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# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from typing import Any, Optional, Union
import torch
from torch import Tensor
from torchmetrics import Metric
from torchmetrics.functional.text.squad import (
PREDS_TYPE,
TARGETS_TYPE,
_squad_compute,
_squad_input_check,
_squad_update,
)
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["SQuAD.plot"]
class SQuAD(Metric):
"""Calculate `SQuAD Metric`_ which is a metric for evaluating question answering models.
This metric corresponds to the scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~Dict`): A Dictionary or List of Dictionary-s that map ``id`` and ``prediction_text`` to
the respective values
Example ``prediction``:
.. code-block:: python
{"prediction_text": "TorchMetrics is awesome", "id": "123"}
- ``target`` (:class:`~Dict`): A Dictionary or List of Dictionary-s that contain the ``answers`` and ``id`` in
the SQuAD Format.
Example ``target``:
.. code-block:: python
{
'answers': [{'answer_start': [1], 'text': ['This is a test answer']}],
'id': '1',
}
Reference SQuAD Format:
.. code-block:: python
{
'answers': {'answer_start': [1], 'text': ['This is a test text']},
'context': 'This is a test context.',
'id': '1',
'question': 'Is this a test?',
'title': 'train test'
}
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``squad`` (:class:`~Dict`): A dictionary containing the F1 score (key: "f1"),
and Exact match score (key: "exact_match") for the batch.
Args:
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torchmetrics.text import SQuAD
>>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
>>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
>>> squad = SQuAD()
>>> squad(preds, target)
{'exact_match': tensor(100.), 'f1': tensor(100.)}
"""
is_differentiable: bool = False
higher_is_better: bool = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 100.0
f1_score: Tensor
exact_match: Tensor
total: Tensor
def __init__(
self,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.add_state(name="f1_score", default=torch.tensor(0, dtype=torch.float), dist_reduce_fx="sum")
self.add_state(name="exact_match", default=torch.tensor(0, dtype=torch.float), dist_reduce_fx="sum")
self.add_state(name="total", default=torch.tensor(0, dtype=torch.int), dist_reduce_fx="sum")
def update(self, preds: PREDS_TYPE, target: TARGETS_TYPE) -> None:
"""Update state with predictions and targets."""
preds_dict, target_dict = _squad_input_check(preds, target)
f1_score, exact_match, total = _squad_update(preds_dict, target_dict)
self.f1_score += f1_score
self.exact_match += exact_match
self.total += total
def compute(self) -> dict[str, Tensor]:
"""Aggregate the F1 Score and Exact match for the batch."""
return _squad_compute(self.f1_score, self.exact_match, self.total)
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> from torchmetrics.text import SQuAD
>>> metric = SQuAD()
>>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
>>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> from torchmetrics.text import SQuAD
>>> metric = SQuAD()
>>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
>>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
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