def evaluate(ckpt, hparams_file):
"""main"""
trainer = Trainer(gpus=0)
model = BertLabeling.load_from_checkpoint(checkpoint_path=ckpt,
hparams_file=hparams_file,
map_location=None,
batch_size=1,
max_length=128,
workers=0)
trainer.test(model=model)
def evaluate(ckpt, hparams_file):
"""main"""
trainer = Trainer(gpus=[0, 1, 2, 3], distributed_backend="ddp")
model = BertLabeling.load_from_checkpoint(checkpoint_path=ckpt,
hparams_file=hparams_file,
map_location=None,
batch_size=8,
max_length=128,
workers=40)
trainer.test(model=model)
def evaluate(ckpt, hparams_file):
"""main"""
trainer = Trainer(gpus=[1])
model = BertLabeling.load_from_checkpoint(checkpoint_path=ckpt,
hparams_file=hparams_file,
map_location=None,
batch_size=16,
max_length=128,
workers=0)
dataset_seen, dataset_unseen = get_dataloader_test(
model.args.tgt_domain, tokenizer=model.tokenizer)
model.dataset_test = dataset_unseen
trainer.test(model=model)
model.dataset_test = dataset_seen
trainer.test(model=model)
def main():
parser = get_parser()
args = parser.parse_args()
trained_mrc_ner_model = BertLabeling.load_from_checkpoint(
checkpoint_path=args.model_ckpt,
hparams_file=args.hparams_file,
map_location=None,
batch_size=1,
max_length=args.max_length,
workers=0)
data_loader, data_tokenizer = get_dataloader(args,)
# load token
vocab_path = os.path.join(args.bert_dir, "vocab.txt")
with open(vocab_path, "r") as f:
subtokens = [token.strip() for token in f.readlines()]
idx2tokens = {}
for token_idx, token in enumerate(subtokens):
idx2tokens[token_idx] = token
query2label_dict = get_query_index_to_label_cate(args.dataset_sign)
for batch in data_loader:
tokens, token_type_ids, start_labels, end_labels, start_label_mask, end_label_mask, match_labels, sample_idx, label_idx = batch
attention_mask = (tokens != 0).long()
start_logits, end_logits, span_logits = trained_mrc_ner_model.model(tokens, attention_mask=attention_mask, token_type_ids=token_type_ids)
start_preds, end_preds, span_preds = start_logits > 0, end_logits > 0, span_logits > 0
subtokens_idx_lst = tokens.numpy().tolist()[0]
subtokens_lst = [idx2tokens[item] for item in subtokens_idx_lst]
label_cate = query2label_dict[label_idx.item()]
readable_input_str = data_tokenizer.decode(subtokens_idx_lst, skip_special_tokens=True)
if args.flat_ner:
entities_info = extract_flat_spans(torch.squeeze(start_preds), torch.squeeze(end_preds),
torch.squeeze(span_preds), torch.squeeze(attention_mask), pseudo_tag=label_cate)
entity_lst = []
if len(entities_info) != 0:
for entity_info in entities_info:
start, end = entity_info[0], entity_info[1]
entity_string = " ".join(subtokens_lst[start: end])
entity_string = entity_string.replace(" ##", "")
entity_lst.append((start, end, entity_string, entity_info[2]))
else:
match_preds = span_logits > 0
entities_info = extract_nested_spans(start_preds, end_preds, match_preds, start_label_mask, end_label_mask, pseudo_tag=label_cate)
entity_lst = []
if len(entities_info) != 0:
for entity_info in entities_info:
start, end = entity_info[0], entity_info[1]
entity_string = " ".join(subtokens_lst[start: end+1 ])
entity_string = entity_string.replace(" ##", "")
entity_lst.append((start, end+1, entity_string, entity_info[2]))
print("*="*10)
print(f"Given input: {readable_input_str}")
print(f"Model predict: {entity_lst}")
)
return dataloader
if __name__ == '__main__':
#company
CHECKPOINTS = "/root/mao/249/mrc-ner-company/train_logs/zh_msra_company/zh_msra_bertlarge_lr8e-620200913_dropout0.2_bsz16_maxlen128/epoch=19_v0.ckpt"
HPARAMS = "/root/mao/249/mrc-ner-company/train_logs/zh_msra_company/zh_msra_bertlarge_lr8e-620200913_dropout0.2_bsz16_maxlen128/lightning_logs/version_0/hparams.yaml"
model = BertLabeling.load_from_checkpoint(
checkpoint_path=CHECKPOINTS,
hparams_file=HPARAMS,
map_location=None,
batch_size=1,
max_length=128,
workers=0
)
dataloader=get_dataloader('demo')
vocab_file = os.path.join(bert_dir, "vocab.txt")
tokenizer = BertWordPieceTokenizer(vocab_file=vocab_file)
query = '公司指企业的组织形式，社会经济组织'
with torch.no_grad():
for batch in dataloader:
tokens, token_type_ids = batch