File Not found issue about sqlova HOT 6 OPEN

Hi @alekdev2019

Please refer to the issue #4 .

Thanks!

from sqlova.

Thank you for your quick solution.
I am interesting in changing your code with some different method.
For now, you've been using PyTorch to build the network, but I would like to change it with Tensorflow based.
Would you let me know what is the quick way to achieve this purpose?
Thank you

from sqlova.

Maybe you can start from original BERT code?
Also, as far as I know, most of "natural language → SQL" models are written in pytorch except fews like MAPO (but this is about weak-supervision model) and some baseline code for other SQL training set.

Sorry I can't be of more help on this issue.

Wonseok

from sqlova.

Thank you for your help.

from sqlova.

I have an update on this spec.
I am willing to use Keras, and Keras include a lot of similar module with PyTorch.
I am very concerned how I need to convert a class based code(PyTorch)
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss

class BERTEmbeddings(nn.Module):
def init(self, config):
super(BERTEmbeddings, self).init()
"""Construct the embedding module from word, position and token_type embeddings.
"""
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

    # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
    # any TensorFlow checkpoint file
    self.LayerNorm = BERTLayerNorm(config)
    self.dropout = nn.Dropout(config.hidden_dropout_prob)

def forward(self, input_ids, token_type_ids=None):
    seq_length = input_ids.size(1)
    position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
    position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
    if token_type_ids is None:
        token_type_ids = torch.zeros_like(input_ids)

    words_embeddings = self.word_embeddings(input_ids)
    position_embeddings = self.position_embeddings(position_ids)
    token_type_embeddings = self.token_type_embeddings(token_type_ids)

    embeddings = words_embeddings + position_embeddings + token_type_embeddings
    embeddings = self.LayerNorm(embeddings)
    embeddings = self.dropout(embeddings)
    return embeddings

As you can see, BERTEmbeddings is a class based on torch.nn.Module, but how I can convert it with keras??
Please help me to implement this.
Thank you

from sqlova.

@whwang299 HI , I am also implementing sqlova in keras with bert embeddings.
I am using pytorch trained bert tokenizer to generate words, positions and segment embeddings .
Were you successful in implementing column attention implementation, as i still struggling with it.
Thanks
Anshu

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