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squad.py
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squad.py
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# Copyright The PyTorch 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 typing import Any, Dict
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,
)
class SQuAD(Metric):
"""Calculate `SQuAD Metric`_ which corresponds to the scoring script for version 1 of the Stanford Question
Answering Dataset (SQuAD).
Args:
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torchmetrics 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.)}
References:
[1] SQuAD: 100,000+ Questions for Machine Comprehension of Text by Pranav Rajpurkar, Jian Zhang, Konstantin
Lopyrev, Percy Liang `SQuAD Metric`_ .
"""
is_differentiable: bool = False
higher_is_better: bool = True
full_state_update: bool = False
f1_score: Tensor
exact_match: Tensor
total: Tensor
def __init__(
self,
**kwargs: Any,
):
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: # type: ignore
"""Compute F1 Score and Exact Match for a collection of predictions and references.
Args:
preds:
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:
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'
}
Raises:
KeyError:
If the required keys are missing in either predictions or 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:
Dictionary containing the F1 score, Exact match score for the batch.
"""
return _squad_compute(self.f1_score, self.exact_match, self.total)