Update parquet files

README.md

@@ -1,50 +0,0 @@
-
----
-language: 
-- en
-bigbio_language: 
-- English
-license: other
-multilinguality: monolingual
-bigbio_license_shortname: ISC
-pretty_name: CPI
-homepage: https://github.com/KerstenDoering/CPI-Pipeline
-bigbio_pubmed: True
-bigbio_public: True
-bigbio_tasks: 
-- NAMED_ENTITY_RECOGNITION
-- NAMED_ENTITY_DISAMBIGUATION
-- RELATION_EXTRACTION
----
-
-
-# Dataset Card for CPI
-
-## Dataset Description
-
-- **Homepage:** https://github.com/KerstenDoering/CPI-Pipeline
-- **Pubmed:** True
-- **Public:** True
-- **Tasks:** NER,NED,RE
-
-
-The compound-protein relationship (CPI) dataset consists of 2,613 sentences 
-from abstracts containing annotations of proteins, small molecules, and their 
-relationships.
-
-
-
-## Citation Information
-
-```
-@article{doring2020automated,
-  title={Automated recognition of functional compound-protein relationships in literature},
-  author={D{\"o}ring, Kersten and Qaseem, Ammar and Becer, Michael and Li, Jianyu and Mishra, Pankaj and Gao, Mingjie and Kirchner, Pascal and Sauter, Florian and Telukunta, Kiran K and Moumbock, Aur{\'e}lien FA and others},
-  journal={Plos one},
-  volume={15},
-  number={3},
-  pages={e0220925},
-  year={2020},
-  publisher={Public Library of Science San Francisco, CA USA}
-}
-```

bigbiohub.py

@@ -1,592 +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"),
-                    }
-                ],
-            }
-        ],
-    }
-)
-
-
-TASK_TO_SCHEMA = {
-    Tasks.NAMED_ENTITY_RECOGNITION.name: "KB",
-    Tasks.NAMED_ENTITY_DISAMBIGUATION.name: "KB",
-    Tasks.EVENT_EXTRACTION.name: "KB",
-    Tasks.RELATION_EXTRACTION.name: "KB",
-    Tasks.COREFERENCE_RESOLUTION.name: "KB",
-    Tasks.QUESTION_ANSWERING.name: "QA",
-    Tasks.TEXTUAL_ENTAILMENT.name: "TE",
-    Tasks.SEMANTIC_SIMILARITY.name: "PAIRS",
-    Tasks.TEXT_PAIRS_CLASSIFICATION.name: "PAIRS",
-    Tasks.PARAPHRASING.name: "T2T",
-    Tasks.TRANSLATION.name: "T2T",
-    Tasks.SUMMARIZATION.name: "T2T",
-    Tasks.TEXT_CLASSIFICATION.name: "TEXT",
-}
-
-SCHEMA_TO_TASKS = defaultdict(set)
-for task, schema in TASK_TO_SCHEMA.items():
-    SCHEMA_TO_TASKS[schema].add(task)
-SCHEMA_TO_TASKS = dict(SCHEMA_TO_TASKS)
-
-VALID_TASKS = set(TASK_TO_SCHEMA.keys())
-VALID_SCHEMAS = set(TASK_TO_SCHEMA.values())
-
-SCHEMA_TO_FEATURES = {
-    "KB": kb_features,
-    "QA": qa_features,
-    "TE": entailment_features,
-    "T2T": text2text_features,
-    "TEXT": text_features,
-    "PAIRS": pairs_features,
-}
-
-
-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)
-        try:
-            with annotation_file.open() as f:
-                ann_lines.extend(f.readlines())
-        except Exception:
-            continue
-
-    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

cpi.py

@@ -1,295 +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.
-"""
-The compound-protein relationship (CPI) dataset consists of 2,613 sentences from abstracts containing
-annotations of proteins, small molecules, and their relationships. For further information see:
-https://pubmed.ncbi.nlm.nih.gov/32126064/ and https://github.com/KerstenDoering/CPI-Pipeline
-"""
-import xml.etree.ElementTree as ET
-from pathlib import Path
-from typing import Dict, Iterator, Tuple
-
-import datasets
-
-from .bigbiohub import kb_features
-from .bigbiohub import BigBioConfig
-from .bigbiohub import Tasks
-
-_LANGUAGES = ['English']
-_PUBMED = True
-_LOCAL = False
-_CITATION = """\
-@article{doring2020automated,
-  title={Automated recognition of functional compound-protein relationships in literature},
-  author={D{\"o}ring, Kersten and Qaseem, Ammar and Becer, Michael and Li, Jianyu and Mishra, Pankaj and Gao, Mingjie and Kirchner, Pascal and Sauter, Florian and Telukunta, Kiran K and Moumbock, Aur{\'e}lien FA and others},
-  journal={Plos one},
-  volume={15},
-  number={3},
-  pages={e0220925},
-  year={2020},
-  publisher={Public Library of Science San Francisco, CA USA}
-}
-"""
-
-_DATASETNAME = "cpi"
-_DISPLAYNAME = "CPI"
-
-_DESCRIPTION = """\
-The compound-protein relationship (CPI) dataset consists of 2,613 sentences from abstracts containing \
-annotations of proteins, small molecules, and their relationships
-"""
-
-_HOMEPAGE = "https://github.com/KerstenDoering/CPI-Pipeline"
-
-_LICENSE = 'ISC License'
-
-_URLS = {
-    "CPI": "https://github.com/KerstenDoering/CPI-Pipeline/raw/master/data_sets/xml/CPI-DS.xml",
-    "CPI_IV": "https://github.com/KerstenDoering/CPI-Pipeline/raw/master/data_sets/xml/CPI-DS_IV.xml",
-    "CPI_NIV": "https://github.com/KerstenDoering/CPI-Pipeline/raw/master/data_sets/xml/CPI-DS_IV.xml",
-}
-
-_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION, Tasks.RELATION_EXTRACTION]
-
-_SOURCE_VERSION = "1.0.2"
-_BIGBIO_VERSION = "1.0.0"
-
-
-class CpiDataset(datasets.GeneratorBasedBuilder):
-    """The compound-protein relationship (CPI) dataset"""
-
-    ENTITY_TYPE_TO_DB_NAME = {"compound": "PubChem", "protein": "UniProt"}
-
-    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
-
-    BUILDER_CONFIGS = [
-        BigBioConfig(
-            name="cpi_source",
-            version=SOURCE_VERSION,
-            description="CPI source schema",
-            schema="source",
-            subset_id="cpi",
-        ),
-        BigBioConfig(
-            name="cpi_iv_source",
-            version=SOURCE_VERSION,
-            description="CPI source schema - subset with interaction verbs",
-            schema="source",
-            subset_id="cpi_iv",
-        ),
-        BigBioConfig(
-            name="cpi_niv_source",
-            version=SOURCE_VERSION,
-            description="CPI source schema - subset without interaction verbs",
-            schema="source",
-            subset_id="cpi_niv",
-        ),
-        BigBioConfig(
-            name="cpi_bigbio_kb",
-            version=BIGBIO_VERSION,
-            description="CPI BigBio schema",
-            schema="bigbio_kb",
-            subset_id="cpi",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "cpi_source"
-
-    def _info(self):
-        if self.config.schema == "source":
-            features = datasets.Features(
-                {
-                    "document_id": datasets.Value("string"),
-                    "document_orig_id": datasets.Value("string"),
-                    "sentences": [
-                        {
-                            "sentence_id": datasets.Value("string"),
-                            "sentence_orig_id": datasets.Value("string"),
-                            "text": datasets.Value("string"),
-                            "entities": [
-                                {
-                                    "entity_id": datasets.Value("string"),
-                                    "entity_orig_id": datasets.Sequence(datasets.Value("string")),
-                                    "type": datasets.Value("string"),
-                                    "offset": datasets.Sequence(datasets.Value("int32")),
-                                    "text": datasets.Value("string"),
-                                }
-                            ],
-                            "pairs": [
-                                {
-                                    "pair_id": datasets.Value("string"),
-                                    "e1": datasets.Value("string"),
-                                    "e2": datasets.Value("string"),
-                                    "interaction": datasets.Value("bool"),
-                                }
-                            ],
-                        }
-                    ],
-                }
-            )
-
-        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):
-        # Distinguish based on the subset id (cpi, cpi_iv, cpi_niv) which file to load
-        subset_url = _URLS[self.config.subset_id.upper()]
-        subset_file = dl_manager.download_and_extract(subset_url)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"subset_file": subset_file},
-            )
-        ]
-
-    def _generate_examples(self, subset_file: Path) -> Iterator[Tuple[str, Dict]]:
-        if self.config.schema == "source":
-            for doc_id, document in self._read_source_examples(subset_file):
-                yield doc_id, document
-
-        elif self.config.name == "cpi_bigbio_kb":
-            # Note: The sentences in a CPI document does not (necessarily) occur consecutive in
-            # the original publication. Nevertheless, in this implementation we capture all sentences
-            # of a document in one kb-schema document to explicitly model documents.
-
-            # Transform each source-schema document to kb-schema document
-            for doc_id, source_document in self._read_source_examples(subset_file):
-                sentence_offset = 0
-                passages = []
-                entities = []
-                relations = []
-
-                # Transform all sentences to kb-schema sentences
-                for source_sentence in source_document["sentences"]:
-                    text = source_sentence["text"]
-                    passages.append(
-                        {
-                            "id": source_sentence["sentence_id"],
-                            "text": [text],
-                            "offsets": [[sentence_offset + 0, sentence_offset + len(text)]],
-                            "type": "",
-                        }
-                    )
-
-                    # Transform source-schema entities to kb-schema entities
-                    for source_entity in source_sentence["entities"]:
-                        db_name = self.ENTITY_TYPE_TO_DB_NAME[source_entity["type"]]
-
-                        entity_offset = source_entity["offset"]
-                        entity_offset = [sentence_offset + entity_offset[0], sentence_offset + entity_offset[1]]
-
-                        entities.append(
-                            {
-                                "id": source_entity["entity_id"],
-                                "type": source_entity["type"],
-                                "text": [source_entity["text"]],
-                                "offsets": [entity_offset],
-                                "normalized": [
-                                    {"db_name": db_name, "db_id": db_id} for db_id in source_entity["entity_orig_id"]
-                                ],
-                            }
-                        )
-
-                    # Transform source-schema pairs to kb-schema relations
-                    for source_pair in source_sentence["pairs"]:
-                        # Ignore pairs that are annotated to be not in a relationship!
-                        if not source_pair["interaction"]:
-                            continue
-
-                        relations.append(
-                            {
-                                "id": source_pair["pair_id"],
-                                "type": "compound-protein-interaction",
-                                "arg1_id": source_pair["e1"],
-                                "arg2_id": source_pair["e2"],
-                                "normalized": [],
-                            }
-                        )
-
-                    sentence_offset += len(text) + 1
-
-                kb_document = {
-                    "id": source_document["document_id"],
-                    "document_id": source_document["document_orig_id"],
-                    "passages": passages,
-                    "entities": entities,
-                    "relations": relations,
-                    "events": [],
-                    "coreferences": [],
-                }
-
-                yield source_document["document_id"], kb_document
-
-    def _read_source_examples(self, input_file: Path) -> Iterator[Tuple[str, Dict]]:
-        """
-        Reads all instances of the given input file and parses them into the source format.
-        """
-        root = ET.parse(input_file)
-        for document in root.iter("document"):
-            sentences = []
-            for sentence in document.iter("sentence"):
-                entities = []
-                for entity in sentence.iter("entity"):
-                    char_offsets = entity.attrib["charOffset"].split("-")
-                    start, end = int(char_offsets[0]), int(char_offsets[1])
-
-                    entities.append(
-                        {
-                            "entity_id": entity.attrib["id"],
-                            "entity_orig_id": entity.attrib["origId"].split(","),
-                            "type": entity.attrib["type"],
-                            "text": entity.attrib["text"],
-                            "offset": [start, end],
-                        }
-                    )
-
-                pairs = []
-                for pair in sentence.iter("pair"):
-                    pairs.append(
-                        {
-                            "pair_id": pair.attrib["id"],
-                            "e1": pair.attrib["e1"],
-                            "e2": pair.attrib["e2"],
-                            "interaction": pair.attrib["interaction"].lower() == "true",
-                        }
-                    )
-
-                sentences.append(
-                    {
-                        "sentence_id": sentence.attrib["id"],
-                        "sentence_orig_id": sentence.attrib["origId"],
-                        "text": sentence.attrib["text"],
-                        "entities": entities,
-                        "pairs": pairs,
-                    }
-                )
-
-            document_dict = {
-                "document_id": document.attrib["id"],
-                "document_orig_id": document.attrib["origId"],
-                "sentences": sentences,
-            }
-
-            yield document.attrib["id"], document_dict

cpi_bigbio_kb/cpi-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05286f477e525d0782aa1e05c83c66b8886ffc7b0135f7fad3cd710dc8eaaa74
+size 831944

cpi_iv_source/cpi-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:760fea3954edfa147d4bca434d5bffff0b47ef1f84472a802020aa7928f9edfe
+size 405525

cpi_niv_source/cpi-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:760fea3954edfa147d4bca434d5bffff0b47ef1f84472a802020aa7928f9edfe
+size 405525

cpi_source/cpi-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a8afe948e01554be3cda9547250767f1aff53f28b232272dac8a6c9d1cdd39a
+size 853376