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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Calculation of the cross-entropy loss function using the huggingface evaluate module."""
import evaluate
import datasets
import numpy as np
import torch
from torch import nn, Tensor, tensor
_CITATION = """\
@InProceedings{huggingface:module,
title = {Loss Metric},
authors={YU YE},
year={2024}
}
"""
_DESCRIPTION = """\
Calculation of the cross-entropy loss function using the huggingface evaluate module.
"""
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of predictions to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Returns:
loss: description of the first score,
Examples:
Examples should be written in doctest format, and should illustrate how
to use the function.
>>> my_new_module = evaluate.load("Aye10032/loss_metric")
>>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
{'loss': 1.0}
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class LossMetric(evaluate.Metric):
"""Calculation of the cross-entropy loss function using the huggingface evaluate module."""
def _info(self):
return evaluate.MetricInfo(
# This is the description that will appear on the modules page.
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features({
'predictions': datasets.Value('int64'),
'references': datasets.Value('int64'),
}),
# Homepage of the module for documentation
homepage="http://module.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
reference_urls=["http://path.to.reference.url/new_module"]
)
def _compute(self, predictions, references):
"""Returns the scores"""
pred = tensor(np.array(predictions), dtype=torch.float16)
label = tensor(np.array(references), dtype=torch.float16)
loss_func = nn.CrossEntropyLoss()
loss = loss_func(pred, label)
mean_loss = loss.item() / label.shape[0]
return {
"loss": mean_loss,
}
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