Upload 4 files

original code from https://huggingface.co/datasets/bigbio/chemprot, but with local data

ChemProt_Corpus.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:492e3d607f38e2727b799e9d60263b776ebd2a5e61cf0fb59bea2b3eb68e1c28
+size 4977337

README.md

@@ -0,0 +1,49 @@
+______________________________________________________________________
+
+language:
+
+- en
+  bigbio_language:
+- English
+  license: other
+  multilinguality: monolingual
+  bigbio_license_shortname: PUBLIC_DOMAIN_MARK_1p0
+  pretty_name: ChemProt
+  homepage: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/
+  bigbio_pubmed: True
+  bigbio_public: True
+  bigbio_tasks:
+- RELATION_EXTRACTION
+- NAMED_ENTITY_RECOGNITION
+
+______________________________________________________________________
+
+# Dataset Card for ChemProt
+
+## Dataset Description
+
+- **Homepage:** https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/
+- **Pubmed:** True
+- **Public:** True
+- **Tasks:** RE,NER
+
+The BioCreative VI Chemical-Protein interaction dataset identifies entities of
+chemicals and proteins and their likely relation to one other. Compounds are
+generally agonists (activators) or antagonists (inhibitors) of proteins.
+
+## Citation Information
+
+```
+@article{DBLP:journals/biodb/LiSJSWLDMWL16,
+  author    = {Krallinger, M., Rabal, O., Lourenço, A.},
+  title     = {Overview of the BioCreative VI chemical-protein interaction Track},
+  journal   = {Proceedings of the BioCreative VI Workshop,},
+  volume    = {141-146},
+  year      = {2017},
+  url       = {https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/},
+  doi       = {},
+  biburl    = {},
+  bibsource = {}
+}
+
+```

bigbiohub.py

@@ -0,0 +1,574 @@
+import logging
+from collections import defaultdict
+from dataclasses import dataclass
+from enum import Enum
+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

chemprot.py

@@ -0,0 +1,434 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The BioCreative VI Chemical-Protein interaction dataset identifies entities of chemicals and
+proteins and their likely relation to one other.
+
+Compounds are generally agonists (activators) or antagonists (inhibitors) of proteins. The script
+loads dataset in bigbio schema (using knowledgebase schema: schemas/kb) AND/OR source (default)
+schema
+"""
+import os
+from typing import Dict, Tuple
+
+import datasets
+
+from .bigbiohub import BigBioConfig, Tasks, kb_features
+
+_LANGUAGES = ["English"]
+_PUBMED = True
+_LOCAL = True
+_CITATION = """\
+@article{DBLP:journals/biodb/LiSJSWLDMWL16,
+  author    = {Krallinger, M., Rabal, O., Lourenço, A.},
+  title     = {Overview of the BioCreative VI chemical-protein interaction Track},
+  journal   = {Proceedings of the BioCreative VI Workshop,},
+  volume    = {141-146},
+  year      = {2017},
+  url       = {https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/},
+  doi       = {},
+  biburl    = {},
+  bibsource = {}
+}
+"""
+_DESCRIPTION = """\
+The BioCreative VI Chemical-Protein interaction dataset identifies entities of
+chemicals and proteins and their likely relation to one other. Compounds are
+generally agonists (activators) or antagonists (inhibitors) of proteins.
+"""
+
+_DATASETNAME = "chemprot"
+_DISPLAYNAME = "ChemProt"
+
+_HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/"
+
+_LICENSE = "Public Domain Mark 1.0"
+
+_URLs = {
+    "source": "./ChemProt_Corpus.zip",
+    "bigbio_kb": "./ChemProt_Corpus.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.RELATION_EXTRACTION, Tasks.NAMED_ENTITY_RECOGNITION]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+# Chemprot specific variables
+# NOTE: There are 3 examples (2 in dev, 1 in training) with CPR:0
+_GROUP_LABELS = {
+    "CPR:0": "Undefined",
+    "CPR:1": "Part_of",
+    "CPR:2": "Regulator",
+    "CPR:3": "Upregulator",
+    "CPR:4": "Downregulator",
+    "CPR:5": "Agonist",
+    "CPR:6": "Antagonist",
+    "CPR:7": "Modulator",
+    "CPR:8": "Cofactor",
+    "CPR:9": "Substrate",
+    "CPR:10": "Not",
+}
+
+
+class ChemprotDataset(datasets.GeneratorBasedBuilder):
+    """BioCreative VI Chemical-Protein Interaction Task."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="chemprot_full_source",
+            version=SOURCE_VERSION,
+            description="chemprot source schema",
+            schema="source",
+            subset_id="chemprot_full",
+        ),
+        BigBioConfig(
+            name="chemprot_shared_task_eval_source",
+            version=SOURCE_VERSION,
+            description="chemprot source schema with only the relation types that were used in the shared task evaluation",
+            schema="source",
+            subset_id="chemprot_shared_task_eval",
+        ),
+        BigBioConfig(
+            name="chemprot_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="chemprot BigBio schema",
+            schema="bigbio_kb",
+            subset_id="chemprot",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "chemprot_full_source"
+
+    def _info(self):
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "pmid": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "entities": datasets.Sequence(
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "offsets": datasets.Sequence(datasets.Value("int64")),
+                        }
+                    ),
+                    "relations": datasets.Sequence(
+                        {
+                            "type": datasets.Value("string"),
+                            "arg1": datasets.Value("string"),
+                            "arg2": 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):
+        """Returns SplitGenerators."""
+        my_urls = _URLs[self.config.schema]
+        data_dir = dl_manager.download_and_extract(my_urls)
+
+        # Extract each of the individual folders
+        # NOTE: omitting "extract" call cause it uses a new folder
+        train_path = dl_manager.extract(
+            os.path.join(data_dir, "ChemProt_Corpus/chemprot_training.zip")
+        )
+        test_path = dl_manager.extract(
+            os.path.join(data_dir, "ChemProt_Corpus/chemprot_test_gs.zip")
+        )
+        dev_path = dl_manager.extract(
+            os.path.join(data_dir, "ChemProt_Corpus/chemprot_development.zip")
+        )
+        sample_path = dl_manager.extract(
+            os.path.join(data_dir, "ChemProt_Corpus/chemprot_sample.zip")
+        )
+
+        return [
+            datasets.SplitGenerator(
+                name="sample",  # should be a named split : /
+                gen_kwargs={
+                    "filepath": os.path.join(sample_path, "chemprot_sample"),
+                    "abstract_file": "chemprot_sample_abstracts.tsv",
+                    "entity_file": "chemprot_sample_entities.tsv",
+                    "relation_file": "chemprot_sample_relations.tsv",
+                    "gold_standard_file": "chemprot_sample_gold_standard.tsv",
+                    "split": "sample",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(train_path, "chemprot_training"),
+                    "abstract_file": "chemprot_training_abstracts.tsv",
+                    "entity_file": "chemprot_training_entities.tsv",
+                    "relation_file": "chemprot_training_relations.tsv",
+                    "gold_standard_file": "chemprot_training_gold_standard.tsv",
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(test_path, "chemprot_test_gs"),
+                    "abstract_file": "chemprot_test_abstracts_gs.tsv",
+                    "entity_file": "chemprot_test_entities_gs.tsv",
+                    "relation_file": "chemprot_test_relations_gs.tsv",
+                    "gold_standard_file": "chemprot_test_gold_standard.tsv",
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(dev_path, "chemprot_development"),
+                    "abstract_file": "chemprot_development_abstracts.tsv",
+                    "entity_file": "chemprot_development_entities.tsv",
+                    "relation_file": "chemprot_development_relations.tsv",
+                    "gold_standard_file": "chemprot_development_gold_standard.tsv",
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self,
+        filepath,
+        abstract_file,
+        entity_file,
+        relation_file,
+        gold_standard_file,
+        split,
+    ):
+        """Yields examples as (key, example) tuples."""
+        if self.config.schema == "source":
+            abstracts = self._get_abstract(os.path.join(filepath, abstract_file))
+
+            entities, entity_id = self._get_entities(os.path.join(filepath, entity_file))
+
+            if self.config.subset_id == "chemprot_full":
+                relations = self._get_relations(os.path.join(filepath, relation_file))
+            elif self.config.subset_id == "chemprot_shared_task_eval":
+                relations = self._get_relations_gs(os.path.join(filepath, gold_standard_file))
+            else:
+                raise ValueError(self.config)
+
+            for id_, pmid in enumerate(abstracts.keys()):
+                yield id_, {
+                    "pmid": pmid,
+                    "text": abstracts[pmid],
+                    "entities": entities[pmid],
+                    "relations": relations.get(pmid, []),
+                }
+
+        elif self.config.schema == "bigbio_kb":
+
+            abstracts = self._get_abstract(os.path.join(filepath, abstract_file))
+            entities, entity_id = self._get_entities(os.path.join(filepath, entity_file))
+            relations = self._get_relations(os.path.join(filepath, relation_file), is_mapped=True)
+
+            uid = 0
+            for id_, pmid in enumerate(abstracts.keys()):
+                data = {
+                    "id": str(uid),
+                    "document_id": str(pmid),
+                    "passages": [],
+                    "entities": [],
+                    "relations": [],
+                    "events": [],
+                    "coreferences": [],
+                }
+                uid += 1
+
+                data["passages"] = [
+                    {
+                        "id": str(uid),
+                        "type": "title and abstract",
+                        "text": [abstracts[pmid]],
+                        "offsets": [[0, len(abstracts[pmid])]],
+                    }
+                ]
+                uid += 1
+
+                entity_to_id = {}
+                for entity in entities[pmid]:
+                    _text = entity["text"]
+                    entity.update({"text": [_text]})
+                    entity_to_id[entity["id"]] = str(uid)
+                    entity.update({"id": str(uid)})
+                    _offsets = entity["offsets"]
+                    entity.update({"offsets": [_offsets]})
+                    entity["normalized"] = []
+                    data["entities"].append(entity)
+                    uid += 1
+
+                for relation in relations.get(pmid, []):
+                    relation["arg1_id"] = entity_to_id[relation.pop("arg1")]
+                    relation["arg2_id"] = entity_to_id[relation.pop("arg2")]
+                    relation.update({"id": str(uid)})
+                    relation["normalized"] = []
+                    data["relations"].append(relation)
+                    uid += 1
+
+                yield id_, data
+
+    @staticmethod
+    def _get_abstract(abs_filename: str) -> Dict[str, str]:
+        """For each document in PubMed ID (PMID) in the ChemProt abstract data file, return the
+        abstract. Data is tab-separated.
+
+        :param filename:`*_abstracts.tsv from ChemProt :returns Dictionary with PMID keys and
+            abstract text as values.
+        """
+        with open(abs_filename) as f:
+            contents = [i.strip() for i in f.readlines()]
+
+        # PMID is the first column, Abstract is last
+        return {
+            doc.split("\t")[0]: "\n".join(doc.split("\t")[1:]) for doc in contents
+        }  # Includes title as line 1
+
+    @staticmethod
+    def _get_entities(ents_filename: str) -> Tuple[Dict[str, str]]:
+        """
+        For each document in the corpus, return entity annotations per PMID.
+        Each column in the entity file is as follows:
+        (1) PMID
+        (2) Entity Number
+        (3) Entity Type (Chemical, Gene-Y, Gene-N)
+        (4) Start index
+        (5) End index
+        (6) Actual text of entity
+
+        :param ents_filename: `_*entities.tsv` file from ChemProt
+
+        :returns: Dictionary with PMID keys and entity annotations.
+        """
+        with open(ents_filename) as f:
+            contents = [i.strip() for i in f.readlines()]
+
+        entities = {}
+        entity_id = {}
+
+        for line in contents:
+
+            pmid, idx, label, start_offset, end_offset, name = line.split("\t")
+
+            # Populate entity dictionary
+            if pmid not in entities:
+                entities[pmid] = []
+
+            ann = {
+                "offsets": [int(start_offset), int(end_offset)],
+                "text": name,
+                "type": label,
+                "id": idx,
+            }
+
+            entities[pmid].append(ann)
+
+            # Populate entity mapping
+            entity_id.update({idx: name})
+
+        return entities, entity_id
+
+    @staticmethod
+    def _get_relations(rel_filename: str, is_mapped: bool = False) -> Dict[str, str]:
+        """For each document in the ChemProt corpus, create an annotation for all relationships.
+
+        :param is_mapped: Whether to convert into NL the relation tags. Default is OFF
+        """
+        with open(rel_filename) as f:
+            contents = [i.strip() for i in f.readlines()]
+
+        relations = {}
+
+        for line in contents:
+            pmid, label, _, _, arg1, arg2 = line.split("\t")
+            arg1 = arg1.split("Arg1:")[-1]
+            arg2 = arg2.split("Arg2:")[-1]
+
+            if pmid not in relations:
+                relations[pmid] = []
+
+            if is_mapped:
+                label = _GROUP_LABELS[label]
+
+            ann = {
+                "type": label,
+                "arg1": arg1,
+                "arg2": arg2,
+            }
+
+            relations[pmid].append(ann)
+
+        return relations
+
+    @staticmethod
+    def _get_relations_gs(rel_filename: str, is_mapped: bool = False) -> Dict[str, str]:
+        """For each document in the ChemProt corpus, create an annotation for the gold-standard
+        relationships.
+
+        The columns include:
+        (1) PMID
+        (2) Relationship Label (CPR)
+        (3) Used in shared task
+        (3) Interactor Argument 1 Entity Identifier
+        (4) Interactor Argument 2 Entity Identifier
+
+        Gold standard includes CPRs 3-9. Relationships are always Gene + Protein.
+        Unlike entities, there is no counter, hence once must be made
+
+        :param rel_filename: Gold standard file name
+        :param ent_dict: Entity Identifier to text
+        """
+        with open(rel_filename) as f:
+            contents = [i.strip() for i in f.readlines()]
+
+        relations = {}
+
+        for line in contents:
+            pmid, label, arg1, arg2 = line.split("\t")
+            arg1 = arg1.split("Arg1:")[-1]
+            arg2 = arg2.split("Arg2:")[-1]
+
+            if pmid not in relations:
+                relations[pmid] = []
+
+            if is_mapped:
+                label = _GROUP_LABELS[label]
+
+            ann = {
+                "type": label,
+                "arg1": arg1,
+                "arg2": arg2,
+            }
+
+            relations[pmid].append(ann)
+
+        return relations