update cpmfog script

super_scirep.py

@@ -0,0 +1,191 @@
+# 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 datasets
+from datasets.data_files import DataFilesDict
+from .super_scirep_config import SUPERSCIREPEVAL_CONFIGS
+
+# from datasets.packaged_modules.json import json
+
+
+# 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 SuperSciRep(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 = SUPERSCIREPEVAL_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",
+                         "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)
+        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 += [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": downloaded_files["train"],
+                        "split": "train",
+                    },
+                ),
+                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"])
+        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 self.config.name != "cite_prediction_new" 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