def __init__(self, tables='data/spider/tables.json', db='data/database'):
self.tables = tables
self.db = db
self.schema_tokens, self.column_names, self.database_schemas = editsql_preprocess.read_database_schema(
tables, {}, {}, {})
self.kmaps = evaluation.build_foreign_key_map_from_json(tables)
self.evaluator = evaluation.Evaluator()
def __init__(self, args, ext, remove_invalid=True):
self.args = args
self.remove_invalid = remove_invalid
if self.remove_invalid:
self.conv = converter.Converter(tables=getattr(
args, 'tables', 'data/spider/tables'),
db=getattr(args, 'db',
'data/database'))
self.sql_vocab = ext['sql_voc']
self.evaluator = evaluation.Evaluator()
def __init__(self, args, ext):
super().__init__(args, ext)
self.conv = converter.Converter(tables=getattr(args, 'tables',
'data/spider/tables'),
db=getattr(args, 'db',
'data/database'))
self.bert_tokenizer = DistilBertTokenizer.from_pretrained(
args.dcache + '/vocab.txt', cache_dir=args.dcache)
self.bert_embedder = DistilBertModel.from_pretrained(
args.dcache, cache_dir=args.dcache)
self.value_bert_embedder = DistilBertModel.from_pretrained(
args.dcache, cache_dir=args.dcache)
self.denc = 768
self.demb = args.demb
self.sql_vocab = ext['sql_voc']
self.sql_emb = nn.Embedding.from_pretrained(ext['sql_emb'],
freeze=False)
self.pad_id = self.sql_vocab.word2index('PAD')
self.dropout = nn.Dropout(args.dropout)
self.bert_dropout = nn.Dropout(args.bert_dropout)
self.table_sa_scorer = nn.Linear(self.denc, 1)
self.col_sa_scorer = nn.Linear(self.denc, 1)
self.col_trans = nn.LSTM(self.denc,
self.demb // 2,
bidirectional=True,
batch_first=True)
self.table_trans = nn.LSTM(self.denc,
args.drnn,
bidirectional=True,
batch_first=True)
self.pointer_decoder = decoder.PointerDecoder(
demb=self.demb,
denc=2 * args.drnn,
ddec=args.drnn,
dropout=args.dec_dropout,
num_layers=args.num_layers)
self.utt_trans = nn.LSTM(self.denc,
self.demb // 2,
bidirectional=True,
batch_first=True)
self.value_decoder = decoder.PointerDecoder(demb=self.demb,
denc=self.denc,
ddec=args.drnn,
dropout=args.dec_dropout,
num_layers=args.num_layers)
self.evaluator = evaluation.Evaluator()
if 'reranker' in ext:
self.reranker = ext['reranker']
else:
self.reranker = rank_max.Module(args, ext, remove_invalid=True)
def __init__(self, args, ext):
super().__init__(args, ext)
self.database_schemas = ext['database_schemas']
self.database_content = ext['db_content']
self.kmaps = ext['kmaps']
self.bert_tokenizer = DistilBertTokenizer.from_pretrained(
preprocess.BERT_MODEL, cache_dir=args.dcache)
self.bert_embedder = DistilBertModel.from_pretrained(
preprocess.BERT_MODEL, cache_dir=args.dcache)
self.denc = 768
self.demb = args.demb
self.sql_vocab = ext['sql_voc']
self.sql_emb = nn.Embedding.from_pretrained(ext['sql_emb'],
freeze=False)
self.pad_id = self.sql_vocab.word2index('PAD')
self.dropout = nn.Dropout(args.dropout)
self.bert_dropout = nn.Dropout(args.bert_dropout)
self.table_sa_scorer = nn.Linear(self.denc, 1)
self.col_sa_scorer = nn.Linear(self.denc, 1)
self.col_trans = nn.LSTM(self.denc,
self.demb // 2,
bidirectional=True,
batch_first=True)
self.table_trans = nn.LSTM(self.denc,
args.drnn,
bidirectional=True,
batch_first=True)
self.pointer_decoder = decoder.PointerDecoder(
demb=self.demb,
denc=2 * args.drnn,
ddec=args.drnn,
dropout=args.dec_dropout,
num_layers=args.num_layers)
self.utt_trans = nn.LSTM(self.denc,
self.demb // 2,
bidirectional=True,
batch_first=True)
self.evaluator = evaluation.Evaluator()