Fork dataset
Thanks for the helpful advice, @lhoestq  -- I wasn't quite able to get the json recipe working - 

```
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/ipc.py in __init__(self, source)
     60 
     61     def __init__(self, source):
---> 62         self._open(source)
     63 
     64 
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/ipc.pxi in pyarrow.lib._RecordBatchStreamReader._open()
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Tried reading schema message, was null or length 0
```

But I'm going to give the generator_dataset_builder a try.

1 more quick question -- can .map be used to output different length mappings -- could I skip one, or yield 2, can you map_batch  
We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. 

We're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.

Our preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.

Is there some good way to "fork" dataset-

EG

1. text + json -> Dataset1
1. Dataset1 -> DatasetNER
1. Dataset1 -> DatasetREL

or 

1. text + json -> Dataset1
1. Dataset1 -> DatasetNER
1. Dataset1 + DatasetNER -> DatasetREL


Fork dataset 
 We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. 

We're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.

Our preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.

Is there some good way to "fork" dataset-

EG

1. text + json -> Dataset1
1. Dataset1 -> DatasetNER
1. Dataset1 -> DatasetREL

or 

1. text + json -> Dataset1
1. Dataset1 -> DatasetNER
1. Dataset1 + DatasetNER -> DatasetREL

 
 Thanks for the helpful advice, @lhoestq  -- I wasn't quite able to get the json recipe working - 

```
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/ipc.py in __init__(self, source)
     60 
     61     def __init__(self, source):
---> 62         self._open(source)
     63 
     64 
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/ipc.pxi in pyarrow.lib._RecordBatchStreamReader._open()
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Tried reading schema message, was null or length 0
```

But I'm going to give the generator_dataset_builder a try.

1 more quick question -- can .map be used to output different length mappings -- could I skip one, or yield 2, can you map_batch  