def forward(self, tokens: List[List[str]]):
seq_lens = make_sequence_lengths(tokens)
word_ids = self.vocab.lookup_indices_2d(tokens)
word_ids = pad_2d(word_ids, seq_lens, self.pad_idx)
logits = self.model(torch.tensor(word_ids),
torch.tensor(seq_lens))
return self.output_layer(logits)
def forward(self, tokens: List[List[str]]):
seq_lens = make_sequence_lengths(tokens)
word_ids = self.vocab.lookup_indices_2d(tokens)
word_ids = pad_2d(word_ids, seq_lens, self.pad_idx)
token_bytes, _ = make_byte_inputs(
tokens, self.max_byte_len,
self.byte_offset_for_non_padding)
logits = self.model(torch.tensor(word_ids), token_bytes,
torch.tensor(seq_lens))
return self.output_layer(logits)
def forward(self, tokens: List[List[str]], dense_feat: List[List[float]]):
seq_lens = make_sequence_lengths(tokens)
word_ids = self.vocab.lookup_indices_2d(tokens)
word_ids = pad_2d(word_ids, seq_lens, self.pad_idx)
dense_feat = self.normalizer.normalize(dense_feat)
logits = self.model(
torch.tensor(word_ids),
torch.tensor(seq_lens),
torch.tensor(dense_feat, dtype=torch.float),
)
return self.output_layer(logits)
