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| | """TODO: Add a description here.""" |
| |
|
| | import evaluate |
| | import datasets |
| |
|
| |
|
| | |
| | _CITATION = """\ |
| | @InProceedings{huggingface:module, |
| | title = {A great new module}, |
| | authors={huggingface, Inc.}, |
| | year={2020} |
| | } |
| | """ |
| |
|
| | |
| | _DESCRIPTION = """\ |
| | This new module is designed to solve this great ML task and is crafted with a lot of care. |
| | """ |
| |
|
| |
|
| | |
| | _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: |
| | accuracy: description of the first score, |
| | another_score: description of the second score, |
| | Examples: |
| | Examples should be written in doctest format, and should illustrate how |
| | to use the function. |
| | |
| | >>> my_new_module = evaluate.load("my_new_module") |
| | >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1]) |
| | >>> print(results) |
| | {'accuracy': 1.0} |
| | """ |
| |
|
| | |
| | BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt" |
| |
|
| |
|
| | @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) |
| | class codebleu2(evaluate.Metric): |
| | """TODO: Short description of my evaluation module.""" |
| |
|
| | def _info(self): |
| | |
| | return evaluate.MetricInfo( |
| | |
| | module_type="metric", |
| | description=_DESCRIPTION, |
| | citation=_CITATION, |
| | inputs_description=_KWARGS_DESCRIPTION, |
| | |
| | features=datasets.Features({ |
| | 'predictions': datasets.Value('int64'), |
| | 'references': datasets.Value('int64'), |
| | }), |
| | |
| | homepage="http://module.homepage", |
| | |
| | codebase_urls=["http://github.com/path/to/codebase/of/new_module"], |
| | reference_urls=["http://path.to.reference.url/new_module"] |
| | ) |
| |
|
| | def _download_and_prepare(self, dl_manager): |
| | """Optional: download external resources useful to compute the scores""" |
| | |
| | pass |
| |
|
| | def _compute(self, predictions, references): |
| | """Returns the scores""" |
| | |
| | accuracy = sum(i == j for i, j in zip(predictions, references)) / len(predictions) |
| | return { |
| | "accuracy": accuracy, |
| | } |