Update czi_drsm based on git version f5bf778

README.md

@@ -1,3 +1,68 @@
 ---
-license: apache-2.0
+language:
+  - en 
+bigbio_language:
+  - English
+license: cc0-1.0
+bigbio_license_shortname: cc0-1.0
+multilinguality: monolingual
+pretty_name: CZI DRSM
+homepage: https://github.com/chanzuckerberg/DRSM-corpus
+bigbio_pubmed: false
+bigbio_public: true
+bigbio_tasks:
+  - TXTCLASS
 ---
+
+# Dataset Card for CZI DRSM
+[README.md](..%2Fmed_qa%2FREADME.md)
+## Dataset Description
+
+- **Homepage:** https://github.com/chanzuckerberg/DRSM-corpus
+- **Pubmed:** False
+- **Public:** True
+- **Tasks:** TXTCLASS
+
+Research Article document classification dataset based on aspects of disease research. Currently, the dataset consists of three subsets: 
+
+(A) classifies title/abstracts of papers into most popular subtypes of clinical, basic, and translational papers (~20k papers); 
+    - Clinical Characteristics, Disease Pathology, and Diagnosis - 
+        Text that describes (A) symptoms, signs, or ‘phenotype’ of a disease; 
+        (B) the effects of the disease on patient organs, tissues, or cells; 
+        (C) the results of clinical tests that reveal pathology (including
+        biomarkers); (D) research that use this information to figure out
+        a diagnosis.
+    - Therapeutics in the clinic - 
+        Text describing how treatments work in the clinic (but not in a clinical trial).
+    - Disease mechanism - 
+        Text that describes either (A) mechanistic involvement of specific genes in disease 
+        (deletions, gain of function, etc); (B) how molecular signalling or metabolism 
+        binding, activating, phosphorylation, concentration increase, etc.) 
+        are involved in the mechanism of a disease; or (C) the physiological 
+        mechanism of disease at the level of tissues, organs, and body systems.
+    - Patient-Based Therapeutics - 
+        Text describing (A) Clinical trials (studies of therapeutic measures being 
+        used on patients in a clinical trial); (B) Post Marketing Drug Surveillance 
+        (effects of a drug after approval in the general population or as part of 
+        ‘standard healthcare’); (C) Drug repurposing (how a drug that has been 
+        approved for one use is being applied to a new disease).
+
+(B) identifies whether a title/abstract of a paper describes substantive research into Quality of Life (~10k papers); 
+    - -1 - the paper is not a primary experimental study in rare disease
+    - 0 - the study does not directly investigate quality of life
+    - 1 - the study investigates qol but not as its primary contribution
+    - 2 - the study's primary contribution centers on quality of life measures
+
+(C) identifies if a paper is a natural history study (~10k papers). 
+   - -1 - the paper is not a primary experimental study in rare disease
+    - 0 - the study is not directly investigating the natural history of a disease
+    - 1 - the study includes some elements a natural history but not as its primary contribution
+    - 2 - the study's primary contribution centers on observing the time course of a rare disease
+    
+These classifications are particularly relevant in rare disease research, a field that is generally understudied.
+
+## Citation Information
+
+```
+# N/A
+```

bigbiohub.py

@@ -0,0 +1,590 @@
+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)
+        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

czi_drsm.py

@@ -0,0 +1,410 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and Gully Burns.
+#
+# 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.
+
+"""
+Research Article document classification dataset based on aspects of disease research. Currently, the dataset consists of three subsets: 
+
+(A) classifies title/abstracts of papers into most popular subtypes of clinical, basic, and translational papers (~20k papers); 
+    - Clinical Characteristics, Disease Pathology, and Diagnosis - 
+        Text that describes (A) symptoms, signs, or ‘phenotype’ of a disease; 
+        (B) the effects of the disease on patient organs, tissues, or cells; 
+        (C) the results of clinical tests that reveal pathology (including
+        biomarkers); (D) research that use this information to figure out
+        a diagnosis.
+    - Therapeutics in the clinic - 
+        Text describing how treatments work in the clinic (but not in a clinical trial).
+    - Disease mechanism - 
+        Text that describes either (A) mechanistic involvement of specific genes in disease 
+        (deletions, gain of function, etc); (B) how molecular signalling or metabolism 
+        binding, activating, phosphorylation, concentration increase, etc.) 
+        are involved in the mechanism of a disease; or (C) the physiological 
+        mechanism of disease at the level of tissues, organs, and body systems.
+    - Patient-Based Therapeutics - 
+        Text describing (A) Clinical trials (studies of therapeutic measures being 
+        used on patients in a clinical trial); (B) Post Marketing Drug Surveillance 
+        (effects of a drug after approval in the general population or as part of 
+        ‘standard healthcare’); (C) Drug repurposing (how a drug that has been 
+        approved for one use is being applied to a new disease).
+
+(B) identifies whether a title/abstract of a paper describes substantive research into Quality of Life (~10k papers); 
+    - -1 - the paper is not a primary experimental study in rare disease
+    - 0 - the study does not directly investigate quality of life
+    - 1 - the study investigates qol but not as its primary contribution
+    - 2 - the study's primary contribution centers on quality of life measures
+
+(C) identifies if a paper is a natural history study (~10k papers). 
+`   - -1 - the paper is not a primary experimental study in rare disease
+    - 0 - the study is not directly investigating the natural history of a disease
+    - 1 - the study includes some elements a natural history but not as its primary contribution
+    - 2 - the study's primary contribution centers on observing the time course of a rare disease
+    
+These classifications are particularly relevant in rare disease research, a field that is generally understudied.
+"""
+
+import os
+from typing import List, Tuple, Dict
+
+import datasets
+import pandas as pd
+from pathlib import Path
+
+import bigbio.utils.parsing as parse
+from bigbio.utils import schemas
+from bigbio.utils.configs import BigBioConfig
+from bigbio.utils.constants import Lang, Tasks
+from bigbio.utils.license import Licenses
+
+#from .bigbiohub import BigBioConfig
+#from .bigbiohub import Tasks
+
+#from .bigbiohub import 
+
+_LOCAL = False
+
+_CITATION = """\
+@article{,
+  author    = {},
+  title     = {},
+  journal   = {},
+  volume    = {},
+  year      = {},
+  url       = {},
+  doi       = {},
+  biburl    = {},
+  bibsource = {}
+}
+"""
+
+_DATASETNAME = "czi_drsm"
+
+_DESCRIPTION = """\
+Research Article document classification dataset based on aspects of disease research. Currently, the dataset consists of three subsets: 
+
+(A) classifies title/abstracts of papers into most popular subtypes of clinical, basic, and translational papers (~20k papers); 
+    - Clinical Characteristics, Disease Pathology, and Diagnosis - 
+        Text that describes (A) symptoms, signs, or ‘phenotype’ of a disease; 
+        (B) the effects of the disease on patient organs, tissues, or cells; 
+        (C) the results of clinical tests that reveal pathology (including
+        biomarkers); (D) research that use this information to figure out
+        a diagnosis.
+    - Therapeutics in the clinic - 
+        Text describing how treatments work in the clinic (but not in a clinical trial).
+    - Disease mechanism - 
+        Text that describes either (A) mechanistic involvement of specific genes in disease 
+        (deletions, gain of function, etc); (B) how molecular signalling or metabolism 
+        binding, activating, phosphorylation, concentration increase, etc.) 
+        are involved in the mechanism of a disease; or (C) the physiological 
+        mechanism of disease at the level of tissues, organs, and body systems.
+    - Patient-Based Therapeutics - 
+        Text describing (A) Clinical trials (studies of therapeutic measures being 
+        used on patients in a clinical trial); (B) Post Marketing Drug Surveillance 
+        (effects of a drug after approval in the general population or as part of 
+        ‘standard healthcare’); (C) Drug repurposing (how a drug that has been 
+        approved for one use is being applied to a new disease).
+
+(B) identifies whether a title/abstract of a paper describes substantive research into Quality of Life (~10k papers); 
+    - -1 - the paper is not a primary experimental study in rare disease
+    - 0 - the study does not directly investigate quality of life
+    - 1 - the study investigates qol but not as its primary contribution
+    - 2 - the study's primary contribution centers on quality of life measures
+
+(C) identifies if a paper is a natural history study (~10k papers). 
+`   - -1 - the paper is not a primary experimental study in rare disease
+    - 0 - the study is not directly investigating the natural history of a disease
+    - 1 - the study includes some elements a natural history but not as its primary contribution
+    - 2 - the study's primary contribution centers on observing the time course of a rare disease
+    
+These classifications are particularly relevant in rare disease research, a field that is generally understudied.
+"""
+
+_HOMEPAGE = "https://github.com/chanzuckerberg/DRSM-corpus/"
+_LICENSE = "CC0_1p0"
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = False
+_DISPLAYNAME = "DRSM Corpus"
+
+# For publicly available datasets you will most likely end up passing these URLs to dl_manager in _split_generators.
+# In most cases the URLs will be the same for the source and bigbio config.
+# However, if you need to access different files for each config you can have multiple entries in this dict.
+# This can be an arbitrarily nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    'base': "https://raw.githubusercontent.com/chanzuckerberg/DRSM-corpus/main/v1/drsm_corpus_v1.tsv",
+    'qol': "https://raw.githubusercontent.com/chanzuckerberg/DRSM-corpus/main/v2/qol_all_2022_12_15.tsv",
+    'nhs': "https://raw.githubusercontent.com/chanzuckerberg/DRSM-corpus/main/v2/nhs_all_2023_03_31.tsv"
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]  
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+_CLASS_NAMES_BASE = [
+    "clinical characteristics or disease pathology",
+    "therapeutics in the clinic",
+    "disease mechanism",
+    "patient-based therapeutics",
+    "other",
+    "irrelevant"
+    ]
+
+_CLASS_NAMES_QOL = [
+    "-1 - the paper is not a primary experimental study in rare disease",
+    "0 - the study does not directly investigate quality of life",
+    "1 - the study investigates qol but not as its primary contribution",
+    "2 - the study's primary contribution centers on quality of life measures"
+    ]
+
+_CLASS_NAMES_NHS = [
+    "-1 - the paper is not a primary experimental study in rare disease",
+    "0 - the study is not directly investigating the natural history of a disease",
+    "1 - the study includes some elements a natural history but not as its primary contribution",
+    "2 - the study's primary contribution centers on observing the time course of a rare disease"
+    ]
+
+class DRSMBaseDataset(datasets.GeneratorBasedBuilder):
+    """DRSM Document Classification Datasets."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    # You will be able to load the "source" or "bigbio" configurations with
+    #ds_source = datasets.load_dataset('drsm_source_dataset', name='source')
+    #ds_bigbio = datasets.load_dataset('drsm_bigbio_dataset', name='bigbio')
+
+    # For local datasets you can make use of the `data_dir` and `data_files` kwargs
+    # https://huggingface.co/docs/datasets/add_dataset.html#downloading-data-files-and-organizing-splits
+    # ds_source = datasets.load_dataset('my_dataset', name='source', data_dir="/path/to/data/files")
+    # ds_bigbio = datasets.load_dataset('my_dataset', name='bigbio', data_dir="/path/to/data/files")
+
+    # TODO: For each dataset, implement Config for Source and BigBio;
+    #  If dataset contains more than one subset (see examples/bioasq.py) implement for EACH of them.
+    #  Each of them should contain:
+    #   - name: should be unique for each dataset config eg. bioasq10b_(source|bigbio)_[bigbio_schema_name]
+    #   - version: option = (SOURCE_VERSION|BIGBIO_VERSION)
+    #   - description: one line description for the dataset
+    #   - schema: options = (source|bigbio_[bigbio_schema_name])
+    #   - subset_id: subset id is the canonical name for the dataset (eg. bioasq10b)
+    #  where [bigbio_schema_name] = ()
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="czi_drsm_base_source",
+            version=SOURCE_VERSION,
+            description="czi_drsm base source schema",
+            schema="base_source",
+            subset_id="czi_drsm_base",
+        ),
+        BigBioConfig(
+            name="czi_drsm_bigbio_base_text",
+            version=BIGBIO_VERSION,
+            description="czi_drsm base BigBio schema",
+            schema="bigbio_text",
+            subset_id="czi_drsm_base",
+        ),
+        BigBioConfig(
+            name="czi_drsm_qol_source",
+            version=SOURCE_VERSION,
+            description="czi_drsm source schema for Quality of Life studies",
+            schema="qol_source",
+            subset_id="czi_drsm_qol",
+        ),
+        BigBioConfig(
+            name="czi_drsm_bigbio_qol_text",
+            version=BIGBIO_VERSION,
+            description="czi_drsm BigBio schema for Quality of Life studies",
+            schema="bigbio_text",
+            subset_id="czi_drsm_qol",
+        ),
+        BigBioConfig(
+            name="czi_drsm_nhs_source",
+            version=SOURCE_VERSION,
+            description="czi_drsm source schema for Natural History Studies",
+            schema="nhs_source",
+            subset_id="czi_drsm_nhs",
+        ),
+        BigBioConfig(
+            name="czi_drsm_bigbio_nhs_text",
+            version=BIGBIO_VERSION,
+            description="czi_drsm BigBio schema for Natural History Studies",
+            schema="bigbio_text",
+            subset_id="czi_drsm_nhs",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "czi_drsm_bigbio_base_text"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        # Create the source schema; this schema will keep all keys/information/labels as close to the original dataset as possible.
+
+        # You can arbitrarily nest lists and dictionaries.
+        # For iterables, use lists over tuples or `datasets.Sequence`
+
+        if self.config.schema == "base_source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "labeling_state": datasets.Value("string"),
+                    "explanation": datasets.Value("string"),
+                    "correct_label": [datasets.ClassLabel(names=_CLASS_NAMES_BASE)],
+                    "agreement": [datasets.Value("string")],
+                    "title": [datasets.Value("string")],
+                    "abstract": [datasets.Value("string")],
+                }
+            )
+
+        elif self.config.schema == "qol_source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "labeling_state": datasets.Value("string"),
+                    "correct_label": [datasets.ClassLabel(names=_CLASS_NAMES_QOL)],
+                    "explanation": datasets.Value("string"),
+                    "agreement": [datasets.Value("string")],
+                    "title": [datasets.Value("string")],
+                    "abstract": [datasets.Value("string")]
+                }
+            )
+
+        elif self.config.schema == "nhs_source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "labeling_state": datasets.Value("string"),
+                    "correct_label": [datasets.ClassLabel(names=_CLASS_NAMES_NHS)],
+                    "explanation": datasets.Value("string"),
+                    "agreement": [datasets.Value("string")],
+                    "title": [datasets.Value("string")],
+                    "abstract": [datasets.Value("string")],
+                }
+            )
+
+        # For example bigbio_kb, bigbio_t2t
+        elif self.config.schema == "bigbio_text":
+            features = schemas.text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        if 'base' in self.config.name:
+            url = _URLS['base']
+        elif 'qol' in self.config.name:
+            url = _URLS['qol']
+        elif 'nhs' in self.config.name:
+            url = _URLS['nhs']
+        else:
+            raise ValueError("Invalid config name: {}".format(self.config.name))        
+
+        data_file = dl_manager.download_and_extract(url)
+        df = pd.read_csv(data_file, sep="\t", encoding="utf-8").fillna('')
+
+        # load tsv file into huggingface dataset
+        ds = datasets.Dataset.from_pandas(df)
+        
+        # generate train_test split
+        ds_dict = ds.train_test_split(test_size=0.2, seed=42)
+        ds_dict2 = ds_dict['test'].train_test_split(test_size=0.5, seed=42)
+        
+        # dump train, val, test to disk
+        data_dir = Path(data_file).parent
+        ds_dict['train'].to_csv(data_dir / "train.tsv", sep="\t", index=False)
+        ds_dict2['train'].to_csv(data_dir / "validation.tsv", sep="\t", index=False)
+        ds_dict2['test'].to_csv(data_dir / "test.tsv", sep="\t", index=False)    
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir / "train.tsv",
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir / "validation.tsv",
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir / "test.tsv",
+                    "split": "test",
+                },
+            )
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        df = pd.read_csv(filepath, sep="\t", encoding="utf-8").fillna('')
+        print(len(df))
+        for id_, l in df.iterrows():
+            if self.config.subset_id == "czi_drsm_base":
+                doc_id = l[0]
+                labeling_state = l[1]
+                correct_label = l[2]
+                agreement = l[3]
+                explanation = l[4]
+                title = l[5]
+                abstract = l[6]
+            elif self.config.subset_id == "czi_drsm_qol":
+                doc_id = l[0]
+                labeling_state = l[1]
+                correct_label = l[2][1:-1]
+                explanation = l[3]
+                agreement = l[4]
+                title = l[5]
+                abstract = l[6]                
+            elif self.config.subset_id == "czi_drsm_nhs":
+                doc_id = l[0]
+                labeling_state = l[1]
+                correct_label = l[2][1:-1]
+                explanation = ''
+                agreement = l[3]
+                title = l[4]
+                abstract = l[5]
+
+            if "_source" in self.config.schema:
+                yield id_, {
+                    "document_id": doc_id,  
+                    "labeling_state": labeling_state,
+                    "explanation": explanation,
+                    "correct_label": [correct_label],
+                    "agreement": str(agreement),
+                    "title": title,
+                    "abstract": abstract
+                }
+            elif self.config.schema == "bigbio_text":                    
+                yield id_, {
+                    "id": id_,
+                    "document_id": doc_id,
+                    "text": title + " " + abstract,
+                    "labels": [correct_label]
+                }
+
+# This template is based on the following template from the datasets package:
+# https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py