Update parquet files

.gitattributes

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

@@ -1,556 +0,0 @@
-from collections import defaultdict
-from dataclasses import dataclass
-from enum import Enum
-import logging
-from pathlib import Path
-from types import SimpleNamespace
-from typing import TYPE_CHECKING, Dict, Iterable, List, Tuple
-
-import datasets
-
-if TYPE_CHECKING:
-    import bioc
-
-logger = logging.getLogger(__name__)
-
-
-BigBioValues = SimpleNamespace(NULL="<BB_NULL_STR>")
-
-
-@dataclass
-class BigBioConfig(datasets.BuilderConfig):
-    """BuilderConfig for BigBio."""
-
-    name: str = None
-    version: datasets.Version = None
-    description: str = None
-    schema: str = None
-    subset_id: str = None
-
-
-class Tasks(Enum):
-    NAMED_ENTITY_RECOGNITION = "NER"
-    NAMED_ENTITY_DISAMBIGUATION = "NED"
-    EVENT_EXTRACTION = "EE"
-    RELATION_EXTRACTION = "RE"
-    COREFERENCE_RESOLUTION = "COREF"
-    QUESTION_ANSWERING = "QA"
-    TEXTUAL_ENTAILMENT = "TE"
-    SEMANTIC_SIMILARITY = "STS"
-    TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS"
-    PARAPHRASING = "PARA"
-    TRANSLATION = "TRANSL"
-    SUMMARIZATION = "SUM"
-    TEXT_CLASSIFICATION = "TXTCLASS"
-
-
-entailment_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "premise": datasets.Value("string"),
-        "hypothesis": datasets.Value("string"),
-        "label": datasets.Value("string"),
-    }
-)
-
-pairs_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "document_id": datasets.Value("string"),
-        "text_1": datasets.Value("string"),
-        "text_2": datasets.Value("string"),
-        "label": datasets.Value("string"),
-    }
-)
-
-qa_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "question_id": datasets.Value("string"),
-        "document_id": datasets.Value("string"),
-        "question": datasets.Value("string"),
-        "type": datasets.Value("string"),
-        "choices": [datasets.Value("string")],
-        "context": datasets.Value("string"),
-        "answer": datasets.Sequence(datasets.Value("string")),
-    }
-)
-
-text_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "document_id": datasets.Value("string"),
-        "text": datasets.Value("string"),
-        "labels": [datasets.Value("string")],
-    }
-)
-
-text2text_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "document_id": datasets.Value("string"),
-        "text_1": datasets.Value("string"),
-        "text_2": datasets.Value("string"),
-        "text_1_name": datasets.Value("string"),
-        "text_2_name": datasets.Value("string"),
-    }
-)
-
-kb_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "document_id": datasets.Value("string"),
-        "passages": [
-            {
-                "id": datasets.Value("string"),
-                "type": datasets.Value("string"),
-                "text": datasets.Sequence(datasets.Value("string")),
-                "offsets": datasets.Sequence([datasets.Value("int32")]),
-            }
-        ],
-        "entities": [
-            {
-                "id": datasets.Value("string"),
-                "type": datasets.Value("string"),
-                "text": datasets.Sequence(datasets.Value("string")),
-                "offsets": datasets.Sequence([datasets.Value("int32")]),
-                "normalized": [
-                    {
-                        "db_name": datasets.Value("string"),
-                        "db_id": datasets.Value("string"),
-                    }
-                ],
-            }
-        ],
-        "events": [
-            {
-                "id": datasets.Value("string"),
-                "type": datasets.Value("string"),
-                # refers to the text_bound_annotation of the trigger
-                "trigger": {
-                    "text": datasets.Sequence(datasets.Value("string")),
-                    "offsets": datasets.Sequence([datasets.Value("int32")]),
-                },
-                "arguments": [
-                    {
-                        "role": datasets.Value("string"),
-                        "ref_id": datasets.Value("string"),
-                    }
-                ],
-            }
-        ],
-        "coreferences": [
-            {
-                "id": datasets.Value("string"),
-                "entity_ids": datasets.Sequence(datasets.Value("string")),
-            }
-        ],
-        "relations": [
-            {
-                "id": datasets.Value("string"),
-                "type": datasets.Value("string"),
-                "arg1_id": datasets.Value("string"),
-                "arg2_id": datasets.Value("string"),
-                "normalized": [
-                    {
-                        "db_name": datasets.Value("string"),
-                        "db_id": datasets.Value("string"),
-                    }
-                ],
-            }
-        ],
-    }
-)
-
-
-def get_texts_and_offsets_from_bioc_ann(ann: "bioc.BioCAnnotation") -> Tuple:
-
-    offsets = [(loc.offset, loc.offset + loc.length) for loc in ann.locations]
-
-    text = ann.text
-
-    if len(offsets) > 1:
-        i = 0
-        texts = []
-        for start, end in offsets:
-            chunk_len = end - start
-            texts.append(text[i : chunk_len + i])
-            i += chunk_len
-            while i < len(text) and text[i] == " ":
-                i += 1
-    else:
-        texts = [text]
-
-    return offsets, texts
-
-
-def remove_prefix(a: str, prefix: str) -> str:
-    if a.startswith(prefix):
-        a = a[len(prefix) :]
-    return a
-
-
-def parse_brat_file(
-    txt_file: Path,
-    annotation_file_suffixes: List[str] = None,
-    parse_notes: bool = False,
-) -> Dict:
-    """
-    Parse a brat file into the schema defined below.
-    `txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
-    Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
-    e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
-    Will include annotator notes, when `parse_notes == True`.
-    brat_features = datasets.Features(
-        {
-            "id": datasets.Value("string"),
-            "document_id": datasets.Value("string"),
-            "text": datasets.Value("string"),
-            "text_bound_annotations": [  # T line in brat, e.g. type or event trigger
-                {
-                    "offsets": datasets.Sequence([datasets.Value("int32")]),
-                    "text": datasets.Sequence(datasets.Value("string")),
-                    "type": datasets.Value("string"),
-                    "id": datasets.Value("string"),
-                }
-            ],
-            "events": [  # E line in brat
-                {
-                    "trigger": datasets.Value(
-                        "string"
-                    ),  # refers to the text_bound_annotation of the trigger,
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "arguments": datasets.Sequence(
-                        {
-                            "role": datasets.Value("string"),
-                            "ref_id": datasets.Value("string"),
-                        }
-                    ),
-                }
-            ],
-            "relations": [  # R line in brat
-                {
-                    "id": datasets.Value("string"),
-                    "head": {
-                        "ref_id": datasets.Value("string"),
-                        "role": datasets.Value("string"),
-                    },
-                    "tail": {
-                        "ref_id": datasets.Value("string"),
-                        "role": datasets.Value("string"),
-                    },
-                    "type": datasets.Value("string"),
-                }
-            ],
-            "equivalences": [  # Equiv line in brat
-                {
-                    "id": datasets.Value("string"),
-                    "ref_ids": datasets.Sequence(datasets.Value("string")),
-                }
-            ],
-            "attributes": [  # M or A lines in brat
-                {
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "ref_id": datasets.Value("string"),
-                    "value": datasets.Value("string"),
-                }
-            ],
-            "normalizations": [  # N lines in brat
-                {
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "ref_id": datasets.Value("string"),
-                    "resource_name": datasets.Value(
-                        "string"
-                    ),  # Name of the resource, e.g. "Wikipedia"
-                    "cuid": datasets.Value(
-                        "string"
-                    ),  # ID in the resource, e.g. 534366
-                    "text": datasets.Value(
-                        "string"
-                    ),  # Human readable description/name of the entity, e.g. "Barack Obama"
-                }
-            ],
-            ### OPTIONAL: Only included when `parse_notes == True`
-            "notes": [  # # lines in brat
-                {
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "ref_id": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                }
-            ],
-        },
-        )
-    """
-
-    example = {}
-    example["document_id"] = txt_file.with_suffix("").name
-    with txt_file.open() as f:
-        example["text"] = f.read()
-
-    # If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
-    # for event extraction
-    if annotation_file_suffixes is None:
-        annotation_file_suffixes = [".a1", ".a2", ".ann"]
-
-    if len(annotation_file_suffixes) == 0:
-        raise AssertionError(
-            "At least one suffix for the to-be-read annotation files should be given!"
-        )
-
-    ann_lines = []
-    for suffix in annotation_file_suffixes:
-        annotation_file = txt_file.with_suffix(suffix)
-        if annotation_file.exists():
-            with annotation_file.open() as f:
-                ann_lines.extend(f.readlines())
-
-    example["text_bound_annotations"] = []
-    example["events"] = []
-    example["relations"] = []
-    example["equivalences"] = []
-    example["attributes"] = []
-    example["normalizations"] = []
-
-    if parse_notes:
-        example["notes"] = []
-
-    for line in ann_lines:
-        line = line.strip()
-        if not line:
-            continue
-
-        if line.startswith("T"):  # Text bound
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["type"] = fields[1].split()[0]
-            ann["offsets"] = []
-            span_str = remove_prefix(fields[1], (ann["type"] + " "))
-            text = fields[2]
-            for span in span_str.split(";"):
-                start, end = span.split()
-                ann["offsets"].append([int(start), int(end)])
-
-            # Heuristically split text of discontiguous entities into chunks
-            ann["text"] = []
-            if len(ann["offsets"]) > 1:
-                i = 0
-                for start, end in ann["offsets"]:
-                    chunk_len = end - start
-                    ann["text"].append(text[i : chunk_len + i])
-                    i += chunk_len
-                    while i < len(text) and text[i] == " ":
-                        i += 1
-            else:
-                ann["text"] = [text]
-
-            example["text_bound_annotations"].append(ann)
-
-        elif line.startswith("E"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-
-            ann["type"], ann["trigger"] = fields[1].split()[0].split(":")
-
-            ann["arguments"] = []
-            for role_ref_id in fields[1].split()[1:]:
-                argument = {
-                    "role": (role_ref_id.split(":"))[0],
-                    "ref_id": (role_ref_id.split(":"))[1],
-                }
-                ann["arguments"].append(argument)
-
-            example["events"].append(ann)
-
-        elif line.startswith("R"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["type"] = fields[1].split()[0]
-
-            ann["head"] = {
-                "role": fields[1].split()[1].split(":")[0],
-                "ref_id": fields[1].split()[1].split(":")[1],
-            }
-            ann["tail"] = {
-                "role": fields[1].split()[2].split(":")[0],
-                "ref_id": fields[1].split()[2].split(":")[1],
-            }
-
-            example["relations"].append(ann)
-
-        # '*' seems to be the legacy way to mark equivalences,
-        # but I couldn't find any info on the current way
-        # this might have to be adapted dependent on the brat version
-        # of the annotation
-        elif line.startswith("*"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["ref_ids"] = fields[1].split()[1:]
-
-            example["equivalences"].append(ann)
-
-        elif line.startswith("A") or line.startswith("M"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-
-            info = fields[1].split()
-            ann["type"] = info[0]
-            ann["ref_id"] = info[1]
-
-            if len(info) > 2:
-                ann["value"] = info[2]
-            else:
-                ann["value"] = ""
-
-            example["attributes"].append(ann)
-
-        elif line.startswith("N"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["text"] = fields[2]
-
-            info = fields[1].split()
-
-            ann["type"] = info[0]
-            ann["ref_id"] = info[1]
-            ann["resource_name"] = info[2].split(":")[0]
-            ann["cuid"] = info[2].split(":")[1]
-            example["normalizations"].append(ann)
-
-        elif parse_notes and line.startswith("#"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["text"] = fields[2] if len(fields) == 3 else BigBioValues.NULL
-
-            info = fields[1].split()
-
-            ann["type"] = info[0]
-            ann["ref_id"] = info[1]
-            example["notes"].append(ann)
-
-    return example
-
-
-def brat_parse_to_bigbio_kb(brat_parse: Dict) -> Dict:
-    """
-    Transform a brat parse (conforming to the standard brat schema) obtained with
-    `parse_brat_file` into a dictionary conforming to the `bigbio-kb` schema (as defined in ../schemas/kb.py)
-    :param brat_parse:
-    """
-
-    unified_example = {}
-
-    # Prefix all ids with document id to ensure global uniqueness,
-    # because brat ids are only unique within their document
-    id_prefix = brat_parse["document_id"] + "_"
-
-    # identical
-    unified_example["document_id"] = brat_parse["document_id"]
-    unified_example["passages"] = [
-        {
-            "id": id_prefix + "_text",
-            "type": "abstract",
-            "text": [brat_parse["text"]],
-            "offsets": [[0, len(brat_parse["text"])]],
-        }
-    ]
-
-    # get normalizations
-    ref_id_to_normalizations = defaultdict(list)
-    for normalization in brat_parse["normalizations"]:
-        ref_id_to_normalizations[normalization["ref_id"]].append(
-            {
-                "db_name": normalization["resource_name"],
-                "db_id": normalization["cuid"],
-            }
-        )
-
-    # separate entities and event triggers
-    unified_example["events"] = []
-    non_event_ann = brat_parse["text_bound_annotations"].copy()
-    for event in brat_parse["events"]:
-        event = event.copy()
-        event["id"] = id_prefix + event["id"]
-        trigger = next(
-            tr
-            for tr in brat_parse["text_bound_annotations"]
-            if tr["id"] == event["trigger"]
-        )
-        if trigger in non_event_ann:
-            non_event_ann.remove(trigger)
-        event["trigger"] = {
-            "text": trigger["text"].copy(),
-            "offsets": trigger["offsets"].copy(),
-        }
-        for argument in event["arguments"]:
-            argument["ref_id"] = id_prefix + argument["ref_id"]
-
-        unified_example["events"].append(event)
-
-    unified_example["entities"] = []
-    anno_ids = [ref_id["id"] for ref_id in non_event_ann]
-    for ann in non_event_ann:
-        entity_ann = ann.copy()
-        entity_ann["id"] = id_prefix + entity_ann["id"]
-        entity_ann["normalized"] = ref_id_to_normalizations[ann["id"]]
-        unified_example["entities"].append(entity_ann)
-
-    # massage relations
-    unified_example["relations"] = []
-    skipped_relations = set()
-    for ann in brat_parse["relations"]:
-        if (
-            ann["head"]["ref_id"] not in anno_ids
-            or ann["tail"]["ref_id"] not in anno_ids
-        ):
-            skipped_relations.add(ann["id"])
-            continue
-        unified_example["relations"].append(
-            {
-                "arg1_id": id_prefix + ann["head"]["ref_id"],
-                "arg2_id": id_prefix + ann["tail"]["ref_id"],
-                "id": id_prefix + ann["id"],
-                "type": ann["type"],
-                "normalized": [],
-            }
-        )
-    if len(skipped_relations) > 0:
-        example_id = brat_parse["document_id"]
-        logger.info(
-            f"Example:{example_id}: The `bigbio_kb` schema allows `relations` only between entities."
-            f" Skip (for now): "
-            f"{list(skipped_relations)}"
-        )
-
-    # get coreferences
-    unified_example["coreferences"] = []
-    for i, ann in enumerate(brat_parse["equivalences"], start=1):
-        is_entity_cluster = True
-        for ref_id in ann["ref_ids"]:
-            if not ref_id.startswith("T"):  # not textbound -> no entity
-                is_entity_cluster = False
-            elif ref_id not in anno_ids:  # event trigger -> no entity
-                is_entity_cluster = False
-        if is_entity_cluster:
-            entity_ids = [id_prefix + i for i in ann["ref_ids"]]
-            unified_example["coreferences"].append(
-                {"id": id_prefix + str(i), "entity_ids": entity_ids}
-            )
-    return unified_example

scifact.py

@@ -1,421 +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.
-
-import json
-import os
-from itertools import chain
-from typing import Dict, List, Tuple
-
-import datasets
-from datasets import Value
-import pandas as pd
-
-from .bigbiohub import pairs_features
-from .bigbiohub import BigBioConfig
-from .bigbiohub import Tasks
-
-_LANGUAGES = ['English']
-_PUBMED = False
-_LOCAL = False
-_CITATION = """\
-@article{wadden2020fact,
-  author    = {David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
-  title     = {Fact or Fiction: Verifying Scientific Claims},
-  year      = {2020},
-  address   = {Online},
-  publisher = {Association for Computational Linguistics},
-  url       = {https://aclanthology.org/2020.emnlp-main.609},
-  doi       = {10.18653/v1/2020.emnlp-main.609},
-  pages     = {7534--7550},
-  biburl    = {},
-  bibsource = {}
-}
-"""
-
-_DATASETNAME = "scifact"
-_DISPLAYNAME = "SciFact"
-
-
-_DESCRIPTION_BASE = """\
-    SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
-    """
-
-_SOURCE_CORPUS_DESCRIPTION = f"""\
-    {_DESCRIPTION_BASE} This config has abstracts and document ids.
-    """
-
-_SOURCE_CLAIMS_DESCRIPTION = """\
-    {_DESCRIPTION_BASE} This config connects the claims to the evidence and doc ids.
-    """
-
-_BIGBIO_PAIRS_RATIONALE_DESCRIPTION = """\
-    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("rationale", "not_rationale") indicating if the span is evidence (can be supporting or refuting) for the claim. This roughly corresponds to the second task outlined in Section 5 of the paper."
-    """
-
-_BIGBIO_PAIRS_LABELPREDICTION_DESCRIPTION = """\
-    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("SUPPORT", "NOINFO", "CONTRADICT") indicating if the span supports, provides no info, or contradicts the claim. This roughly corresponds to the thrid task outlined in Section 5 of the paper.
-    """
-
-_DESCRIPTION = {
-    "scifact_corpus_source": _SOURCE_CORPUS_DESCRIPTION,
-    "scifact_claims_source": _SOURCE_CLAIMS_DESCRIPTION,
-    "scifact_rationale_bigbio_pairs": _BIGBIO_PAIRS_RATIONALE_DESCRIPTION,
-    "scifact_labelprediction_bigbio_pairs": _BIGBIO_PAIRS_LABELPREDICTION_DESCRIPTION,
-}
-
-_HOMEPAGE = "https://scifact.apps.allenai.org/"
-
-
-_LICENSE = 'Creative Commons Attribution Non Commercial 2.0 Generic'
-
-_URLS = {
-    _DATASETNAME: "https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz",
-}
-
-_SUPPORTED_TASKS = [Tasks.TEXT_PAIRS_CLASSIFICATION]
-
-_SOURCE_VERSION = "1.0.0"
-
-_BIGBIO_VERSION = "1.0.0"
-
-
-class SciFact(datasets.GeneratorBasedBuilder):
-    """
-    SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
-    """
-
-    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
-
-    BUILDER_CONFIGS = [
-        BigBioConfig(
-            name="scifact_corpus_source",
-            version=SOURCE_VERSION,
-            description="scifact source schema for the corpus config",
-            schema="source",
-            subset_id="scifact_corpus_source",
-        ),
-        BigBioConfig(
-            name="scifact_claims_source",
-            version=SOURCE_VERSION,
-            description="scifact source schema for the claims config",
-            schema="source",
-            subset_id="scifact_claims_source",
-        ),
-        BigBioConfig(
-            name="scifact_rationale_bigbio_pairs",
-            version=BIGBIO_VERSION,
-            description="scifact BigBio text pairs classification schema for rationale task",
-            schema="bigbio_pairs",
-            subset_id="scifact_rationale_pairs",
-        ),
-        BigBioConfig(
-            name="scifact_labelprediction_bigbio_pairs",
-            version=BIGBIO_VERSION,
-            description="scifact BigBio text pairs classification schema for label prediction task",
-            schema="bigbio_pairs",
-            subset_id="scifact_labelprediction_pairs",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "scifact_claims_source"
-
-    def _info(self) -> datasets.DatasetInfo:
-
-        if self.config.schema == "source":
-            # modified from
-            # https://huggingface.co/datasets/scifact/blob/main/scifact.py#L50
-
-            if self.config.name == "scifact_corpus_source":
-                features = datasets.Features(
-                    {
-                        "doc_id": Value("int32"),      # The document's S2ORC ID.
-                        "title": Value("string"),      # The title.
-                        "abstract": [Value("string")], # The abstract, written as a list of sentences.
-                        "structured": Value("bool"),   # Indicator for whether this is a structured abstract.
-                    }
-                )
-
-            elif self.config.name == "scifact_claims_source":
-                features = datasets.Features(
-                    {
-                        "id": Value("int32"),  # An integer claim ID.
-                        "claim": Value("string"),  # The text of the claim.
-                        "evidences": [
-                            {
-                                "doc_id": Value("int32"),         # source doc_id for evidence
-                                "sentence_ids": [Value("int32")], # sentence ids from doc_id
-                                "label": Value("string"),         # SUPPORT or CONTRADICT
-                            },
-                        ],
-                        "cited_doc_ids": [Value("int32")],   # The claim's "cited documents".
-                    }
-                )
-
-            else:
-                raise NotImplementedError(
-                    f"{self.config.name} config not implemented"
-                )
-
-        elif self.config.schema == "bigbio_pairs":
-            features = pairs_features
-
-        else:
-            raise NotImplementedError(f"{self.config.schema} schema not implemented")
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION[self.config.name],
-            features=features,
-            homepage=_HOMEPAGE,
-            license=str(_LICENSE),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
-        urls = _URLS[_DATASETNAME]
-        self.config.data_dir = dl_manager.download_and_extract(urls)
-
-        if self.config.name == "scifact_corpus_source":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepath": os.path.join(
-                            self.config.data_dir, "data", "corpus.jsonl"
-                        ),
-                        "split": "train",
-                    },
-                ),
-            ]
-
-        # the test split is only returned in source schema
-        # this is b/c it only has claims with no cited docs or evidence
-        # the bigbio implementation of this dataset requires
-        # cited docs or evidence to construct samples
-        elif self.config.name == "scifact_claims_source":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepath": os.path.join(
-                            self.config.data_dir, "data", "claims_train.jsonl"
-                        ),
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "filepath": os.path.join(
-                            self.config.data_dir, "data", "claims_dev.jsonl"
-                        ),
-                        "split": "dev",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={
-                        "filepath": os.path.join(
-                            self.config.data_dir, "data", "claims_test.jsonl"
-                        ),
-                        "split": "test",
-                    },
-                ),
-            ]
-
-        elif self.config.name in [
-            "scifact_rationale_bigbio_pairs",
-            "scifact_labelprediction_bigbio_pairs",
-        ]:
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepath": os.path.join(
-                            self.config.data_dir, "data", "claims_train.jsonl"
-                        ),
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "filepath": os.path.join(
-                            self.config.data_dir, "data", "claims_dev.jsonl"
-                        ),
-                        "split": "dev",
-                    },
-                ),
-            ]
-
-
-    def _source_generate_examples(self, filepath, split) -> Tuple[str, Dict[str, str]]:
-
-        # here we just read corpus.jsonl and return the abstracts
-        if self.config.name == "scifact_corpus_source":
-            with open(filepath) as fp:
-                for id_, row in enumerate(fp.readlines()):
-                    data = json.loads(row)
-                    yield id_, {
-                        "doc_id": int(data["doc_id"]),
-                        "title": data["title"],
-                        "abstract": data["abstract"],
-                        "structured": data["structured"],
-                    }
-
-        # here we are reading one of claims_(train|dev|test).jsonl
-        elif self.config.name == "scifact_claims_source":
-
-            # claims_test.jsonl only has "id" and "claim" keys
-            # claims_train.jsonl and claims_dev.jsonl sometimes have evidence
-            with open(filepath) as fp:
-                for id_, row in enumerate(fp.readlines()):
-                    data = json.loads(row)
-                    evidences_dict = data.get("evidence", {})
-                    evidences_list = []
-                    for doc_id, sent_lbl_list in evidences_dict.items():
-                        for sent_lbl_dict in sent_lbl_list:
-                            evidence = {
-                                "doc_id": doc_id,
-                                "sentence_ids": sent_lbl_dict["sentences"],
-                                "label": sent_lbl_dict["label"],
-                            }
-                            evidences_list.append(evidence)
-
-                    yield id_, {
-                        "id": data["id"],
-                        "claim": data["claim"],
-                        "evidences": evidences_list,
-                        "cited_doc_ids": data.get("cited_doc_ids", []),
-                    }
-
-
-    def _bigbio_generate_examples(self, filepath, split) -> Tuple[str, Dict[str, str]]:
-        """
-        Here we always create one sample per sentence group.
-        Any sentence group in an evidence attribute will have
-        a label in {"rationale"} for the rationale task or
-        in {"SUPPORT", "CONTRADICT"} for the labelprediction task.
-        All other sentences will have either a "not_rationale"
-        label or a "NOINFO" label depending on the task.
-        """
-
-        # read corpus (one row per abstract)
-        corpus_file_path = os.path.join(self.config.data_dir, "data", "corpus.jsonl")
-        df_corpus = pd.read_json(corpus_file_path, lines=True)
-
-        # create one row per sentence and create sentence index
-        df_sents = df_corpus.explode('abstract')
-        df_sents = df_sents.rename(columns={"abstract": "sentence"})
-        df_sents['sent_num'] = df_sents.groupby('doc_id').transform('cumcount')
-        df_sents['doc_sent_id'] = df_sents.apply(lambda x: f"{x['doc_id']}-{x['sent_num']}", axis=1)
-
-        # read claims
-        df_claims = pd.read_json(filepath, lines=True)
-
-
-        # join claims to corpus
-        for _, claim_row in df_claims.iterrows():
-
-            evidence = claim_row['evidence']
-            cited_doc_ids = set(claim_row['cited_doc_ids'])
-            evidence_doc_ids = set([int(doc_id) for doc_id in evidence.keys()])
-
-            # assert all evidence doc IDs are in cited_doc_ids
-            assert len(evidence_doc_ids - cited_doc_ids) == 0
-
-            # this will have all abstract sentences from cited docs
-            df_claim_sents = df_sents[df_sents['doc_id'].isin(cited_doc_ids)]
-
-            # create all sentence samples as NOINFO then fix
-            noinfo_samples = {}
-            for _, row in df_claim_sents.iterrows():
-                sample = {
-                    "claim": claim_row["claim"],
-                    "claim_id": claim_row["id"],
-                    "doc_id": row['doc_id'],
-                    "sentence_ids": (row['sent_num'],),
-                    "doc_sent_ids": (row['doc_sent_id'],),
-                    "span": row['sentence'].strip(),
-                    "label": "NOINFO",
-                }
-                noinfo_samples[sample["doc_sent_ids"]] = sample
-
-            # create evidence samples and remove from noinfo samples as we go
-            evidence_samples = []
-            for doc_id_str, sent_lbl_list in evidence.items():
-                doc_id = int(doc_id_str)
-
-                for sent_lbl_dict in sent_lbl_list:
-                    sent_ids = sent_lbl_dict['sentences']
-                    doc_sent_ids = [f"{doc_id}-{sent_id}" for sent_id in sent_ids]
-                    df_evi = df_claim_sents[df_claim_sents['doc_sent_id'].isin(doc_sent_ids)]
-
-                    sample = {
-                        "claim": claim_row["claim"],
-                        "claim_id": claim_row["id"],
-                        "doc_id": doc_id,
-                        "sentence_ids": tuple(sent_ids),
-                        "doc_sent_ids": tuple(doc_sent_ids),
-                        "span": " ".join([el.strip() for el in df_evi["sentence"].values]),
-                        "label": sent_lbl_dict["label"],
-                    }
-                    evidence_samples.append(sample)
-                    for doc_sent_id in doc_sent_ids:
-                        del noinfo_samples[(doc_sent_id,)]
-
-            # combine all sample and put back in sentence order
-            all_samples = evidence_samples + list(noinfo_samples.values())
-            all_samples = sorted(all_samples, key=lambda x: (x['doc_id'], x['sentence_ids'][0]))
-
-            # add a unique ID
-            for _id, sample in enumerate(all_samples):
-                sample["id"] = f"{_id}-{sample['claim_id']}-{sample['doc_id']}-{sample['sentence_ids'][0]}"
-
-            RATIONALE_LABEL_MAP = {
-                "SUPPORT": "rationale",
-                "CONTRADICT": "rationale",
-                "NOINFO": "not_rationale",
-            }
-
-            if self.config.name == "scifact_rationale_bigbio_pairs":
-                for sample in all_samples:
-                    yield sample['id'], {
-                        "id": sample["id"],
-                        "document_id": sample["doc_id"],
-                        "text_1": sample["claim"],
-                        "text_2": sample["span"],
-                        "label": RATIONALE_LABEL_MAP[sample['label']],
-                    }
-
-            elif self.config.name == "scifact_labelprediction_bigbio_pairs":
-                for sample in all_samples:
-                    yield sample['id'], {
-                        "id": sample["id"],
-                        "document_id": sample["doc_id"],
-                        "text_1": sample["claim"],
-                        "text_2": sample["span"],
-                        "label": sample['label'],
-                    }
-
-    def _generate_examples(self, filepath, split) -> Tuple[int, dict]:
-
-        if "source" in self.config.name:
-            for sample in self._source_generate_examples(filepath, split):
-                yield sample
-
-        elif "bigbio" in self.config.name:
-            for sample in self._bigbio_generate_examples(filepath, split):
-                yield sample

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