upload hubscripts/multi_xscience_hub.py to hub from bigbio repo

multi_xscience.py

@@ -0,0 +1,205 @@
+# coding=utf-8
+# Copyright 2022 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 json
+import os
+from typing import List
+
+import datasets
+
+from .bigbiohub import text2text_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@misc{https://doi.org/10.48550/arxiv.2010.14235,
+  doi = {10.48550/ARXIV.2010.14235},
+  
+  url = {https://arxiv.org/abs/2010.14235},
+  
+  author = {Lu, Yao and Dong, Yue and Charlin, Laurent},
+  
+  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
+  title = {Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
+  
+  publisher = {arXiv},
+  
+  year = {2020},
+  
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+"""
+
+_DATASETNAME = "multi_xscience"
+_DISPLAYNAME = "Multi-XScience"
+
+_DESCRIPTION = """\
+Multi-document summarization is a challenging task for which there exists little large-scale datasets. 
+We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. 
+Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section 
+of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, 
+a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and 
+empirical results---using several state-of-the-art models trained on the Multi-XScience dataset---reveal t
+hat Multi-XScience is well suited for abstractive models.
+"""
+
+_HOMEPAGE = "https://github.com/yaolu/Multi-XScience"
+
+_LICENSE = 'MIT License'
+
+_URLS = {
+    _DATASETNAME: [
+        "https://github.com/yaolu/Multi-XScience/blob/master/data/train.json.gz?raw=true",
+        "https://github.com/yaolu/Multi-XScience/blob/master/data/test.json.gz?raw=true",
+        "https://github.com/yaolu/Multi-XScience/blob/master/data/val.json.gz?raw=true",
+    ],
+}
+
+_SUPPORTED_TASKS = [Tasks.PARAPHRASING, Tasks.SUMMARIZATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MultiXScience(datasets.GeneratorBasedBuilder):
+    """
+    Dataset for the EMNLP 2020 paper, Multi-XScience:
+    A Large-scale Dataset for Extreme Multi-document Summarization
+    of Scientific Articles.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="multi_xscience_source",
+            version=SOURCE_VERSION,
+            description="multi_xscience source schema",
+            schema="source",
+            subset_id="multi_xscience",
+        ),
+        BigBioConfig(
+            name="multi_xscience_bigbio_t2t",
+            version=BIGBIO_VERSION,
+            description="multi_xscienceBigBio schema",
+            schema="bigbio_t2t",
+            subset_id="multi_xscience",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "multi_xscience_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "aid": datasets.Value("string"),
+                    "mid": datasets.Value("string"),
+                    "abstract": datasets.Value("string"),
+                    "ref_abstract": datasets.Sequence(
+                        {
+                            "mid": datasets.Value("string"),
+                            "abstract": datasets.Value("string"),
+                        }
+                    ),
+                }
+            )
+        elif self.config.schema == "bigbio_t2t":
+            features = text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir[0]).replace("\\", "/"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir[1]).replace("\\", "/"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir[2]).replace("\\", "/"),
+                    "split": "val",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+
+    def _generate_examples(self, filepath, split):
+        j_file = open(filepath, "r")
+        j_file.seek(0)
+        j_json = json.load(j_file)
+
+        if self.config.schema == "source":
+            for key, example in enumerate(j_json):
+                yield key, {
+                    "aid": example["aid"],
+                    "mid": example["mid"],
+                    "abstract": example["abstract"],
+                    "ref_abstract": [
+                        {
+                            "mid": example["ref_abstract"][key]["mid"],
+                            "abstract": example["ref_abstract"][key]["abstract"],
+                        }
+                        for key in example["ref_abstract"].keys()
+                    ],
+                }
+
+        elif self.config.schema == "bigbio_t2t":
+            uid = 0
+
+            for key, example in enumerate(j_json):
+                uid += 1
+                yield key, {
+                    "id": str(uid),
+                    "document_id": str(key),
+                    "text_1": example["abstract"],
+                    "text_2": " ".join(
+                        [e["abstract"] for e in example["ref_abstract"].values()]
+                    ),
+                    "text_1_name": "Abstract of query paper",
+                    "text_2_name": "Cite abstracts",
+                }
+
+        j_file.close()