caching in map causes same result to be returned for train, validation and test
Hi @dpressel, 

thanks for posting your issue! Can you maybe add a complete code snippet that we can copy paste to reproduce the error? For example, I'm not sure where the variable `train_set` comes from in your code and it seems like you are loading multiple datasets at once?  
hello,

I am working on a program that uses the `nlp` library with the `SST2` dataset.

The rough outline of the program is:

```
import nlp as nlp_datasets
...
parser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')
...
dataset = nlp_datasets.load_dataset(*args.dataset)
...
# Create feature vocabs
vocabs = create_vocabs(dataset.values(), vectorizers)
...
# Create a function to vectorize based on vectorizers and vocabs:

print('TS', train_set.num_rows)
print('VS', valid_set.num_rows)
print('ES', test_set.num_rows)

# factory method to create a `convert_to_features` function based on vocabs
convert_to_features = create_featurizer(vectorizers, vocabs)
train_set = train_set.map(convert_to_features, batched=True)
train_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)

valid_set = valid_set.map(convert_to_features, batched=True)
valid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
valid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)

test_set = test_set.map(convert_to_features, batched=True)
test_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)

print('TS', train_set.num_rows)
print('VS', valid_set.num_rows)
print('ES', test_set.num_rows)

```
Im not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:

```
TS 67349
VS 872
ES 1821
TS 67349
VS 67349
ES 67349
```

The behavior changes if I turn off the caching but then the results fail:

```
train_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)
...
valid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)
...
test_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)
```

Now I get the right set of features back...
```
TS 67349
VS 872
ES 1821
100%|██████████| 68/68 [00:00<00:00, 92.78it/s]
100%|██████████| 1/1 [00:00<00:00, 75.47it/s]
  0%|          | 0/2 [00:00<?, ?it/s]TS 67349
VS 872
ES 1821
100%|██████████| 2/2 [00:00<00:00, 77.19it/s]
```
but I think its losing track of the original training set:

```
Traceback (most recent call last):
  File "/home/dpressel/dev/work/baseline/api-examples/layers-classify-hf-datasets.py", line 148, in <module>
    for x in train_loader:
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
    data = self._next_data()
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 385, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__
    output_all_columns=self._output_all_columns,
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 294, in _getitem
    outputs = self._unnest(self._data.slice(key, 1).to_pydict())
  File "pyarrow/table.pxi", line 1211, in pyarrow.lib.Table.slice
  File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table
  File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)

Process finished with exit code 1
```

The full-example program (minus the print stmts) is here:
https://github.com/dpressel/mead-baseline/pull/620/files


caching in map causes same result to be returned for train, validation and test 
 hello,

I am working on a program that uses the `nlp` library with the `SST2` dataset.

The rough outline of the program is:

```
import nlp as nlp_datasets
...
parser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')
...
dataset = nlp_datasets.load_dataset(*args.dataset)
...
# Create feature vocabs
vocabs = create_vocabs(dataset.values(), vectorizers)
...
# Create a function to vectorize based on vectorizers and vocabs:

print('TS', train_set.num_rows)
print('VS', valid_set.num_rows)
print('ES', test_set.num_rows)

# factory method to create a `convert_to_features` function based on vocabs
convert_to_features = create_featurizer(vectorizers, vocabs)
train_set = train_set.map(convert_to_features, batched=True)
train_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)

valid_set = valid_set.map(convert_to_features, batched=True)
valid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
valid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)

test_set = test_set.map(convert_to_features, batched=True)
test_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)

print('TS', train_set.num_rows)
print('VS', valid_set.num_rows)
print('ES', test_set.num_rows)

```
Im not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:

```
TS 67349
VS 872
ES 1821
TS 67349
VS 67349
ES 67349
```

The behavior changes if I turn off the caching but then the results fail:

```
train_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)
...
valid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)
...
test_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)
```

Now I get the right set of features back...
```
TS 67349
VS 872
ES 1821
100%|██████████| 68/68 [00:00<00:00, 92.78it/s]
100%|██████████| 1/1 [00:00<00:00, 75.47it/s]
  0%|          | 0/2 [00:00<?, ?it/s]TS 67349
VS 872
ES 1821
100%|██████████| 2/2 [00:00<00:00, 77.19it/s]
```
but I think its losing track of the original training set:

```
Traceback (most recent call last):
  File "/home/dpressel/dev/work/baseline/api-examples/layers-classify-hf-datasets.py", line 148, in <module>
    for x in train_loader:
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
    data = self._next_data()
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 385, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__
    output_all_columns=self._output_all_columns,
  File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 294, in _getitem
    outputs = self._unnest(self._data.slice(key, 1).to_pydict())
  File "pyarrow/table.pxi", line 1211, in pyarrow.lib.Table.slice
  File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table
  File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)

Process finished with exit code 1
```

The full-example program (minus the print stmts) is here:
https://github.com/dpressel/mead-baseline/pull/620/files

 
 Hi @dpressel, 

thanks for posting your issue! Can you maybe add a complete code snippet that we can copy paste to reproduce the error? For example, I'm not sure where the variable `train_set` comes from in your code and it seems like you are loading multiple datasets at once?  