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"""
Pointer Sentinel Mixture Models
https://arxiv.org/pdf/1609.07843.pdf
The WikiText language modeling dataset is a collection of over 100 million tokens
extracted from the set of verified Good and Featured articles on Wikipedia.
NOTE: This `Task` is based on WikiText-2.
Homepage: https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/
"""
import re
from lm_eval.base import PerplexityTask
_CITATION = """
@misc{merity2016pointer,
title={Pointer Sentinel Mixture Models},
author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},
year={2016},
eprint={1609.07843},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
def wikitext_detokenizer(string):
# contractions
string = string.replace("s '", "s'")
string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string)
# number separators
string = string.replace(" @-@ ", "-")
string = string.replace(" @,@ ", ",")
string = string.replace(" @.@ ", ".")
# punctuation
string = string.replace(" : ", ": ")
string = string.replace(" ; ", "; ")
string = string.replace(" . ", ". ")
string = string.replace(" ! ", "! ")
string = string.replace(" ? ", "? ")
string = string.replace(" , ", ", ")
# double brackets
string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string)
string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string)
string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string)
string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string)
string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string)
# miscellaneous
string = string.replace("= = = =", "====")
string = string.replace("= = =", "===")
string = string.replace("= =", "==")
string = string.replace(" " + chr(176) + " ", chr(176))
string = string.replace(" \n", "\n")
string = string.replace("\n ", "\n")
string = string.replace(" N ", " 1 ")
string = string.replace(" 's", "'s")
return string
class WikiText(PerplexityTask):
VERSION = 1
DATASET_PATH = "EleutherAI/wikitext_document_level"
DATASET_NAME = "wikitext-2-raw-v1"
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return True
def training_docs(self):
return map(self._process_doc, self.dataset["train"])
def validation_docs(self):
return map(self._process_doc, self.dataset["validation"])
def test_docs(self):
return map(self._process_doc, self.dataset["test"])
def _process_doc(self, doc):
return doc["page"]
def doc_to_target(self, doc):
return wikitext_detokenizer(doc)
def should_decontaminate(self):
return True
def count_words(self, doc):
# count number of words in *original doc before detokenization*
return len(re.split(r"\s+", doc))