LightningIROutput

class lightning_ir.base.model.LightningIROutput(scores: Tensor | None = None)

Bases: ModelOutput

Base class for the output of the Lightning IR model. It is a subclass of transformers.ModelOutput.

Parameters:

scores (torch.Tensor | None, optional) – Output relevance scores for query–document pairs, defaults to None

__init__(scores: Tensor | None = None) → None

Args:

Methods

__init__([scores])

Args:

Attributes

scores

clear() → None.  Remove all items from od.

copy() → a shallow copy of od

fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

pop(key[, default]) → v, remove specified key and return the corresponding value.

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

setdefault(*args, **kwargs)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

to_tuple() → tuple[Any]

Convert self to a tuple containing all the attributes/keys that are not None.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values