File size: 2,654 Bytes
737279c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
from imports import *
from all_datasets import *
class PhoBertLstmCrf(RobertaForTokenClassification):
def __init__(self, config):
super(PhoBertLstmCrf, self).__init__(config=config)
self.num_labels = config.num_labels
self.lstm = nn.LSTM(input_size=config.hidden_size,
hidden_size=config.hidden_size // 2,
num_layers=1,
batch_first=True,
bidirectional=True)
self.crf = CRF(config.num_labels, batch_first=True)
@staticmethod
def sort_batch(src_tensor, lengths):
"""
Sort a minibatch by the length of the sequences with the longest sequences first
return the sorted batch targes and sequence lengths.
This way the output can be used by pack_padd ed_sequences(...)
"""
seq_lengths, perm_idx = lengths.sort(0, descending=True)
seq_tensor = src_tensor[perm_idx]
_, reversed_idx = perm_idx.sort(0, descending=False)
return seq_tensor, seq_lengths, reversed_idx
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, valid_ids=None,
label_masks=None):
seq_outputs = self.roberta(input_ids=input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
head_mask=None)[0]
batch_size, max_len, feat_dim = seq_outputs.shape
seq_lens = torch.sum(label_masks, dim=-1)
range_vector = torch.arange(0, batch_size, dtype=torch.long, device=seq_outputs.device).unsqueeze(1)
seq_outputs = seq_outputs[range_vector, valid_ids]
sorted_seq_outputs, sorted_seq_lens, reversed_idx = self.sort_batch(src_tensor=seq_outputs,
lengths=seq_lens)
packed_words = pack_padded_sequence(sorted_seq_outputs, sorted_seq_lens.cpu(), True)
lstm_outs, _ = self.lstm(packed_words)
lstm_outs, _ = pad_packed_sequence(lstm_outs, batch_first=True, total_length=max_len)
seq_outputs = lstm_outs[reversed_idx]
seq_outputs = self.dropout(seq_outputs)
logits = self.classifier(seq_outputs)
seq_tags = self.crf.decode(logits, mask=label_masks != 0)
if labels is not None:
log_likelihood = self.crf(logits, labels, mask=label_masks.type(torch.uint8))
return NerOutput(loss=-1.0 * log_likelihood, tags=seq_tags, cls_metrics=seq_tags)
else:
return NerOutput(tags=seq_tags)
|