def predict(s):
pre_toks = tokenizer.tokenize(s)
max_seq_len = 128
with open('tmp', 'w') as f:
f.write('\t'.join(
['na', ' '.join(pre_toks), ' '.join(pre_toks), 'na', 'na']))
dl, _ = get_dataloader('tmp', tok2id, 1)
for batch in dl:
(pre_id, pre_mask, pre_len, post_in_id, post_out_id, pre_tok_label_id,
_, rel_ids, pos_ids, categories) = batch
post_start_id = tok2id['行']
max_len = min(max_seq_len, pre_len[0].detach().cpu().numpy() + 10)
with torch.no_grad():
predicted_toks, predicted_probs = joint_model.inference_forward(
pre_id,
post_start_id,
pre_mask,
pre_len,
max_len,
pre_tok_label_id,
rel_ids=rel_ids,
pos_ids=pos_ids,
categories=categories,
beam_width=1)
# print(predicted_toks); quit()
pred_seq = [id2tok[x] for x in predicted_toks[0][1:]]
if '止' in pred_seq:
pred_seq = pred_seq[:pred_seq.index('止')]
pred_seq = ' '.join(pred_seq).replace('[PAD]', '').strip()
pred_seq = pred_seq.replace(' ##', '')
return pred_seq
import model as joint_model
import utils as joint_utils
assert ARGS.inference_output, "Need to specify inference_output arg!"
# # # # # # # # ## # # # ## # # DATA # # # # # # # # ## # # # ## # #
tokenizer = BertTokenizer.from_pretrained(ARGS.bert_model,
cache_dir=ARGS.working_dir +
'/cache')
tok2id = tokenizer.vocab
tok2id['<del>'] = len(tok2id)
eval_dataloader, num_eval_examples = get_dataloader(
ARGS.test,
tok2id,
ARGS.test_batch_size,
ARGS.working_dir + '/test_data.pkl',
test=True,
add_del_tok=ARGS.add_del_tok)
# # # # # # # # ## # # # ## # # MODEL # # # # # # # # ## # # # ## # #
if ARGS.pointer_generator:
debias_model = seq2seq_model.PointerSeq2Seq(
vocab_size=len(tok2id),
hidden_size=ARGS.hidden_size,
emb_dim=768,
dropout=0.2,
tok2id=tok2id) # 768 = bert hidden size
else:
debias_model = seq2seq_model.Seq2Seq(vocab_size=len(tok2id),
sys.path.append('.')
from shared.data import get_dataloader
from shared.args import ARGS
if not os.path.exists(ARGS.working_dir):
os.makedirs(ARGS.working_dir)
print('LOADING DATA...')
tokenizer = BertTokenizer.from_pretrained(ARGS.bert_model,
cache_dir=ARGS.working_dir +
'/cache')
tok2id = tokenizer.vocab
tok2id['<del>'] = len(tok2id)
train_dataloader, num_train_examples = get_dataloader(
ARGS.train, tok2id, ARGS.train_batch_size, ARGS.max_seq_len,
ARGS.working_dir + '/train_data.pkl')
eval_dataloader, num_eval_examples = get_dataloader(ARGS.test,
tok2id,
ARGS.test_batch_size,
ARGS.max_seq_len,
ARGS.working_dir +
'/test_data.pkl',
test=True)
featurizer = Featurizer(tok2id)
def data_for_scipy(dataloader, by_seq=False):
outX = []
outY = []
writer = SummaryWriter(ARGS.working_dir)
# # # # # # # # ## # # # ## # # DATA # # # # # # # # ## # # # ## # #
tokenizer = BertTokenizer.from_pretrained(ARGS.bert_model,
cache_dir=ARGS.working_dir +
'/cache')
tok2id = tokenizer.vocab
tok2id['<del>'] = len(tok2id)
print("Vocab size: {}".format(len(tok2id)))
if ARGS.pretrain_data:
print("Loading pretrain data...")
pretrain_dataloader, num_pretrain_examples = get_dataloader(
ARGS.pretrain_data,
tok2id,
ARGS.train_batch_size,
ARGS.working_dir + '/pretrain_data.pkl',
noise=True)
print("Loading train data...")
train_dataloader, num_train_examples = get_dataloader(
ARGS.train,
tok2id,
ARGS.train_batch_size,
ARGS.working_dir + '/train_data.pkl',
categories_path=ARGS.categories_file,
add_del_tok=ARGS.add_del_tok)
print("Loading eval data...")
eval_dataloader, num_eval_examples = get_dataloader(
ARGS.test,
tok2id,
with open(ARGS.working_dir + '/command.sh', 'w') as f:
f.write('python' + ' '.join(sys.argv) + '\n')
# # # # # # # # ## # # # ## # # DATA # # # # # # # # ## # # # ## # #
print('LOADING DATA...')
tokenizer = BertTokenizer.from_pretrained(ARGS.bert_model,
cache_dir=ARGS.working_dir +
'/cache')
tok2id = tokenizer.vocab
tok2id['<del>'] = len(tok2id)
print(ARGS)
train_dataloader, num_train_examples = get_dataloader(
ARGS.train,
tok2id,
ARGS.train_batch_size,
ARGS.working_dir + '/train_data.pkl',
categories_path=ARGS.categories_file)
eval_dataloader, num_eval_examples = get_dataloader(
ARGS.test,
tok2id,
ARGS.test_batch_size,
ARGS.working_dir + '/test_data.pkl',
test=True,
categories_path=ARGS.categories_file)
# # # # # # # # ## # # # ## # # MODEL # # # # # # # # ## # # # ## # #
print('BUILDING MODEL...')
if ARGS.extra_features_top:
model = tagging_model.BertForMultitaskWithFeaturesOnTop.from_pretrained(
TEST_BATCH_SIZE = 16
else:
TRAIN_BATCH_SIZE = ARGS.train_batch_size
TEST_BATCH_SIZE = ARGS.test_batch_size // ARGS.beam_width
# # # # # # # # ## # # # ## # # DATA # # # # # # # # ## # # # ## # #
tokenizer = BertTokenizer.from_pretrained(BERT_MODEL,
cache_dir=ARGS.working_dir +
'/cache')
tok2id = tokenizer.vocab
tok2id['<del>'] = len(tok2id)
if ARGS.pretrain_data:
pretrain_dataloader, num_pretrain_examples = get_dataloader(
ARGS.pretrain_data,
tok2id,
TRAIN_BATCH_SIZE,
ARGS.working_dir + '/pretrain_data.pkl',
noise=True)
train_dataloader, num_train_examples = get_dataloader(
ARGS.train,
tok2id,
TRAIN_BATCH_SIZE,
ARGS.working_dir + '/train_data.pkl',
add_del_tok=ARGS.add_del_tok)
eval_dataloader, num_eval_examples = get_dataloader(
ARGS.test,
tok2id,
TEST_BATCH_SIZE,
ARGS.working_dir + '/test_data.pkl',
test=True,
BERT_MODEL = "bert-base-uncased"
tokenizer = BertTokenizer.from_pretrained(BERT_MODEL,
cache_dir=ARGS.working_dir +
'/cache')
tok2id = tokenizer.vocab
tok2id['<del>'] = len(tok2id)
featurizer = Featurizer(tok2id)
feature_names = featurizer.get_feature_names()
TARGET_VARIABLE = 'entailing'
tgt_idx = feature_names.index(TARGET_VARIABLE)
dataloader, num_pretrain_examples = get_dataloader(
ARGS.train, tok2id, ARGS.train_batch_size,
ARGS.working_dir + '/pretrain_data.pkl')
feature_counts = defaultdict(
lambda: {
'n00': 1., # docs without term, 0 label
'n01': 1., # docs without term, 1 label
'n10': 1., # docs with term, 0 label
'n11': 1. # docs with term, 1 label
})
for batch in tqdm(dataloader):
(pre_id, pre_mask, pre_len, post_in_id, post_out_id, pre_tok_label_id,
post_tok_label_id, rel_ids, pos_ids, categories) = batch
features = featurizer.featurize_batch(pre_id.detach().cpu().numpy(),
