Update parquet files

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text
-# Image files - uncompressed
-*.bmp filter=lfs diff=lfs merge=lfs -text
-*.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-*.tiff filter=lfs diff=lfs merge=lfs -text
-# Image files - compressed
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.jpeg filter=lfs diff=lfs merge=lfs -text
-*.webp filter=lfs diff=lfs merge=lfs -text

bigbiohub.py

@@ -1,556 +0,0 @@
-from collections import defaultdict
-from dataclasses import dataclass
-from enum import Enum
-import logging
-from pathlib import Path
-from types import SimpleNamespace
-from typing import TYPE_CHECKING, Dict, Iterable, List, Tuple
-
-import datasets
-
-if TYPE_CHECKING:
-    import bioc
-
-logger = logging.getLogger(__name__)
-
-
-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"),
-                    }
-                ],
-            }
-        ],
-    }
-)
-
-
-def get_texts_and_offsets_from_bioc_ann(ann: "bioc.BioCAnnotation") -> Tuple:
-
-    offsets = [(loc.offset, loc.offset + loc.length) for loc in ann.locations]
-
-    text = ann.text
-
-    if len(offsets) > 1:
-        i = 0
-        texts = []
-        for start, end in offsets:
-            chunk_len = end - start
-            texts.append(text[i : chunk_len + i])
-            i += chunk_len
-            while i < len(text) and text[i] == " ":
-                i += 1
-    else:
-        texts = [text]
-
-    return offsets, texts
-
-
-def remove_prefix(a: str, prefix: str) -> str:
-    if a.startswith(prefix):
-        a = a[len(prefix) :]
-    return a
-
-
-def parse_brat_file(
-    txt_file: Path,
-    annotation_file_suffixes: List[str] = None,
-    parse_notes: bool = False,
-) -> Dict:
-    """
-    Parse a brat file into the schema defined below.
-    `txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
-    Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
-    e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
-    Will include annotator notes, when `parse_notes == True`.
-    brat_features = datasets.Features(
-        {
-            "id": datasets.Value("string"),
-            "document_id": datasets.Value("string"),
-            "text": datasets.Value("string"),
-            "text_bound_annotations": [  # T line in brat, e.g. type or event trigger
-                {
-                    "offsets": datasets.Sequence([datasets.Value("int32")]),
-                    "text": datasets.Sequence(datasets.Value("string")),
-                    "type": datasets.Value("string"),
-                    "id": datasets.Value("string"),
-                }
-            ],
-            "events": [  # E line in brat
-                {
-                    "trigger": datasets.Value(
-                        "string"
-                    ),  # refers to the text_bound_annotation of the trigger,
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "arguments": datasets.Sequence(
-                        {
-                            "role": datasets.Value("string"),
-                            "ref_id": datasets.Value("string"),
-                        }
-                    ),
-                }
-            ],
-            "relations": [  # R line in brat
-                {
-                    "id": datasets.Value("string"),
-                    "head": {
-                        "ref_id": datasets.Value("string"),
-                        "role": datasets.Value("string"),
-                    },
-                    "tail": {
-                        "ref_id": datasets.Value("string"),
-                        "role": datasets.Value("string"),
-                    },
-                    "type": datasets.Value("string"),
-                }
-            ],
-            "equivalences": [  # Equiv line in brat
-                {
-                    "id": datasets.Value("string"),
-                    "ref_ids": datasets.Sequence(datasets.Value("string")),
-                }
-            ],
-            "attributes": [  # M or A lines in brat
-                {
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "ref_id": datasets.Value("string"),
-                    "value": datasets.Value("string"),
-                }
-            ],
-            "normalizations": [  # N lines in brat
-                {
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "ref_id": datasets.Value("string"),
-                    "resource_name": datasets.Value(
-                        "string"
-                    ),  # Name of the resource, e.g. "Wikipedia"
-                    "cuid": datasets.Value(
-                        "string"
-                    ),  # ID in the resource, e.g. 534366
-                    "text": datasets.Value(
-                        "string"
-                    ),  # Human readable description/name of the entity, e.g. "Barack Obama"
-                }
-            ],
-            ### OPTIONAL: Only included when `parse_notes == True`
-            "notes": [  # # lines in brat
-                {
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "ref_id": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                }
-            ],
-        },
-        )
-    """
-
-    example = {}
-    example["document_id"] = txt_file.with_suffix("").name
-    with txt_file.open() as f:
-        example["text"] = f.read()
-
-    # If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
-    # for event extraction
-    if annotation_file_suffixes is None:
-        annotation_file_suffixes = [".a1", ".a2", ".ann"]
-
-    if len(annotation_file_suffixes) == 0:
-        raise AssertionError(
-            "At least one suffix for the to-be-read annotation files should be given!"
-        )
-
-    ann_lines = []
-    for suffix in annotation_file_suffixes:
-        annotation_file = txt_file.with_suffix(suffix)
-        if annotation_file.exists():
-            with annotation_file.open() as f:
-                ann_lines.extend(f.readlines())
-
-    example["text_bound_annotations"] = []
-    example["events"] = []
-    example["relations"] = []
-    example["equivalences"] = []
-    example["attributes"] = []
-    example["normalizations"] = []
-
-    if parse_notes:
-        example["notes"] = []
-
-    for line in ann_lines:
-        line = line.strip()
-        if not line:
-            continue
-
-        if line.startswith("T"):  # Text bound
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["type"] = fields[1].split()[0]
-            ann["offsets"] = []
-            span_str = remove_prefix(fields[1], (ann["type"] + " "))
-            text = fields[2]
-            for span in span_str.split(";"):
-                start, end = span.split()
-                ann["offsets"].append([int(start), int(end)])
-
-            # Heuristically split text of discontiguous entities into chunks
-            ann["text"] = []
-            if len(ann["offsets"]) > 1:
-                i = 0
-                for start, end in ann["offsets"]:
-                    chunk_len = end - start
-                    ann["text"].append(text[i : chunk_len + i])
-                    i += chunk_len
-                    while i < len(text) and text[i] == " ":
-                        i += 1
-            else:
-                ann["text"] = [text]
-
-            example["text_bound_annotations"].append(ann)
-
-        elif line.startswith("E"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-
-            ann["type"], ann["trigger"] = fields[1].split()[0].split(":")
-
-            ann["arguments"] = []
-            for role_ref_id in fields[1].split()[1:]:
-                argument = {
-                    "role": (role_ref_id.split(":"))[0],
-                    "ref_id": (role_ref_id.split(":"))[1],
-                }
-                ann["arguments"].append(argument)
-
-            example["events"].append(ann)
-
-        elif line.startswith("R"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["type"] = fields[1].split()[0]
-
-            ann["head"] = {
-                "role": fields[1].split()[1].split(":")[0],
-                "ref_id": fields[1].split()[1].split(":")[1],
-            }
-            ann["tail"] = {
-                "role": fields[1].split()[2].split(":")[0],
-                "ref_id": fields[1].split()[2].split(":")[1],
-            }
-
-            example["relations"].append(ann)
-
-        # '*' seems to be the legacy way to mark equivalences,
-        # but I couldn't find any info on the current way
-        # this might have to be adapted dependent on the brat version
-        # of the annotation
-        elif line.startswith("*"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["ref_ids"] = fields[1].split()[1:]
-
-            example["equivalences"].append(ann)
-
-        elif line.startswith("A") or line.startswith("M"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-
-            info = fields[1].split()
-            ann["type"] = info[0]
-            ann["ref_id"] = info[1]
-
-            if len(info) > 2:
-                ann["value"] = info[2]
-            else:
-                ann["value"] = ""
-
-            example["attributes"].append(ann)
-
-        elif line.startswith("N"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["text"] = fields[2]
-
-            info = fields[1].split()
-
-            ann["type"] = info[0]
-            ann["ref_id"] = info[1]
-            ann["resource_name"] = info[2].split(":")[0]
-            ann["cuid"] = info[2].split(":")[1]
-            example["normalizations"].append(ann)
-
-        elif parse_notes and line.startswith("#"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["text"] = fields[2] if len(fields) == 3 else BigBioValues.NULL
-
-            info = fields[1].split()
-
-            ann["type"] = info[0]
-            ann["ref_id"] = info[1]
-            example["notes"].append(ann)
-
-    return example
-
-
-def brat_parse_to_bigbio_kb(brat_parse: Dict) -> Dict:
-    """
-    Transform a brat parse (conforming to the standard brat schema) obtained with
-    `parse_brat_file` into a dictionary conforming to the `bigbio-kb` schema (as defined in ../schemas/kb.py)
-    :param brat_parse:
-    """
-
-    unified_example = {}
-
-    # Prefix all ids with document id to ensure global uniqueness,
-    # because brat ids are only unique within their document
-    id_prefix = brat_parse["document_id"] + "_"
-
-    # identical
-    unified_example["document_id"] = brat_parse["document_id"]
-    unified_example["passages"] = [
-        {
-            "id": id_prefix + "_text",
-            "type": "abstract",
-            "text": [brat_parse["text"]],
-            "offsets": [[0, len(brat_parse["text"])]],
-        }
-    ]
-
-    # get normalizations
-    ref_id_to_normalizations = defaultdict(list)
-    for normalization in brat_parse["normalizations"]:
-        ref_id_to_normalizations[normalization["ref_id"]].append(
-            {
-                "db_name": normalization["resource_name"],
-                "db_id": normalization["cuid"],
-            }
-        )
-
-    # separate entities and event triggers
-    unified_example["events"] = []
-    non_event_ann = brat_parse["text_bound_annotations"].copy()
-    for event in brat_parse["events"]:
-        event = event.copy()
-        event["id"] = id_prefix + event["id"]
-        trigger = next(
-            tr
-            for tr in brat_parse["text_bound_annotations"]
-            if tr["id"] == event["trigger"]
-        )
-        if trigger in non_event_ann:
-            non_event_ann.remove(trigger)
-        event["trigger"] = {
-            "text": trigger["text"].copy(),
-            "offsets": trigger["offsets"].copy(),
-        }
-        for argument in event["arguments"]:
-            argument["ref_id"] = id_prefix + argument["ref_id"]
-
-        unified_example["events"].append(event)
-
-    unified_example["entities"] = []
-    anno_ids = [ref_id["id"] for ref_id in non_event_ann]
-    for ann in non_event_ann:
-        entity_ann = ann.copy()
-        entity_ann["id"] = id_prefix + entity_ann["id"]
-        entity_ann["normalized"] = ref_id_to_normalizations[ann["id"]]
-        unified_example["entities"].append(entity_ann)
-
-    # massage relations
-    unified_example["relations"] = []
-    skipped_relations = set()
-    for ann in brat_parse["relations"]:
-        if (
-            ann["head"]["ref_id"] not in anno_ids
-            or ann["tail"]["ref_id"] not in anno_ids
-        ):
-            skipped_relations.add(ann["id"])
-            continue
-        unified_example["relations"].append(
-            {
-                "arg1_id": id_prefix + ann["head"]["ref_id"],
-                "arg2_id": id_prefix + ann["tail"]["ref_id"],
-                "id": id_prefix + ann["id"],
-                "type": ann["type"],
-                "normalized": [],
-            }
-        )
-    if len(skipped_relations) > 0:
-        example_id = brat_parse["document_id"]
-        logger.info(
-            f"Example:{example_id}: The `bigbio_kb` schema allows `relations` only between entities."
-            f" Skip (for now): "
-            f"{list(skipped_relations)}"
-        )
-
-    # get coreferences
-    unified_example["coreferences"] = []
-    for i, ann in enumerate(brat_parse["equivalences"], start=1):
-        is_entity_cluster = True
-        for ref_id in ann["ref_ids"]:
-            if not ref_id.startswith("T"):  # not textbound -> no entity
-                is_entity_cluster = False
-            elif ref_id not in anno_ids:  # event trigger -> no entity
-                is_entity_cluster = False
-        if is_entity_cluster:
-            entity_ids = [id_prefix + i for i in ann["ref_ids"]]
-            unified_example["coreferences"].append(
-                {"id": id_prefix + str(i), "entity_ids": entity_ids}
-            )
-    return unified_example

spl_adr_200db.py

@@ -1,402 +0,0 @@
-# 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.
-
-"""
-Dataset containing standardised information about known adverse reactions for 200
-FDA-approved drugs using information from the respective Structured Product Labels (SPLs).
-This data resulted from a partnership between the United States Food and Drug Administration
-(FDA) and the National Library of Medicine.
-
-Structured Product Labels (SPLs) are the documents FDA uses to exchange information
-about drugs and other products. For this dataset, SPLs were manually annotated for
-adverse reactions at the mention level to facilitate development and evaluation of
-text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised
-to the Unified Medical Language System (UMLS) and to the Medical Dictionary for
-Regulatory Activities (MedDRA).
-
-These data were used for the adverse event challenge at TAC 2017 (Text Analysis Conference)
-in four different tasks:
-* Task 1: Extract AdverseReactions and related mentions (Severity, Factor, DrugClass,
-Negation, Animal). This is similar to many NLP Named Entity Recognition (NER) evaluations.
-* Task 2: Identify the relations between AdverseReactions and related mentions (i.e.,
-Negated, Hypothetical, and Effect). This is similar to many NLP relation
-identification evaluations.
-* Task 3: Identify the positive AdverseReaction mention names in the labels.
-For the purposes of this task, positive will be defined as the caseless strings
-of all the AdverseReactions that have not been negated and are not related by
-a Hypothetical relation to a DrugClass or Animal. Note that this means Factors
-related via a Hypothetical relation are considered positive (e.g., "[unknown risk]
-Factor of [stroke]AdverseReaction") for the purposes of this task. The result of
-this task will be a list of unique strings corresponding to the positive ADRs
-as they were written in the label.
-* Task 4: Provide MedDRA PT(s) and LLT(s) for each positive AdverseReaction (occasionally,
-two or more PTs are necessary to fully describe the reaction). For participants
-approaching the tasks sequentially, this can be viewed as normalization of the terms
-extracted in Task 3 to MedDRA LLTs/PTs. Because MedDRA is not publicly available,
-and contains several versions, a standard version of MedDRA v18.1 will be provided
-to the participants. Other resources such as the UMLS Terminology Services may be
-used to aid with the normalization process.
-
-For more information regarding the challenge at TAC 2017, please visit:
-https://bionlp.nlm.nih.gov/tac2017adversereactions/
-
-"""
-
-import xml.etree.ElementTree as ET
-from collections import defaultdict
-from itertools import accumulate
-from typing import BinaryIO, Dict, Iterable, List, Tuple
-
-import datasets
-
-from .bigbiohub import kb_features
-from .bigbiohub import BigBioConfig
-from .bigbiohub import Tasks
-
-_LANGUAGES = ['English']
-_PUBMED = False
-_LOCAL = False
-_CITATION = """\
-@article{demner2018dataset,
-  author    = {Demner-Fushman, Dina and Shooshan, Sonya and Rodriguez, Laritza and Aronson,
-               Alan and Lang, Francois and Rogers, Willie and Roberts, Kirk and Tonning, Joseph},
-  title     = {A dataset of 200 structured product labels annotated for adverse drug reactions},
-  journal   = {Scientific Data},
-  volume    = {5},
-  year      = {2018},
-  month     = {01},
-  pages     = {180001},
-  url       = {
-    https://www.researchgate.net/publication/322810855_A_dataset_of_200_structured_product_labels_annotated_for_adverse_drug_reactions
-  },
-  doi       = {10.1038/sdata.2018.1}
-}
-"""
-
-_DATASETNAME = "spl_adr_200db"
-_DISPLAYNAME = "SPL ADR"
-
-_DESCRIPTION = """\
-The United States Food and Drug Administration (FDA) partnered with the National Library
-of Medicine to create a pilot dataset containing standardised information about known
-adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs),
-the documents FDA uses to exchange information about drugs and other products, were
-manually annotated for adverse reactions at the mention level to facilitate development
-and evaluation of text mining tools for extraction of ADRs from all SPLs.  The ADRs were
-then normalised to the Unified Medical Language System (UMLS) and to the Medical
-Dictionary for Regulatory Activities (MedDRA).
-"""
-
-_HOMEPAGE = "https://bionlp.nlm.nih.gov/tac2017adversereactions/"
-
-# NOTE: Source: https://osf.io/6h9q4/
-_LICENSE = 'Creative Commons Zero v1.0 Universal'
-_URLS = {
-    _DATASETNAME: {
-        "train": "https://bionlp.nlm.nih.gov/tac2017adversereactions/train_xml.tar.gz",
-        "unannotated": "https://bionlp.nlm.nih.gov/tac2017adversereactions/unannotated_xml.tar.gz",
-    }
-}
-
-_SUPPORTED_TASKS = [
-    Tasks.NAMED_ENTITY_RECOGNITION,
-    Tasks.NAMED_ENTITY_DISAMBIGUATION,
-    Tasks.RELATION_EXTRACTION,
-]
-
-_SOURCE_VERSION = "1.0.0"
-_BIGBIO_VERSION = "1.0.0"
-
-
-class SplAdr200DBDataset(datasets.GeneratorBasedBuilder):
-    """
-    The United States Food and Drug Administration (FDA) partnered with the National Library
-    of Medicine to create a pilot dataset containing standardised information about known
-    adverse reactions for 200 FDA-approved drugs.
-
-    These data were used in the adverse event challenge at TAC 2017 (Text Analysis Conference).
-    For more information on the tasks, see: https://bionlp.nlm.nih.gov/tac2017adversereactions/
-    """
-
-    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
-
-    BUILDER_CONFIGS = []
-
-    for subset_name in _URLS[_DATASETNAME]:
-        BUILDER_CONFIGS.extend(
-            [
-                BigBioConfig(
-                    name=f"spl_adr_200db_{subset_name}_source",
-                    version=SOURCE_VERSION,
-                    description=f"SPL ADR 200db source {subset_name} schema",
-                    schema="source",
-                    subset_id=f"spl_adr_200db_{subset_name}",
-                ),
-                BigBioConfig(
-                    name=f"spl_adr_200db_{subset_name}_bigbio_kb",
-                    version=BIGBIO_VERSION,
-                    description=f"SPL ADR 200db BigBio {subset_name} schema",
-                    schema="bigbio_kb",
-                    subset_id=f"spl_adr_200db_{subset_name}",
-                ),
-            ]
-        )
-
-    DEFAULT_CONFIG_NAME = "spl_adr_200db_source"
-
-    def _info(self) -> datasets.DatasetInfo:
-        if self.config.schema == "source":
-            unannotated_features = {
-                "drug_name": datasets.Value("string"),
-                "text": [datasets.Value("string")],
-                "sections": [
-                    {
-                        "id": datasets.Value("string"),
-                        "name": datasets.Value("string"),
-                        "text": datasets.Value("string"),
-                    }
-                ],
-            }
-            features = datasets.Features(
-                {
-                    **unannotated_features,
-                    "mentions": [
-                        {
-                            "id": datasets.Value("string"),
-                            "section": datasets.Value("string"),
-                            "type": datasets.Value("string"),
-                            "start": datasets.Value("string"),
-                            "len": datasets.Value("string"),
-                            "str": datasets.Value("string"),
-                        }
-                    ],
-                    "relations": [
-                        {
-                            "id": datasets.Value("string"),
-                            "type": datasets.Value("string"),
-                            "arg1": datasets.Value("string"),
-                            "arg2": datasets.Value("string"),
-                        }
-                    ],
-                    "reactions": [
-                        {
-                            "id": datasets.Value("string"),
-                            "str": datasets.Value("string"),
-                            "normalizations": [
-                                {
-                                    "id": datasets.Value("string"),
-                                    "meddra_pt": datasets.Value("string"),
-                                    "meddra_pt_id": datasets.Value("string"),
-                                    "meddra_llt": datasets.Value("string"),
-                                    "meddra_llt_id": datasets.Value("string"),
-                                    "flag": datasets.Value("string"),
-                                }
-                            ],
-                        }
-                    ],
-                }
-            )
-
-        elif self.config.schema == "bigbio_kb":
-            features = kb_features
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=str(_LICENSE),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
-        """Returns SplitGenerators."""
-        *_, subset_name = self.config.subset_id.split("_")
-
-        urls = _URLS[_DATASETNAME][subset_name]
-
-        data_dir = dl_manager.download(urls)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": dl_manager.iter_archive(data_dir),
-                },
-            ),
-        ]
-
-    def _source_features_from_xml(self, element_tree):
-        root = element_tree.getroot()
-        drug_name = root.attrib["drug"]
-
-        sections = root.findall(".//Text/Section")
-        relations = root.findall(".//Relations/Relation")
-        reactions = [
-            {
-                "id": reaction.attrib["id"],
-                "str": reaction.attrib["str"],
-                "normalizations": [
-                    {
-                        # NOTE: Default features to `None` as not all of them
-                        # will be present in all reactions.
-                        "meddra_pt": None,
-                        "meddra_pt_id": None,
-                        "meddra_llt": None,
-                        "meddra_llt_id": None,
-                        "flag": None,
-                        **normalization.attrib,
-                    }
-                    for normalization in reaction.findall("Normalization")
-                ],
-            }
-            for reaction in root.findall(".//Reactions/Reaction")
-        ]
-
-        mentions = root.findall(".//Mentions/Mention")
-        return {
-            "drug_name": drug_name,
-            "text": [section.text for section in sections],
-            "mentions": [mention.attrib for mention in mentions],
-            "relations": [relation.attrib for relation in relations],
-            "reactions": reactions,
-            "sections": [
-                {**section.attrib, "text": section.text} for section in sections
-            ],
-        }
-
-    def _bigbio_kb_features_from_xml(self, element_tree):
-        source_features = self._source_features_from_xml(
-            element_tree=element_tree,
-        )
-        entity_normalizations = defaultdict(list)
-
-        for reaction in source_features["reactions"]:
-            entity_name = reaction["str"]
-            for normalization in reaction["normalizations"]:
-
-                # commenting this out for now
-                # if there is no db_name then its not a useful normalization
-                # if normalization["meddra_pt_id"]:
-                #    entity_normalizations[entity_name].append(
-                #        {"db_name": None, "db_id": f"pt_{normalization['meddra_pt_id']}"}
-                #    )
-
-                if normalization["meddra_llt_id"]:
-                    entity_normalizations[entity_name].append(
-                        {
-                            "db_name": "MedDRA v18.1",
-                            "db_id": f"llt_{normalization['meddra_llt_id']}",
-                        }
-                    )
-
-        section_lengths = list(
-            accumulate(len(section["text"]) for section in source_features["sections"])
-        )
-
-        section_offsets = [
-            (start + index, end + index)
-            for index, (start, end) in enumerate(
-                zip([0] + section_lengths[:-1], section_lengths)
-            )
-        ]
-
-        section_start_offset_map = {
-            f"S{section_index}": offsets[0]
-            for section_index, offsets in enumerate(section_offsets, 1)
-        }
-
-        entities = []
-
-        for mention in source_features["mentions"]:
-            start_points = [
-                int(start_point) + section_start_offset_map[mention["section"]]
-                for start_point in mention["start"].split(",")
-            ]
-
-            lens = [int(len_) for len_ in mention["len"].split(",")]
-
-            offsets = [
-                (start_point, start_point + len_)
-                for start_point, len_ in zip(start_points, lens)
-            ]
-
-            text = " ".join(section["text"] for section in source_features["sections"])
-
-            entity_strings = [
-                text[start_point : start_point + len_]
-                for start_point, len_ in zip(start_points, lens)
-            ]
-
-            entities.append(
-                {
-                    "id": f"{source_features['drug_name']}_entity_{mention['id']}",
-                    "type": mention["type"],
-                    "text": entity_strings,
-                    "offsets": offsets,
-                    "normalized": entity_normalizations[mention["str"]],
-                }
-            )
-
-        return {
-            "document_id": source_features["drug_name"],
-            "passages": [
-                {
-                    "id": f"{source_features['drug_name']}_section_{section['id']}",
-                    "type": section["name"],
-                    "text": [section["text"]],
-                    "offsets": [offsets],
-                }
-                for section, offsets in zip(
-                    source_features["sections"], section_offsets
-                )
-            ],
-            "entities": entities,
-            "relations": [
-                {
-                    "id": f"{source_features['drug_name']}_relation_{relation['id']}",
-                    "type": relation["type"],
-                    "arg1_id": relation["arg1"],
-                    "arg2_id": relation["arg2"],
-                    "normalized": [],
-                }
-                for relation in source_features["relations"]
-            ],
-            "events": [],
-            "coreferences": [],
-        }
-
-    def _generate_examples(self, filepaths: Iterable[Tuple[str, BinaryIO]]) -> Tuple[int, Dict]:
-        """Yields examples as (key, example) tuples."""
-
-        for file_index, (drug_filename, drug_file) in enumerate(filepaths):
-            element_tree = ET.parse(drug_file)
-
-            if self.config.schema == "source":
-                features = self._source_features_from_xml(
-                    element_tree=element_tree,
-                )
-            elif self.config.schema == "bigbio_kb":
-                features = self._bigbio_kb_features_from_xml(
-                    element_tree=element_tree,
-                )
-                features["id"] = file_index
-            else:
-                raise ValueError(
-                    f"Unsupported schema '{self.config.schema}' requested for "
-                    f"dataset with name '{_DATASETNAME}'."
-                )
-
-            yield file_index, features

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