[`feat`] Move dataset card creation to method for easier overriding
Hello!

## Pull Request overview
* Move dataset card creation to method for easier overriding

## Details
It's common for me to fully automatically download, reformat, and upload a dataset (e.g. see https://huggingface.co/datasets?other=sentence-transformers), but one aspect that I cannot easily automate is the dataset card generation. This is because during `push_to_hub`, the dataset card is created in 3 lines of code in a much larger method. To automatically generate a dataset card, I need to either:
1. Subclass `Dataset`/`DatasetDict`, copy the entire `push_to_hub` method to override the ~3 lines used to generate the dataset card. This is not viable as the method is likely to change over time.
2. Use `push_to_hub` normally, then separately download the pushed (but empty) dataset card, update it, and reupload the modified dataset. This works fine, but prevents me from being able to return a `Dataset` to my users which will automatically use a nice dataset card.

So, in this PR I'm proposing to move the dataset generation into another method so that it can be overridden more easily. For example, imagine the following use case:
````python

import json
from typing import Any, Dict, Optional
from datasets import Dataset, load_dataset
from datasets.info import DatasetInfosDict, DatasetInfo
from datasets.utils.metadata import MetadataConfigs
from huggingface_hub import DatasetCardData, DatasetCard

TEMPLATE = r"""---
{dataset_card_data}
---

# Dataset Card for {source_dataset_name} with mined hard negatives

This dataset is a collection of {column_one}-{column_two}-negative triplets from the {source_dataset_name} dataset. See [{source_dataset_name}](https://huggingface.co/datasets/{source_dataset_id}) for additional information. This dataset can be used directly with Sentence Transformers to train embedding models.

## Mining Parameters
The negative samples have been mined using the following parameters:
- `range_min`: {range_min}, i.e. we skip the {range_min} most similar samples
- `range_max`: {range_max}, i.e. we only look at the top {range_max} most similar samples
- `margin`: {margin}, i.e. we require negative similarity + margin < positive similarity, so negative samples can't be more similar than the known true answer
- `sampling_strategy`: {sampling_strategy}, i.e. whether to randomly sample from the candidate negatives or take the "top" negatives
- `num_negatives`: {num_negatives}, i.e. we mine {num_negatives} negatives per question-answer pair

## Dataset Format
- Columns: {column_one}, {column_two}, negative
- Column types: str, str, str
- Example:
```python
{example}
```
"""

class HNMDataset(Dataset):
    @classmethod
    def from_dict(cls, *args, mining_kwargs: Dict[str, Any], **kwargs) -> "HNMDataset":
        dataset = super().from_dict(*args, **kwargs)
        dataset.mining_kwargs = mining_kwargs
        return dataset
    
    def _create_dataset_card(
        self,
        dataset_card_data: DatasetCardData,
        dataset_card: Optional[DatasetCard],
        config_name: str,
        info_to_dump: DatasetInfo,
        metadata_config_to_dump: MetadataConfigs,
    ) -> DatasetCard:
        if dataset_card:
            return dataset_card

        DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)
        MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)
        dataset_card_data.tags = ["sentence-transformers"]
        dataset_name = self.mining_kwargs["source_dataset"].info.dataset_name
        # Very messy, just as an example:
        dataset_id = list(self.mining_kwargs["source_dataset"].info.download_checksums.keys())[0].removeprefix("hf://datasets/").split("@")[0]
        content = TEMPLATE.format(**{
            "dataset_card_data": str(dataset_card_data),
            "source_dataset_name": dataset_name,
            "source_dataset_id": dataset_id,
            "range_min": self.mining_kwargs["range_min"],
            "range_max": self.mining_kwargs["range_max"],
            "margin": self.mining_kwargs["margin"],
            "sampling_strategy": self.mining_kwargs["sampling_strategy"],
            "num_negatives": self.mining_kwargs["num_negatives"],
            "column_one": self.column_names[0],
            "column_two": self.column_names[1],
            "example": json.dumps(self[0], indent=4),
        })
        return DatasetCard(content)

source_dataset = load_dataset("sentence-transformers/gooaq", split="train[:100]")
dataset = HNMDataset.from_dict({
    "query": source_dataset["question"],
    "answer": source_dataset["answer"],
    # "negative": ... <- In my case, this column would be 'mined' automatically with these parameters
}, mining_kwargs={
    "range_min": 10,
    "range_max": 20,
    "max_score": 0.9,
    "margin": 0.1,
    "sampling_strategy": "random",
    "num_negatives": 3,
    "source_dataset": source_dataset,
})
dataset.push_to_hub("tomaarsen/mining_demo", private=True)
````

In this script, I've created a subclass which stores some additional information about how the dataset was generated. It's a bit hacky (e.g. setting a `mining_kwargs` parameter in `from_dict` that wasn't created in `__init__`, but that's just a consequence of how the `from_...` methods don't accept kwargs), but it allows me to create a "hard negatives mining" function that returns a dataset which people can use locally like normal, but if they choose to upload it, then it'll automatically include some information, e.g.: https://huggingface.co/datasets/tomaarsen/mining_demo

This allows others to actually find this dataset (e.g. via the `sentence-transformers` tag) and get an idea of the quality, source, etc. by looking at the model card.

## Note
I'm not fixed on this solution whatsoever: I am also completely fine with other solutions, e.g. a `dataset.set_dataset_card_creator` method that allows me to provide a function without even having to subclass anything. I'm open to all ideas :)

cc @albertvillanova @lhoestq 
cc @LysandreJik 

- Tom Aarsen