[Question] Best way to batch a large dataset?
Also note that since #322 it is now possible to do
```python
ids = [1, 10, 42, 100]
batch = dataset[ids]
```
From my experience it is quite fast but it can take lots of memory for large batches (haven't played that much with it).
Let me know if you think there could be a better way to implement it. (current code is [here](https://github.com/huggingface/nlp/blob/78628649962671b4aaa31a6b24e7275533416845/src/nlp/arrow_dataset.py#L463))
I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:

```python
train_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)
columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']
train_tf_dataset.set_format(type='tensorflow', columns=columns)
features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} 
labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
### Question about this last line ###
tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
```

This code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.

So I tried manual batching using `dataset.select()`:

```python
idxs = np.random.randint(len(dataset), size=bsz)
batch = dataset.select(idxs).map(lambda example: {"input_ids": tokenizer(example["text"])})
tf_batch = tf.constant(batch["ids"], dtype=tf.int64)
```

This appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.

Is there a performant scalable way to lazily load batches of nlp Datasets?
[Question] Best way to batch a large dataset? 
 I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:

```python
train_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)
columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']
train_tf_dataset.set_format(type='tensorflow', columns=columns)
features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} 
labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
### Question about this last line ###
tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
```

This code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.

So I tried manual batching using `dataset.select()`:

```python
idxs = np.random.randint(len(dataset), size=bsz)
batch = dataset.select(idxs).map(lambda example: {"input_ids": tokenizer(example["text"])})
tf_batch = tf.constant(batch["ids"], dtype=tf.int64)
```

This appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.

Is there a performant scalable way to lazily load batches of nlp Datasets? 
 Also note that since #322 it is now possible to do
```python
ids = [1, 10, 42, 100]
batch = dataset[ids]
```
From my experience it is quite fast but it can take lots of memory for large batches (haven't played that much with it).
Let me know if you think there could be a better way to implement it. (current code is [here](https://github.com/huggingface/nlp/blob/78628649962671b4aaa31a6b24e7275533416845/src/nlp/arrow_dataset.py#L463))