Bugs : dataset.map() is frozen on ELI5
I created a PR to fix the issue.
It was due to an overflow check that handled badly an empty list.

You can try the changes by using 
```
!pip install git+https://github.com/huggingface/nlp.git@fix-bad-type-in-overflow-check
```

Also I noticed that the first 1000 examples have an empty list in the `title_urls` field. The feature type inference in `.map` will consider it `null` because of that, and it will crash when it encounter the next example with a `title_urls` that is not empty.

Therefore to fix that, what you can do for now is increase the writer batch size so that the feature inference will take into account at least one example with a non-empty `title_urls`:

```python
# default batch size is 1_000 and it's not enough for feature type inference because of empty lists
valid_dataset = valid_dataset.map(make_input_target, writer_batch_size=3_000) 
```

I was able to run the frozen cell with these changes.
Hi Huggingface Team!

Thank you guys once again for this amazing repo.

I have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) 

However, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 / 0.17.0 / 1.0.0 also have the same frozen process.

Reproducible code can be found on [this colab notebook ](https://colab.research.google.com/drive/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.

----------------------------------------
**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)

I also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ?
Bugs : dataset.map() is frozen on ELI5 
 Hi Huggingface Team!

Thank you guys once again for this amazing repo.

I have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) 

However, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 / 0.17.0 / 1.0.0 also have the same frozen process.

Reproducible code can be found on [this colab notebook ](https://colab.research.google.com/drive/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.

----------------------------------------
**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)

I also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ? 
 I created a PR to fix the issue.
It was due to an overflow check that handled badly an empty list.

You can try the changes by using 
```
!pip install git+https://github.com/huggingface/nlp.git@fix-bad-type-in-overflow-check
```

Also I noticed that the first 1000 examples have an empty list in the `title_urls` field. The feature type inference in `.map` will consider it `null` because of that, and it will crash when it encounter the next example with a `title_urls` that is not empty.

Therefore to fix that, what you can do for now is increase the writer batch size so that the feature inference will take into account at least one example with a non-empty `title_urls`:

```python
# default batch size is 1_000 and it's not enough for feature type inference because of empty lists
valid_dataset = valid_dataset.map(make_input_target, writer_batch_size=3_000) 
```

I was able to run the frozen cell with these changes.