src/transformers/data/data_collator.py/numpy_default_data_collator
def numpy_default_data_collator(features: List[InputDataClass]) -> Dict[str, Any]:
    if not isinstance(features[0], Mapping):
        features = [vars(f) for f in features]
    first = features[0]
    batch = {}

    # Special handling for labels.
    # Ensure that tensor is created with the correct type
    # (it should be automatically the case, but let's make sure of it.)
    if "label" in first and first["label"] is not None:
        label = first["label"].item() if isinstance(first["label"], np.ndarray) else first["label"]
        dtype = np.int64 if isinstance(label, int) else np.float32
        batch["labels"] = np.array([f["label"] for f in features], dtype=dtype)
    elif "label_ids" in first and first["label_ids"] is not None:
        if isinstance(first["label_ids"], np.ndarray):
            batch["labels"] = np.stack([f["label_ids"] for f in features])
        else:
            dtype = np.int64 if type(first["label_ids"][0]) is int else np.float32
            batch["labels"] = np.array([f["label_ids"] for f in features], dtype=dtype)

    # Handling of all other possible keys.
    # Again, we will use the first element to figure out which key/values are not None for this model.
    for k, v in first.items():
        if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
            if isinstance(v, np.ndarray):
                batch[k] = np.stack([f[k] for f in features])
            else:
                batch[k] = np.array([f[k] for f in features])

    return batch