Problems after upgrading to 2.6.1
Hi! I can't reproduce the error following these steps. Can you please provide a reproducible example? I faced the same issue:

### Repro
```
!pip install datasets==2.6.1
import datasets as Dataset
dataset = Dataset.from_pandas(dataframe)
dataset.save_to_disk(local)

!pip install datasets==2.5.2
import datasets as Dataset
dataset = Dataset.load_from_disk(local)
```

 @Lokiiiiii And what are the contents of the "dataframe" in your example? I bumped into the issue too. @Lokiiiiii thanks for steps. I "solved" if for now by `pip install datasets>=2.6.1` everywhere. Hi all, 
I experienced the same issue. 
Please note that the pull request is related to the IMDB example provided in the doc, and is a fix for that, in that context, to make sure that people can follow the doc example and have a working system. 
It does not provide a fix for Datasets itself.  im getting the same error.
- using the base AWS HF container that uses a datasets <2.
- updating the AWS HF container to use dataset 2.4
 Same here, running on our SageMaker pipelines. It's only happening for some but not all of our saved Datasets. I am also receiving this error on Sagemaker but not locally, I have noticed that this occurs when the `.dataset/` folder does not contain a single file like:

`dataset.arrow`

but instead contains multiple files like:

`data-00000-of-00002.arrow`
`data-00001-of-00002.arrow`

I think that it may have something to do with this recent PR that updated the behaviour of `dataset.save_to_disk` by introducing sharding: https://github.com/huggingface/datasets/pull/5268

For now I can get around this by forcing datasets==2.8.0 on machine that creates dataset and in the huggingface instance for training (by running this at the start of training script `os.system("pip install datasets==2.8.0")`)

To ensure the dataset is a single shard when saving the dataset locally:

```python3
dataset.flatten_indices().save_to_disk('path/to/dataset', num_shards=1)
```

 and then manually changing the name afterwards from `path/to/dataset/data-00000-of-00001.arrow` to `path/to/dataset/dataset.arrow` and updating the `path/to/dataset/state.json` to reflect this name change. i.e. by changing `state.json` to this:

```javascript
{
  "_data_files": [
    {
      "filename": "dataset.arrow"
    }
  ],
  "_fingerprint": "420086f0636f8727",
  "_format_columns": null,
  "_format_kwargs": {},
  "_format_type": null,
  "_output_all_columns": false,
  "_split": null
}
``` Does anyone know if this has been resolved? I have the same issue in datasets version 2.3.2
### Describe the bug

Loading a dataset_dict from disk with `load_from_disk` is now creating a `KeyError "length"` that was not occurring in v2.5.2.

Context: 
- Each individual dataset in the dict is created with `Dataset.from_pandas` 
- The dataset_dict is create from a dict of `Dataset`s, e.g., `DatasetDict({"train": train_ds, "validation": val_ds})
- The pandas dataframe, besides text columns, has a column with a dictionary inside and potentially different keys in each row. Correctly the `Dataset.from_pandas` function adds `key: None` to all dictionaries in each row so that the schema can be correctly inferred.

### Steps to reproduce the bug

Steps to reproduce:

- Upgrade to datasets==2.6.1
- Create a dataset from pandas dataframe with `Dataset.from_pandas`
- Create a dataset_dict from a dict of `Dataset`s, e.g., `DatasetDict({"train": train_ds, "validation": val_ds})
- Save to disk with the `save` function

### Expected behavior

Same as in v2.5.2, that is load from disk without errors

### Environment info

- `datasets` version: 2.6.1
- Platform: Linux-5.4.209-129.367.amzn2int.x86_64-x86_64-with-glibc2.26
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1