ignore
assert isinstance(docs, list)
self.require_model()
logits = self.model(get_last_hidden_state_from_docs(docs))
target = self._create_target_from_docs_golds(docs, golds, logits)
loss = F.cross_entropy(logits.transpose(1, 2),
target,
ignore_index=self.ignore_label_index)
add_loss_to_docs(docs, loss)
def update( # type: ignore
self, docs: List[Doc], golds: Iterable[GoldParse], **kwargs):
self.require_model()
self.model.train()
x = get_last_hidden_state_from_docs(docs)
preds: torch.Tensor = self.model(x)
preds = preds.view(-1, self.model.config.vocab_size)
targets = (get_maskedlm_labels(docs).view(-1)[:len(preds)].to(
device=self.device))
loss = F.cross_entropy(preds, targets, ignore_index=-1)
add_loss_to_docs(docs, loss)
def update( # type: ignore
self, docs: List[Doc], golds: Iterable[GoldParse], **kwargs):
assert isinstance(docs, list)
self.require_model()
self.model.train()
x = get_last_hidden_state_from_docs(docs)
logits = self.model(x)
targets = self.golds_to_tensor(golds)
weight = self.label_weights.to(device=self.device) # type: ignore
loss = F.cross_entropy(logits, targets, weight=weight)
add_loss_to_docs(docs, loss)
