Update parquet files

.gitattributes

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

bigbiohub.py

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

evidence-inference_bigbio_qa/evidence_inference-test.parquet

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evidence-inference_bigbio_qa/evidence_inference-train.parquet

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evidence-inference_source/evidence_inference-validation.parquet

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evidence_inference.py

@@ -1,293 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-The dataset consists of biomedical articles describing randomized control trials (RCTs)
-that compare multiple treatments. Each of these articles will have multiple questions,
-or 'prompts' associated with them. These prompts will ask about the relationship between
-an intervention and comparator with respect to an outcome, as reported in the trial.
-For example, a prompt may ask about the reported effects of aspirin as compared to placebo
-on the duration of headaches.
-For the sake of this task, we assume that a particular article will report that the intervention of interest either
-significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator.
-"""
-
-import os
-from typing import Dict, List, Tuple
-
-import datasets
-import pandas as pd
-
-from .bigbiohub import qa_features
-from .bigbiohub import BigBioConfig
-from .bigbiohub import Tasks
-
-_LANGUAGES = ['English']
-_PUBMED = True
-_LOCAL = False
-_CITATION = """\
-@inproceedings{deyoung-etal-2020-evidence,
-    title = "Evidence Inference 2.0: More Data, Better Models",
-    author = "DeYoung, Jay  and
-      Lehman, Eric  and
-      Nye, Benjamin  and
-      Marshall, Iain  and
-      Wallace, Byron C.",
-    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
-    month = jul,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.bionlp-1.13",
-    pages = "123--132",
-}
-"""
-
-_DATASETNAME = "evidence_inference"
-_DISPLAYNAME = "Evidence Inference 2.0"
-
-_DESCRIPTION = """\
-The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple
-treatments. Each of these articles will have multiple questions, or 'prompts' associated with them.
-These prompts will ask about the relationship between an intervention and comparator with respect to an outcome,
-as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared
-to placebo on the duration of headaches. For the sake of this task, we assume that a particular article
-will report that the intervention of interest either significantly increased, significantly decreased
-or had significant effect on the outcome, relative to the comparator.
-"""
-
-_HOMEPAGE = "https://github.com/jayded/evidence-inference"
-
-_LICENSE = 'MIT License'
-
-_URLS = {
-    _DATASETNAME: "http://evidence-inference.ebm-nlp.com/v2.0.tar.gz",
-}
-
-_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
-
-_SOURCE_VERSION = "2.0.0"
-
-_BIGBIO_VERSION = "1.0.0"
-
-QA_CHOICES = [
-    "significantly increased",
-    "no significant difference",
-    "significantly decreased",
-]
-
-# Some examples are removed due to comments on the dataset's github page
-# https://github.com/jayded/evidence-inference/blob/master/annotations/README.md#caveat
-
-INCORRECT_PROMPT_IDS = set([
-    911, 912, 1262, 1261, 3044, 3248, 3111, 3620, 4308, 4490, 4491, 4324,
-    4325, 4492, 4824, 5000, 5001, 5002, 5046, 5047, 4948, 5639, 5710, 5752,
-    5775, 5782, 5841, 5843, 5861, 5862, 5863, 5964, 5965, 5966, 5975, 4807,
-    5776, 5777, 5778, 5779, 5780, 5781, 6034, 6065, 6066, 6666, 6667, 6668,
-    6669, 7040, 7042, 7944, 8590, 8605, 8606, 8639, 8640, 8745, 8747, 8749,
-    8877, 8878, 8593, 8631, 8635, 8884, 8886, 8773, 10032, 10035, 8876, 8875,
-    8885, 8917, 8921, 8118, 10885, 10886, 10887, 10888, 10889, 10890
-])
-
-QUESTIONABLE_PROMPT_IDS = set([
-    7811, 7812, 7813, 7814, 7815, 8197, 8198, 8199,
-    8200, 8201, 9429, 9430, 9431, 8536, 9432
-])
-
-SOMEWHAT_MALFORMED_PROMPT_IDS = set([
-    3514, 346, 5037, 4715, 8767, 9295, 9297, 8870, 9862
-])
-
-SKIP_PROMPT_IDS = INCORRECT_PROMPT_IDS | QUESTIONABLE_PROMPT_IDS | SOMEWHAT_MALFORMED_PROMPT_IDS
-
-
-class EvidenceInferenceDataset(datasets.GeneratorBasedBuilder):
-    f"""{_DESCRIPTION}"""
-
-    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
-
-    BUILDER_CONFIGS = [
-        BigBioConfig(
-            name="evidence-inference_source",
-            version=SOURCE_VERSION,
-            description="evidence-inference source schema",
-            schema="source",
-            subset_id="evidence-inference",
-        ),
-        BigBioConfig(
-            name="evidence-inference_bigbio_qa",
-            version=BIGBIO_VERSION,
-            description="evidence-inference BigBio schema",
-            schema="bigbio_qa",
-            subset_id="evidence-inference",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "evidence-inference_source"
-
-    def _info(self) -> datasets.DatasetInfo:
-        if self.config.schema == "source":
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("int64"),
-                    "prompt_id": datasets.Value("int64"),
-                    "pmcid": datasets.Value("int64"),
-                    "label": datasets.Value("string"),
-                    "evidence": datasets.Value("string"),
-                    "intervention": datasets.Value("string"),
-                    "comparator": datasets.Value("string"),
-                    "outcome": datasets.Value("string"),
-                }
-            )
-
-        elif self.config.schema == "bigbio_qa":
-            features = qa_features
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=str(_LICENSE),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
-        """Returns SplitGenerators."""
-
-        urls = _URLS[_DATASETNAME]
-        data_dir = dl_manager.download_and_extract(urls)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": [
-                        os.path.join(data_dir, "annotations_merged.csv"),
-                        os.path.join(data_dir, "prompts_merged.csv"),
-                    ],
-                    "datapath": os.path.join(data_dir, "txt_files"),
-                    "split": "train",
-                    "datadir": data_dir,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepaths": [
-                        os.path.join(data_dir, "annotations_merged.csv"),
-                        os.path.join(data_dir, "prompts_merged.csv"),
-                    ],
-                    "datapath": os.path.join(data_dir, "txt_files"),
-                    "split": "validation",
-                    "datadir": data_dir,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepaths": [
-                        os.path.join(data_dir, "annotations_merged.csv"),
-                        os.path.join(data_dir, "prompts_merged.csv"),
-                    ],
-                    "datapath": os.path.join(data_dir, "txt_files"),
-                    "split": "test",
-                    "datadir": data_dir,
-                },
-            ),
-        ]
-
-    def _generate_examples(
-        self, filepaths, datapath, split, datadir
-    ) -> Tuple[int, Dict]:
-        """Yields examples as (key, example) tuples."""
-        with open(f"{datadir}/splits/{split}_article_ids.txt", "r") as f:
-            ids = [int(i.strip()) for i in f.readlines()]
-        prompts = pd.read_csv(filepaths[-1], encoding="utf8")
-        prompts = prompts[prompts["PMCID"].isin(ids)]
-
-        annotations = pd.read_csv(filepaths[0], encoding="utf8").set_index("PromptID")
-        evidences = pd.read_csv(filepaths[0], encoding="utf8").set_index("PMCID")
-        evidences = evidences[evidences["Evidence Start"] != -1]
-        uid = 0
-
-        def lookup(df: pd.DataFrame, id, col) -> str:
-            try:
-                label = df.loc[id][col]
-                if isinstance(label, pd.Series):
-                    return label.values[0]
-                else:
-                    return label
-            except KeyError:
-                return -1
-
-        def extract_evidence(doc_id, start, end):
-            p = f"{datapath}/PMC{doc_id}.txt"
-            with open(p, "r") as f:
-                return f.read()[start:end]
-
-
-        for key, sample in prompts.iterrows():
-
-            pid = sample["PromptID"]
-            pmcid = sample["PMCID"]
-            label = lookup(annotations, pid, "Label")
-            start = lookup(evidences, pmcid, "Evidence Start")
-            end = lookup(evidences, pmcid, "Evidence End")
-
-            if pid in SKIP_PROMPT_IDS:
-                continue
-
-            if label == -1:
-                continue
-
-            evidence = extract_evidence(pmcid, start, end)
-
-            if self.config.schema == "source":
-
-                feature_dict = {
-                    "id": uid,
-                    "pmcid": pmcid,
-                    "prompt_id": pid,
-                    "intervention": sample["Intervention"],
-                    "comparator": sample["Comparator"],
-                    "outcome": sample["Outcome"],
-                    "evidence": evidence,
-                    "label": label,
-                }
-
-                uid += 1
-                yield key, feature_dict
-
-            elif self.config.schema == "bigbio_qa":
-
-                context = evidence
-                question = (
-                    f"Compared to {sample['Comparator']} "
-                    f"what was the result of {sample['Intervention']} on {sample['Outcome']}?"
-                )
-                feature_dict = {
-                    "id": uid,
-                    "question_id": pid,
-                    "document_id": pmcid,
-                    "question": question,
-                    "type": "multiple_choice",
-                    "choices": QA_CHOICES,
-                    "context": context,
-                    "answer": [label],
-                }
-
-                uid += 1
-                yield key, feature_dict