Since bert cannot handle sequence longer than 512, each column/table is encoded individually The representation of a column/table is

return 1 if the values between prediction and gold are matching in the corresponding index. Currently not support multiple col_unit(pairs).

Run evaluation and return metrics. The calling script will be responsible for providing a method to compute metrics, as they are

FP16-compatible function that fills a input_ids with -inf.

FP16-compatible function that fills a input_ids with -inf.

r""" Generates sequences for models with a LM head. The method currently supports greedy decoding, beam-search decoding, sampling with temperature, sa

Setup the optimizer and the learning rate scheduler. We provide a reasonable default that works well. If you want to use som

Setup the optimizer and the learning rate scheduler. We provide a reasonable default that works well. If you want to use som

Get database's schema, which is a dict with table name as key and list of column names as value :param db: database path :return: sch

If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst one in the heap, then

If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst one in the heap, then

item_dict: {"model": model, "opt1": opt1, ...}

Load a model (identified by the config used for construction) and return it

True for pad tokens

True for pad tokens

Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. If ignore_segment is not None, then thi

If the token is not a word piece, then find its lemma If it is, combine pieces into a word, and then find its lemma E.g., a #

Assume in the from clause, all table units are combined with join

:returns next idx, table id, table name

If it's bert, we also cache the normalized version of question/column/table for schema linking