def evaluate_model(config: Config, model: NNCRF, batch_insts_ids, name: str,
insts: List[Instance]):
## evaluation
metrics = np.asarray([0, 0, 0], dtype=int)
batch_id = 0
batch_size = config.batch_size
for batch in batch_insts_ids:
one_batch_insts = insts[batch_id * batch_size:(batch_id + 1) *
batch_size]
sorted_batch_insts = sorted(one_batch_insts,
key=lambda inst: len(inst.input.words),
reverse=True)
batch_max_scores, batch_max_ids = model.decode(batch)
metrics += eval.evaluate_num(sorted_batch_insts, batch_max_ids,
batch[-1], batch[1], config.idx2labels)
batch_id += 1
p, total_predict, total_entity = metrics[0], metrics[1], metrics[2]
precision = p * 1.0 / total_predict * 100 if total_predict != 0 else 0
recall = p * 1.0 / total_entity * 100 if total_entity != 0 else 0
fscore = 2.0 * precision * recall / (
precision + recall) if precision != 0 or recall != 0 else 0
print("[%s set] Precision: %.2f, Recall: %.2f, F1: %.2f" %
(name, precision, recall, fscore),
flush=True)
return [precision, recall, fscore]
class MT_LSTMCRF(nn.Module):
def __init__(self, config_base, config_conll, config_ontonotes):
super(MT_LSTMCRF, self).__init__()
self.config_base = config_base
self.config_conll = config_conll
self.config_ontonotes = config_ontonotes
self.lstmcrf_base = NNCRF(config_base)
self.lstmcrf_conll = NNCRF(config_conll)
self.lstmcrf_ontonotes = NNCRF(config_ontonotes)
def neg_log_obj_total(self, words, word_seq_lens, batch_context_emb, chars,
char_seq_lens, prefix_label, conll_label,
notes_label, mask_base, mask_conll, mask_ontonotes):
loss_base, hiddens_base = self.lstmcrf_base.neg_log_obj(
words, word_seq_lens, batch_context_emb, chars, char_seq_lens,
prefix_label, mask_base)
# hidden_base = w1 * h1
loss_conll, _ = self.lstmcrf_conll.neg_log_obj(words, word_seq_lens,
batch_context_emb,
chars, char_seq_lens,
conll_label, mask_conll,
hiddens_base)
loss_ontonotes, _ = self.lstmcrf_ontonotes.neg_log_obj(
words, word_seq_lens, batch_context_emb, chars, char_seq_lens,
notes_label, mask_ontonotes, hiddens_base)
loss_total = loss_base + loss_conll + loss_ontonotes
# loss_total = loss_ontonotes
return loss_total
def decode(self, batchinput):
words, word_seq_lens, batch_context_emb, chars, char_seq_lens, prefix_label, conll_label, notes_label, mask_base, mask_conll, mask_ontonotes = batchinput
_, hiddens_base = self.lstmcrf_base.neg_log_obj(
words, word_seq_lens, batch_context_emb, chars, char_seq_lens,
prefix_label, mask_base)
bestScores_conll, decodeIdx_conll = self.lstmcrf_conll.decode(
batchinput, hiddens_base)
bestScores_notes, decodeIdx_notes = self.lstmcrf_ontonotes.decode(
batchinput, hiddens_base)
return bestScores_conll, decodeIdx_conll, bestScores_notes, decodeIdx_notes, mask_conll, mask_ontonotes
