Remove scripts altogether
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7592). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. Hi @lhoestq,
I wanted to ask
are you planning to stop supporting dataset builds using `GeneratorBasedBuilder`?

If so, could you share the reason why? We stopped supporting dataset scripts altogether, whether they are based on GeneratorBasedBuilder or any other builder. This means you can't `load_dataset()` a dataset script anymore. We did this mostly for security reasons which is blocking for many users and also impossible to build upon (e.g. the for the Dataset Viewer on HF) Ah, so only the `trust_remote_code` feature of `load_dataset` is deprecated, and

```python
from datasets import load_dataset_builder
  
builder = load_dataset_builder('cornell-movie-review-data/rotten_tomatoes')  
builder.download_and_prepare()  
```

we can still load data using `load_dataset_builder` and `download_and_prepare`, right?
That's a relief. I thought the removal of `trust_remote_code` in `load_dataset` meant `GeneratorBasedBuilder` was being deprecated too, haha.
Got it, thanks for the clarification!
 Can you give an example on how to upgrade from using `trust_remote_code`? I used to load_dataset from a script generating my training data in a streaming way. For guys who dislike this change +1 > Can you give an example on how to upgrade from using trust_remote_code? I used to load_dataset from a script generating my training data in a streaming way.

here is a command to convert a script based dataset on HF to a Parquet dataset:

```
uv run --with "datasets==3.6.0" datasets-cli convert_to_parquet <username/dataset-name> --trust_remote_code
```

And if you want to define a streaming pipeline, feel free to use the `IterableDataset` API instead:

```python
from datasets import load_dataset

ds = load_dataset(..., streaming=True)  # source data
ds = ds.map(...)  # preprocessing
ds = ds.filter(...)  # remove bad samples

# For training
# data_loader = DataLoader(ds, ...)

# Saving the dataset
# ds.push_to_hub(...)
```
TODO:
- [x] remplace fixtures based on script with no-script fixtures
- [x] windaube