Repository: pykeen/pykeen
Issue: `LabelBasedInitializer` sample code may be missing an argument `triples_factory`
Root cause: Documentation Deficiency

### Describe the bug

Hello! 

When following the instructions in the [LabelBasedInitializer](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.init.LabelBasedInitializer.html#pykeen.nn.init.LabelBasedInitializer) docs, I run into this error:

```
Traceback (most recent call last):
  File "~/train_pykeen.py", line 77, in <module>
    model = ERMLPE(
  File "~/miniconda3/envs/py39/lib/python3.9/site-packages/pykeen/models/unimodal/ermlpe.py", line 90, in __init__
    super().__init__(
TypeError: __init__() missing 1 required keyword-only argument: 'triples_factory'
```

Potentially caused because the `ERMLPE` model makes a call to the `__init__()` method of `ERModel`, which expects a `triples_factory`. 

### How to reproduce

```python
from pykeen.pipeline import pipeline
from pykeen.models import ERMLPE
from pykeen.datasets import get_dataset
from pykeen.nn.init import LabelBasedInitializer

dataset = get_dataset(dataset="WN18RR")
model = ERMLPE(
    embedding_dim=768,  # for BERT base
    entity_initializer=LabelBasedInitializer.from_triples_factory(
        triples_factory=dataset.training,
        encoder="transformer",
    ),
)

run_name = "test_run"

result = pipeline(
    dataset=dataset,
    model=model,
    epochs=1,
    device="gpu",
    training_kwargs={"checkpoint_name": f"{run_name}_checkpoint.pt", 
                     "checkpoint_frequency": 10, 
                     "continue_training": False,
                     "callbacks": ["evaluation-loss", "evaluation-loop"],
                    "callbacks_kwargs": [
                        {"triples_factory": dataset.validation, 
                        "prefix": "validation",},  
                        {"factory": dataset.training,
                            "frequency": 10,
                            "prefix": "training",}
                        ],
                    },
    optimizer_kwargs={"lr": 0.001},
    clear_optimizer=False,
    stopper="early",
    stopper_kwargs={"frequency": 10, 
                    "patience": 20, 
                    "relative_delta": 0.002},
    result_tracker="csv",
    result_tracker_kwargs={"name": run_name},
    random_seed=42
)
```

### Environment

```
| Key             | Value                    |
|-----------------|--------------------------|
| OS              | posix                    |
| Platform        | Linux                    |
| Release         | 5.4.0-1063-aws           |
| Time            | Fri Jun 21 22:34:57 2024 |
| Python          | 3.9.19                   |
| PyKEEN          | 1.10.2                   |
| PyKEEN Hash     | UNHASHED                 |
| PyKEEN Branch   |                          |
| PyTorch         | 2.3.1+cu121              |
| CUDA Available? | true                     |
| CUDA Version    | 12.1                     |
| cuDNN Version   | 8903                     |
```

### Additional information

_No response_

### Issue Template Checks

- [X] This is not a feature request (use a different issue template if it is)
- [X] This is not a question (use the [discussions](https://github.com/pykeen/pykeen/discussions/new) forum instead)
- [X] I've read the text explaining why including environment information is important and understand if I omit this information that my issue will be dismissed

Hi @thefirebanks ,

thanks for reporting the issue. Here is a smaller examples which can reproduce the problem
```python
from pykeen.datasets import get_dataset
from pykeen.models import ERMLPE
from pykeen.nn.init import LabelBasedInitializer

dataset = get_dataset(dataset="nations")
model = ERMLPE(
    embedding_dim=16,
    entity_initializer=LabelBasedInitializer.from_triples_factory(
        triples_factory=dataset.training,
        encoder="characterembedding",
        encoder_kwargs=dict(dim=16),
    ),
)
```

and here how it could be fixed
```python
from pykeen.datasets import get_dataset
from pykeen.models import ERMLPE
from pykeen.nn.init import LabelBasedInitializer

dataset = get_dataset(dataset="nations")
triples_factory = dataset.training
model = ERMLPE(
    triples_factory=triples_factory,
    embedding_dim=16,
    entity_initializer=LabelBasedInitializer.from_triples_factory(
        triples_factory=triples_factory,
        encoder="characterembedding",
        encoder_kwargs=dict(dim=16),
    ),
    relation_initializer="uniform",
)
```

Note that I had to explicitly provide a `relation_inìtializer` since it defaults to re-using the one for entities.

Thanks @mberr !