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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.
import evaluate
import datasets
from .tokenizer_13a import Tokenizer13a
_CITATION = """\
@inproceedings{liu-etal-2022-rethinking,
title = "Rethinking and Refining the Distinct Metric",
author = "Liu, Siyang and
Sabour, Sahand and
Zheng, Yinhe and
Ke, Pei and
Zhu, Xiaoyan and
Huang, Minlie",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
year = "2022",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.86",
doi = "10.18653/v1/2022.acl-short.86",
}
@inproceedings{li-etal-2016-diversity,
title = "A Diversity-Promoting Objective Function for Neural Conversation Models",
author = "Li, Jiwei and
Galley, Michel and
Brockett, Chris and
Gao, Jianfeng and
Dolan, Bill",
booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
year = "2016",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N16-1014",
doi = "10.18653/v1/N16-1014",
}
"""
# 
_DESCRIPTION = """\
Distinct metric is to calculate corpus-level diversity of language. We provide two versions of distinct score. Expectation-Adjusted-Distinct (EAD) is the default one, which removes
the biases of the original distinct score on lengthier sentences (see Figure below). Distinct is the original version.
"""
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of sentecnes. Each prediction should be a string.
Returns:
Expectation-Adjusted-Distinct
Distinct-1
Distinct-2
Distinct-3
Examples:
Examples should be written in doctest format, and should illustrate how
to use the function.
>>> my_new_module = evaluate.load("lsy641/distinct")
>>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], vocab_size=50257)
>>> print(results)
>>> dataset = ["This is my friend jack", "I'm sorry to hear that", "But you know I am the one who always support you", "Welcome to our family"]
>>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], dataForVocabCal = dataset)
>>> print(results)
>>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], mode="Distinct")
>>> print(results)
"""
# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class distinct(evaluate.Measurement):
def _info(self):
return evaluate.MeasurementInfo(
# This is the description that will appear on the modules page.
module_type="measurement",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features({
'predictions': datasets.Value('string')
}),
# Homepage of the module for documentation
homepage="https://huggingface.co/spaces/lsy641/distinct",
# Additional links to the codebase or references
codebase_urls=["https://github.com/lsy641/Expectation-Adjusted-Distinct/tree/main"],
reference_urls=["https://aclanthology.org/2022.acl-short.86/"]
)
def _download_and_prepare(self, dl_manager):
"""Optional: download external resources useful to compute the scores"""
def _compute(self, predictions, dataForVocabCal=None, vocab_size=None, tokenizer=Tokenizer13a(), mode="Expectation-Adjusted-Distinct"):
from nltk.util import ngrams
"""Returns the scores"""
if mode == "Expectation-Adjusted-Distinct" and vocab_size is None and dataForVocabCal is None:
raise ValueError("Either vocab_size or dataForVocabCal needs to be specified when using mode 'Expectation-Adjusted-Distinct'. See https://github.com/lsy641/Expectation-Adjusted-Distinct/blob/main/EAD.ipynb for vocab_size specification. \n Or use mode='Distinct' to get original version of distinct score.")
elif mode == "Expectation-Adjusted-Distinct" and vocab_size is not None and dataForVocabCal is not None:
raise Warning("We've detected that both vocab_size and dataForVocabCal are specified. We will use dataForVocabCal.")
elif mode == "Distinct":
pass
if mode == "Expectation-Adjusted-Distinct" and dataForVocabCal is not None:
if isinstance(dataForVocabCal, list) and len(dataForVocabCal) > 0 and isinstance(dataForVocabCal[0], str):
vocab = set()
for sentence in dataForVocabCal:
# if tokenizer == "white_space":
# vocab = vocab | set(sentence.split(" "))
# else:
vocab = vocab | set(tokenizer.tokenize(sentence))
vocab_size = len(vocab)
else:
raise TypeError("Argument dataForVocabCal should be a list of strings")
distinct_tokens = set()
distinct_tokens_2grams = set()
distinct_tokens_3grams = set()
total_tokens = []
total_tokens_2grams = []
total_tokens_3grams = []
for prediction in predictions:
try:
tokens = list(tokenizer.tokenize(prediction))
tokens_2grams = list(ngrams(list(tokenizer.tokenize(prediction)), 2, pad_left=True, left_pad_symbol='<s>'))
tokens_3grams = list(ngrams(list(tokenizer.tokenize(prediction)), 3, pad_left=True, left_pad_symbol='<s>'))
except Exception as e:
raise e
distinct_tokens = distinct_tokens | set(tokens)
distinct_tokens_2grams = distinct_tokens_2grams | set(tokens_2grams)
distinct_tokens_3grams = distinct_tokens_3grams | set(tokens_3grams)
total_tokens.extend(tokens)
total_tokens_2grams.extend(list(tokens_2grams))
total_tokens_3grams.extend(list(tokens_3grams))
Distinct_1 = len(distinct_tokens)/len(total_tokens)
Distinct_2 = len(distinct_tokens_2grams)/len(total_tokens_2grams)
Distinct_3 = len(distinct_tokens_3grams)/len(total_tokens_3grams)
if mode == "Expectation-Adjusted-Distinct":
Expectation_Adjusted_Distinct = len(distinct_tokens)/(vocab_size*(1-((vocab_size-1)/vocab_size)**len(total_tokens)))
return {
"Expectation-Adjusted-Distinct": Expectation_Adjusted_Distinct,
"Distinct-1": Distinct_1,
"Distinct-2": Distinct_2,
"Distinct-3": Distinct_3
}
if mode == "Distinct":
return {
"Distinct-1": Distinct_1,
"Distinct-2": Distinct_2,
"Distinct-3": Distinct_3
} |