initial commit

README.md

@@ -1,3 +1,38 @@
 ---
+language: en
 license: cc-by-4.0
+multilinguality: momolingual
+pretty_name: GAD
 ---
+
+
+# Dataset Card for GAD
+
+## Dataset Description
+
+- **Homepage:** "https://github.com/dmis-lab/biobert"  # This data source is used by the BLURB benchmark
+- **Pubmed:** True
+- **Public:** True
+- **Tasks:** Text Classification
+
+
+A corpus identifying associations between genes and diseases by a semi-automatic
+annotation procedure based on the Genetic Association Database
+
+
+## Citation Information
+
+```
+@article{Bravo2015,
+  doi = {10.1186/s12859-015-0472-9},
+  url = {https://doi.org/10.1186/s12859-015-0472-9},
+  year = {2015},
+  month = feb,
+  publisher = {Springer Science and Business Media {LLC}},
+  volume = {16},
+  number = {1},
+  author = {{\`{A}}lex Bravo and Janet Pi{\~{n}}ero and N{\'{u}}ria Queralt-Rosinach and Michael Rautschka and Laura I Furlong},
+  title = {Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research},
+  journal = {{BMC} Bioinformatics}
+}
+```

bigbiohub.py

@@ -0,0 +1,153 @@
+from dataclasses import dataclass
+from enum import Enum
+import datasets
+from types import SimpleNamespace
+
+
+BigBioValues = SimpleNamespace(NULL="<BB_NULL_STR>")
+
+
+@dataclass
+class BigBioConfig(datasets.BuilderConfig):
+    """BuilderConfig for BigBio."""
+
+    name: str = None
+    version: datasets.Version = None
+    description: str = None
+    schema: str = None
+    subset_id: str = None
+
+
+class Tasks(Enum):
+    NAMED_ENTITY_RECOGNITION = "NER"
+    NAMED_ENTITY_DISAMBIGUATION = "NED"
+    EVENT_EXTRACTION = "EE"
+    RELATION_EXTRACTION = "RE"
+    COREFERENCE_RESOLUTION = "COREF"
+    QUESTION_ANSWERING = "QA"
+    TEXTUAL_ENTAILMENT = "TE"
+    SEMANTIC_SIMILARITY = "STS"
+    TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS"
+    PARAPHRASING = "PARA"
+    TRANSLATION = "TRANSL"
+    SUMMARIZATION = "SUM"
+    TEXT_CLASSIFICATION = "TXTCLASS"
+
+
+entailment_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "premise": datasets.Value("string"),
+        "hypothesis": datasets.Value("string"),
+        "label": datasets.Value("string"),
+    }
+)
+
+pairs_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "text_1": datasets.Value("string"),
+        "text_2": datasets.Value("string"),
+        "label": datasets.Value("string"),
+    }
+)
+
+qa_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "question_id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "question": datasets.Value("string"),
+        "type": datasets.Value("string"),
+        "choices": [datasets.Value("string")],
+        "context": datasets.Value("string"),
+        "answer": datasets.Sequence(datasets.Value("string")),
+    }
+)
+
+text_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "text": datasets.Value("string"),
+        "labels": [datasets.Value("string")],
+    }
+)
+
+text2text_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "text_1": datasets.Value("string"),
+        "text_2": datasets.Value("string"),
+        "text_1_name": datasets.Value("string"),
+        "text_2_name": datasets.Value("string"),
+    }
+)
+
+kb_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "passages": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "text": datasets.Sequence(datasets.Value("string")),
+                "offsets": datasets.Sequence([datasets.Value("int32")]),
+            }
+        ],
+        "entities": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "text": datasets.Sequence(datasets.Value("string")),
+                "offsets": datasets.Sequence([datasets.Value("int32")]),
+                "normalized": [
+                    {
+                        "db_name": datasets.Value("string"),
+                        "db_id": datasets.Value("string"),
+                    }
+                ],
+            }
+        ],
+        "events": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                # refers to the text_bound_annotation of the trigger
+                "trigger": {
+                    "text": datasets.Sequence(datasets.Value("string")),
+                    "offsets": datasets.Sequence([datasets.Value("int32")]),
+                },
+                "arguments": [
+                    {
+                        "role": datasets.Value("string"),
+                        "ref_id": datasets.Value("string"),
+                    }
+                ],
+            }
+        ],
+        "coreferences": [
+            {
+                "id": datasets.Value("string"),
+                "entity_ids": datasets.Sequence(datasets.Value("string")),
+            }
+        ],
+        "relations": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "arg1_id": datasets.Value("string"),
+                "arg2_id": datasets.Value("string"),
+                "normalized": [
+                    {
+                        "db_name": datasets.Value("string"),
+                        "db_id": datasets.Value("string"),
+                    }
+                ],
+            }
+        ],
+    }
+)

gad.py

@@ -0,0 +1,211 @@
+from pathlib import Path
+from typing import List
+
+import datasets
+import pandas as pd
+
+from .bigbiohub import text_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+
+_SOURCE_VIEW_NAME = "source"
+_UNIFIED_VIEW_NAME = "bigbio"
+
+_LANGUAGES = ["English"]
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{Bravo2015,
+  doi = {10.1186/s12859-015-0472-9},
+  url = {https://doi.org/10.1186/s12859-015-0472-9},
+  year = {2015},
+  month = feb,
+  publisher = {Springer Science and Business Media {LLC}},
+  volume = {16},
+  number = {1},
+  author = {{\`{A}}lex Bravo and Janet Pi{\~{n}}ero and N{\'{u}}ria Queralt-Rosinach and Michael Rautschka and Laura I Furlong},
+  title = {Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research},
+  journal = {{BMC} Bioinformatics}
+}
+"""
+
+_DESCRIPTION = """\
+A corpus identifying associations between genes and diseases by a semi-automatic
+annotation procedure based on the Genetic Association Database
+"""
+
+_DATASETNAME = "gad"
+_DISPLAYNAME = "GAD"
+
+_HOMEPAGE = "https://github.com/dmis-lab/biobert"  # This data source is used by the BLURB benchmark
+
+_LICENSE = "CC_BY_4p0"
+
+_URLs = {
+    "source": "https://drive.google.com/uc?export=download&id=1-jDKGcXREb2X9xTFnuiJ36PvsqoyHWcw",
+    "bigbio_text": "https://drive.google.com/uc?export=download&id=1-jDKGcXREb2X9xTFnuiJ36PvsqoyHWcw",
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class GAD(datasets.GeneratorBasedBuilder):
+    """GAD is a weakly labeled dataset for Entity Relations (REL) task which is treated as a sentence classification task."""
+
+    BUILDER_CONFIGS = [
+        # 10-fold source schema
+        BigBioConfig(
+            name=f"gad_fold{i}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="GAD source schema",
+            schema="source",
+            subset_id=f"gad_fold{i}",
+        )
+        for i in range(10)
+    ] + [
+        # 10-fold bigbio schema
+        BigBioConfig(
+            name=f"gad_fold{i}_bigbio_text",
+            version=datasets.Version(_BIGBIO_VERSION),
+            description="GAD BigBio schema",
+            schema="bigbio_text",
+            subset_id=f"gad_fold{i}",
+        )
+        for i in range(10)
+    ]
+
+    # BLURB Benchmark config https://microsoft.github.io/BLURB/
+    BUILDER_CONFIGS.append(
+        BigBioConfig(
+            name=f"gad_blurb_bigbio_text",
+            version=datasets.Version(_BIGBIO_VERSION),
+            description=f"GAD BLURB benchmark in simplified BigBio schema",
+            schema="bigbio_text",
+            subset_id=f"gad_blurb",
+        )
+    )
+
+    DEFAULT_CONFIG_NAME = "gad_fold0_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "index": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    "label": datasets.Value("int32"),
+                }
+            )
+        elif self.config.schema == "bigbio_text":
+            features = text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+
+        if "blurb" in self.config.name:
+            return self._blurb_split_generator(dl_manager)
+
+        fold_id = int(self.config.subset_id.split("_fold")[1][0]) + 1
+
+        my_urls = _URLs[self.config.schema]
+        data_dir = Path(dl_manager.download_and_extract(my_urls))
+        data_files = {
+            "train": data_dir / "GAD" / str(fold_id) / "train.tsv",
+            "test": data_dir / "GAD" / str(fold_id) / "test.tsv",
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_files["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path):
+        if "train.tsv" in str(filepath):
+            df = pd.read_csv(filepath, sep="\t", header=None).reset_index()
+        else:
+            df = pd.read_csv(filepath, sep="\t")
+        df.columns = ["id", "sentence", "label"]
+
+        if self.config.schema == "source":
+            for id, row in enumerate(df.itertuples()):
+                ex = {
+                    "index": row.id,
+                    "sentence": row.sentence,
+                    "label": int(row.label),
+                }
+                yield id, ex
+        elif self.config.schema == "bigbio_text":
+            for id, row in enumerate(df.itertuples()):
+                ex = {
+                    "id": id,
+                    "document_id": row.id,
+                    "text": row.sentence,
+                    "labels": [str(row.label)],
+                }
+                yield id, ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+    def _blurb_split_generator(self, dl_manager: datasets.DownloadManager):
+        """Creates train/dev/test for BLURB split"""
+
+        my_urls = _URLs[self.config.schema]
+        data_dir = Path(dl_manager.download_and_extract(my_urls))
+        data_files = {
+            "train": data_dir / "GAD" / str(1) / "train.tsv",
+            "test": data_dir / "GAD" / str(1) / "test.tsv",
+        }
+
+        root_path = data_files["train"].parents[1]
+        # Save the train + validation sets accordingly
+        with open(data_files["train"], "r") as f:
+            train_data = f.readlines()
+
+        data = {}
+        data["train"], data["dev"] = train_data[:4261], train_data[4261:]
+
+        for batch in ["train", "dev"]:
+            fname = batch + "_blurb.tsv"
+            fname = root_path / fname
+
+            with open(fname, "w") as f:
+                f.write("index\tsentence\tlabel\n")
+                for idx, line in enumerate(data[batch]):
+                    f.write(f"{idx}\t{line}")
+
+        train_fpath = root_path / "train_blurb.tsv"
+        dev_fpath = root_path / "dev_blurb.tsv"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": train_fpath},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": dev_fpath},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_files["test"]},
+            ),
+        ]