Feature Request: Dataset.add_item
Hi @sshleifer :) 

We don't have methods like `Dataset.add_batch` or `Dataset.add_entry/add_item` yet.
But that's something we'll add pretty soon. Would an API that looks roughly like this help ? Do you have suggestions ?
```python
import numpy as np
from datasets import Dataset

tokenized = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])

# API suggestion (not available yet)
d = Dataset()
for input_ids in tokenized:
    d.add_item({"input_ids": input_ids})

print(d[0]["input_ids"])
# [4, 4, 2]
```

Currently you can define a dataset with what @albertvillanova suggest, or via a generator using dataset builders. It's also possible to [concatenate datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets).
I'm trying to integrate `huggingface/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.
Is this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.

### Desired API

```python
import numpy as np
tokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])

def build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:
   """FIXME"""
   dataset = EmptyDataset()
   for t in tokenized: dataset.append(t)
   return dataset
ds = build_dataset_from_tokenized(tokenized)
assert (ds[0] == np.array([4,4,2])).all()
```

### What I tried
grep, google for "add one entry at a time", "datasets.append"

### Current Code
This code achieves the same result but doesn't fit into the `add_item` abstraction.

```python
    dataset = load_dataset('text', data_files={'train': 'train.txt'})
    tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)
    def tokenize_function(examples):
        ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']
        return {'input_ids': [x[1:] for x in ids]}
    ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)
	print(ds['train'][0]) => np array
```

Thanks in advance!
Feature Request: Dataset.add_item 
 I'm trying to integrate `huggingface/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.
Is this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.

### Desired API

```python
import numpy as np
tokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])

def build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:
   """FIXME"""
   dataset = EmptyDataset()
   for t in tokenized: dataset.append(t)
   return dataset
ds = build_dataset_from_tokenized(tokenized)
assert (ds[0] == np.array([4,4,2])).all()
```

### What I tried
grep, google for "add one entry at a time", "datasets.append"

### Current Code
This code achieves the same result but doesn't fit into the `add_item` abstraction.

```python
    dataset = load_dataset('text', data_files={'train': 'train.txt'})
    tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)
    def tokenize_function(examples):
        ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']
        return {'input_ids': [x[1:] for x in ids]}
    ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)
	print(ds['train'][0]) => np array
```

Thanks in advance! 
 Hi @sshleifer :) 

We don't have methods like `Dataset.add_batch` or `Dataset.add_entry/add_item` yet.
But that's something we'll add pretty soon. Would an API that looks roughly like this help ? Do you have suggestions ?
```python
import numpy as np
from datasets import Dataset

tokenized = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])

# API suggestion (not available yet)
d = Dataset()
for input_ids in tokenized:
    d.add_item({"input_ids": input_ids})

print(d[0]["input_ids"])
# [4, 4, 2]
```

Currently you can define a dataset with what @albertvillanova suggest, or via a generator using dataset builders. It's also possible to [concatenate datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets).