本文整理汇总了Python中allennlp.modules.token_embedders.ElmoTokenEmbedder方法的典型用法代码示例。如果您正苦于以下问题：Python token_embedders.ElmoTokenEmbedder方法的具体用法？Python token_embedders.ElmoTokenEmbedder怎么用？Python token_embedders.ElmoTokenEmbedder使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类allennlp.modules.token_embedders的用法示例。
在下文中一共展示了token_embedders.ElmoTokenEmbedder方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _run_test
# 需要导入模块: from allennlp.modules import token_embedders [as 别名]
# 或者: from allennlp.modules.token_embedders import ElmoTokenEmbedder [as 别名]
def _run_test(self, requires_grad):
embedder = ElmoTokenEmbedder(
self.options_file, self.weight_file, requires_grad=requires_grad
)
batch_size = 3
seq_len = 4
char_ids = torch.from_numpy(numpy.random.randint(0, 262, (batch_size, seq_len, 50)))
embeddings = embedder(char_ids)
loss = embeddings.sum()
loss.backward()
elmo_grads = [
param.grad for name, param in embedder.named_parameters() if "_elmo_lstm" in name
]
if requires_grad:
# None of the elmo grads should be None.
assert all(grad is not None for grad in elmo_grads)
else:
# All of the elmo grads should be None.
assert all(grad is None for grad in elmo_grads) 示例2: _run_test
# 需要导入模块: from allennlp.modules import token_embedders [as 别名]
# 或者: from allennlp.modules.token_embedders import ElmoTokenEmbedder [as 别名]
def _run_test(self, requires_grad):
embedder = ElmoTokenEmbedder(self.options_file, self.weight_file, requires_grad=requires_grad)
batch_size = 3
seq_len = 4
char_ids = torch.from_numpy(numpy.random.randint(0, 262, (batch_size, seq_len, 50)))
embeddings = embedder(char_ids)
loss = embeddings.sum()
loss.backward()
elmo_grads = [param.grad for name, param in embedder.named_parameters() if u'_elmo_lstm' in name]
if requires_grad:
# None of the elmo grads should be None.
assert all([grad is not None for grad in elmo_grads])
else:
# All of the elmo grads should be None.
assert all([grad is None for grad in elmo_grads]) 示例3: forward
# 需要导入模块: from allennlp.modules import token_embedders [as 别名]
# 或者: from allennlp.modules.token_embedders import ElmoTokenEmbedder [as 别名]
def forward(self, # type: ignore
hypothesis0: Dict[str, torch.LongTensor],
hypothesis1: Dict[str, torch.LongTensor],
hypothesis2: Dict[str, torch.LongTensor],
hypothesis3: Dict[str, torch.LongTensor],
label: torch.IntTensor = None,
) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
Returns
-------
An output dictionary consisting of:
logits : torch.FloatTensor
A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
unnormalised log probabilities of the tag classes.
class_probabilities : torch.FloatTensor
A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
a distribution of the tag classes per word.
loss : torch.FloatTensor, optional
A scalar loss to be optimised.
"""
logits = []
for tokens in [hypothesis0, hypothesis1, hypothesis2, hypothesis3]:
if isinstance(self.text_field_embedder, ElmoTokenEmbedder):
self.text_field_embedder._elmo._elmo_lstm._elmo_lstm.reset_states()
embedded_text_input = self.embedding_dropout(self.text_field_embedder(tokens))
mask = get_text_field_mask(tokens)
batch_size, sequence_length, _ = embedded_text_input.size()
encoded_text = self.encoder(embedded_text_input, mask)
logits.append(self.output_prediction(encoded_text.max(1)[0]))
logits = torch.cat(logits, -1)
class_probabilities = F.softmax(logits, dim=-1).view([batch_size, 4])
output_dict = {"label_logits": logits, "label_probs": class_probabilities}
if label is not None:
loss = self._loss(logits, label.long().view(-1))
self._accuracy(logits, label.squeeze(-1))
output_dict["loss"] = loss
return output_dict