.splits() returns a tuple of datasets, in your case it is of length 1. So

trn = TabularDataset.splits(
...
...
...
fields=tv_datafields)[0]

should work here or you can use a regular TabularDataset constructor instead.

I had the same problem with TabularDataset too
http://mlexplained.com/2018/02/08/a-comprehensive-tutorial-to-torchtext/
This tutorial was helpful.

added these two lines and it worked like a charm

thanks @cheryllwl , this should be documented properly.

@tu-artem Can you please elaborate on what adding the index [0] does?
From what I gather the splits() method returns a Dataset object as a tuple containing Example objects (instances/rows)
So, if I write;
train, val = torchtext.data.TabularDataset.splits(path='./', train = "train.csv", test = "test.csv", format='csv', fields=data_fields, skip_header = True)
I will get a Dataset object which is a tuple containing all training instances in train variable and another Dataset object containing all test instances in val variable. Am I right?
In this case, please help me understand what the indexing [0] does. Thanks.

@kunjmehta in your case you are already doing tuple unpacking via multiple assignment train, val = ..., so you don't need any further indexing

What worked for me was to simply add sort=False, as sorting was not needed in my case.

What worked for me was to simply add sort=False, as sorting was not needed in my case.

This worked for me too. I added sort=False as a parameter in the BucketIterator.