# To use recent corenlp: https://github.com/stanfordnlp/python-stanford-corenlp
# 1. pip install stanford-corenlp
# 2. download java crsion
# 3. export CORENLP_HOME=/Users/wonseok/utils/stanford-corenlp-full-2018-10-05
# from stanza.nlp.corenlp import CoreNLPClient
# client = CoreNLPClient(server='http://localhost:9000', default_annotators='ssplit,tokenize'.split(','))
import corenlp
client = corenlp.CoreNLPClient(annotators='ssplit,tokenize'.split(','))
nlu1 = "What position does the player who played for butler cc (ks) play??"
path_db = './data_and_model'
db_name = 'dev'
data_table = load_jsonl('./data_and_model/dev.tables.jsonl')
table_name = 'table_1_10015132_11'
n_Q = 100000 if args.infer_loop else 1
for i in range(n_Q):
if n_Q > 1:
nlu1 = input('Type question: ')
'''pr_sql_i, pr_ans = infer(
nlu1,
table_name, data_table, path_db, db_name,
model, model_bert, bert_config, max_seq_length=args.max_seq_length,
num_target_layers=args.num_target_layers,
beam_size=1, show_table=False, show_answer_only=False
)
'''
pr_sql_i, pr_ans = infernew(dev_loader,
dev_data,
# 2. download java crsion
# 3. export CORENLP_HOME=/Users/wonseok/utils/stanford-corenlp-full-2018-10-05
# from stanza.nlp.corenlp import CoreNLPClient
# client = CoreNLPClient(server='http://localhost:9000', default_annotators='ssplit,tokenize'.split(','))
import corenlp
import corenlp
import os
os.environ["CORENLP_HOME"] = './models/stanford-corenlp-4.0.0'
client = corenlp.CoreNLPClient(annotators='ssplit,tokenize'.split(','))
path_db = './data/WikiSQL-1.1/data'
db_name = 'dev'
data_table = load_jsonl('./data/WikiSQL-1.1/data/dev.tables.jsonl')
while True:
nlu1 = input("Enter query to be executed : ")
table_name = input("Enter table name : ")
pr_sql_i, pr_ans = infer(nlu1,
table_name,
data_table,
path_db,
db_name,
model,
model_bert,
bert_config,
max_seq_length=args.max_seq_length,
num_target_layers=args.num_target_layers,
beam_size=1,
#
# client = corenlp.CoreNLPClient(annotators='ssplit,tokenize'.split(','))
'''2020/12/02修改：infer分词函数'''
# from nltk.tokenize.stanford import CoreNLPTokenizer
# sttok = CoreNLPTokenizer('http://localhost:9000') #注意端口号要对应启动stanza的端口号
# import jieba
# nlu1 = "长沙2011年平均每天成交量是3.17，那么近一周的成交量是多少"
# path_db = 'data_and_model'
# db_name = 'dev'
# data_table = load_jsonl('./data_and_model/dev.tables.json')
# table_name = 'Table_69cc8c0c334311e98692542696d6e445'
nlu1 = "你知不知道股票代码等于002043且时间等于现金流量表的数据值是？"
path_db = 'data_and_model'
db_name = 'test'
data_table = load_jsonl('data_and_model/test.tables.json')
table_name = 'Table_financial_statements'
n_Q = 100000 if args.infer_loop else 1
for i in range(n_Q):
if n_Q > 1:
nlu1 = input('Type question: ')
pr_sql_i, pr_ans = infer(nlu1,
table_name,
data_table,
path_db,
db_name,
model,
model_bert,
bert_config,
max_seq_length=args.max_seq_length,
num_target_layers=args.num_target_layers,
# 2. download java crsion
# 3. export CORENLP_HOME=/Users/wonseok/utils/stanford-corenlp-full-2018-10-05
# from stanza.nlp.corenlp import CoreNLPClient
# client = CoreNLPClient(server='http://localhost:9000', default_annotators='ssplit,tokenize'.split(','))
import corenlp
client = corenlp.CoreNLPClient(annotators='ssplit,tokenize'.split(','),
start_server=False,
endpoint='http://localhost:9004')
nlu1 = "Which company have more than 100 employees?"
path_db = './data_and_model'
db_name = 'train'
data_table = load_jsonl('./data_and_model/train_knowledge.jsonl')
table_name = 'sqlite_master'
n_Q = 100000 if args.infer_loop else 1
for i in range(n_Q):
if n_Q > 1:
nlu1 = input('Type question: ')
pr_sql_i, pr_ans = infer(nlu1,
table_name,
data_table,
path_db,
db_name,
model,
model_bert,
bert_config,
max_seq_length=args.max_seq_length,
num_target_layers=args.num_target_layers,
