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scirepeval / scirepeval.py
Amanpreet Singh
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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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
import glob
import datasets
from datasets.data_files import DataFilesDict
from .scirepeval_configs import SCIREPEVAL_CONFIGS
#from datasets.packaged_modules.json import json
from datasets.utils.logging import get_logger
logger = get_logger(__name__)
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2021}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
"second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class Scirepeval(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = SCIREPEVAL_CONFIGS
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=self.config.description,
# This defines the different columns of the dataset and their types
features=datasets.Features(self.config.features), # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage="",
# License for the dataset if available
license=self.config.license,
# Citation for the dataset
citation=self.config.citation,
)
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
base_url = "https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval"
data_urls = dict()
data_dir = self.config.url if self.config.url else self.config.name
if self.config.is_training:
data_urls = {"train": f"{base_url}/train/{data_dir}/train.jsonl"}
if "refresh" not in self.config.name:
data_urls.update({"val": f"{base_url}/train/{data_dir}/val.jsonl"})
if "cite_prediction" not in self.config.name:
data_urls.update({"test": f"{base_url}/test/{data_dir}/meta.jsonl"})
# print(data_urls)
downloaded_files = dl_manager.download_and_extract(data_urls)
# print(downloaded_files)
splits = []
if "test" in downloaded_files:
splits = [datasets.SplitGenerator(
name=datasets.Split("evaluation"),
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["test"],
"split": "evaluation"
},
),
]
if "train" in downloaded_files:
splits.append(
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["train"],
"split": "train",
},
))
if "val" in downloaded_files:
splits.append(datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["val"],
"split": "validation",
}))
return splits
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
def read_data(data_path):
task_data = []
try:
task_data = json.load(open(data_path, "r", encoding="utf-8"))
except:
with open(data_path) as f:
task_data = [json.loads(line) for line in f]
if type(task_data) == dict:
task_data = list(task_data.values())
return task_data
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
# data = read_data(filepath)
seen_keys = set()
IGNORE=set(["n_key_citations", "session_id", "user_id", "user"])
logger.warning(filepath)
with open(filepath, encoding="utf-8") as f:
for line in f:
d = json.loads(line)
d = {k:v for k,v in d.items() if k not in IGNORE}
key="doc_id" if "cite_prediction_" not in self.config.name else "corpus_id"
if self.config.task_type == "proximity":
if "cite_prediction" in self.config.name:
if "arxiv_id" in d["query"]:
for item in ["query", "pos", "neg"]:
del d[item]["arxiv_id"]
del d[item]["doi"]
if "fos" in d["query"]:
del d["query"]["fos"]
if "score" in d["pos"]:
del d["pos"]["score"]
yield str(d["query"][key]) + str(d["pos"][key]) + str(d["neg"][key]), d
else:
if d["query"][key] not in seen_keys:
seen_keys.add(d["query"][key])
yield str(d["query"][key]), d
else:
if d[key] not in seen_keys:
seen_keys.add(d[key])
if self.config.task_type != "search":
if "corpus_id" not in d:
d["corpus_id"] = None
if "scidocs" in self.config.name:
if "cited by" not in d:
d["cited_by"] = []
if type(d["corpus_id"]) == str:
d["corpus_id"] = None
yield d[key], d