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
-*.ftz 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
-*.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
-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
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-*.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
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-*.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

LICENSE

@@ -1,26 +0,0 @@
-name: National Library of Medicine Terms and Conditions
-short_name: NLM_LICENSE
-
-
-National Library of Medicine Terms and Conditions
-
-INTRODUCTION
-
-Downloading data from the National Library of Medicine FTP servers indicates your acceptance of the following Terms and Conditions: No charges, usage fees or royalties are paid to NLM for this data.
-
-GENERAL TERMS AND CONDITIONS
-
-    Users of the data agree to:
-        acknowledge NLM as the source of the data by including the phrase "Courtesy of the U.S. National Library of Medicine" in a clear and conspicuous manner,
-        properly use registration and/or trademark symbols when referring to NLM products, and
-        not indicate or imply that NLM has endorsed its products/services/applications.
-
-    Users who republish or redistribute the data (services, products or raw data) agree to:
-        maintain the most current version of all distributed data, or
-        make known in a clear and conspicuous manner that the products/services/applications do not reflect the most current/accurate data available from NLM.
-
-    These data are produced with a reasonable standard of care, but NLM makes no warranties express or implied, including no warranty of merchantability or fitness for particular purpose, regarding the accuracy or completeness of the data. Users agree to hold NLM and the U.S. Government harmless from any liability resulting from errors in the data. NLM disclaims any liability for any consequences due to use, misuse, or interpretation of information contained or not contained in the data.
-
-    NLM does not provide legal advice regarding copyright, fair use, or other aspects of intellectual property rights. See the NLM Copyright page.
-
-    NLM reserves the right to change the type and format of its machine-readable data. NLM will take reasonable steps to inform users of any changes to the format of the data before the data are distributed via the announcement section or subscription to email and RSS updates.

README.md

@@ -1,68 +0,0 @@
----
-language: en
-license: other
-multilinguality: monolingual
-pretty_name: BLURB
----
-
-
-# Dataset Card for BLURB
-
-## Dataset Description
-
-- **Homepage:** https://microsoft.github.io/BLURB/tasks.html
-- **Pubmed:** True
-- **Public:** True
-- **Tasks:** Named Entity Recognition
-
-BLURB is a collection of resources for biomedical natural language processing. 
-In general domains, such as newswire and the Web, comprehensive benchmarks and 
-leaderboards such as GLUE have greatly accelerated progress in open-domain NLP. 
-In biomedicine, however, such resources are ostensibly scarce. In the past, 
-there have been a plethora of shared tasks in biomedical NLP, such as 
-BioCreative, BioNLP Shared Tasks, SemEval, and BioASQ, to name just a few. These 
-efforts have played a significant role in fueling interest and progress by the 
-research community, but they typically focus on individual tasks. The advent of 
-neural language models, such as BERT provides a unifying foundation to leverage 
-transfer learning from unlabeled text to support a wide range of NLP 
-applications. To accelerate progress in biomedical pretraining strategies and 
-task-specific methods, it is thus imperative to create a broad-coverage 
-benchmark encompassing diverse biomedical tasks. 
-
-Inspired by prior efforts toward this direction (e.g., BLUE), we have created 
-BLURB (short for Biomedical Language Understanding and Reasoning Benchmark). 
-BLURB comprises of a comprehensive benchmark for PubMed-based biomedical NLP 
-applications, as well as a leaderboard for tracking progress by the community. 
-BLURB includes thirteen publicly available datasets in six diverse tasks. To 
-avoid placing undue emphasis on tasks with many available datasets, such as 
-named entity recognition (NER), BLURB reports the macro average across all tasks 
-as the main score. The BLURB leaderboard is model-agnostic. Any system capable 
-of producing the test predictions using the same training and development data 
-can participate. The main goal of BLURB is to lower the entry barrier in 
-biomedical NLP and help accelerate progress in this vitally important field for 
-positive societal and human impact.
-
-This implementation contains a subset of 5 tasks as of 2022.10.06, with their original train, dev, and test splits.
-
-
-## Citation Information
-
-```
-@article{gu2021domain,
-    title = {
-        Domain-specific language model pretraining for biomedical natural
-        language processing
-    },
-    author = {
-        Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and
-        Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao,
-        Jianfeng and Poon, Hoifung
-    },
-    year = 2021,
-    journal = {ACM Transactions on Computing for Healthcare (HEALTH)},
-    publisher = {ACM New York, NY},
-    volume = 3,
-    number = 1,
-    pages = {1--23}
-}
-```

bc2gm/blurb-test.parquet

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bigbiohub.py

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-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

blurb.py

@@ -1,349 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-BLURB is a collection of resources for biomedical natural language processing. 
-In general domains, such as newswire and the Web, comprehensive benchmarks and 
-leaderboards such as GLUE have greatly accelerated progress in open-domain NLP. 
-In biomedicine, however, such resources are ostensibly scarce. In the past, 
-there have been a plethora of shared tasks in biomedical NLP, such as 
-BioCreative, BioNLP Shared Tasks, SemEval, and BioASQ, to name just a few. These 
-efforts have played a significant role in fueling interest and progress by the 
-research community, but they typically focus on individual tasks. The advent of 
-neural language models, such as BERT provides a unifying foundation to leverage 
-transfer learning from unlabeled text to support a wide range of NLP 
-applications. To accelerate progress in biomedical pretraining strategies and 
-task-specific methods, it is thus imperative to create a broad-coverage 
-benchmark encompassing diverse biomedical tasks. 
-
-Inspired by prior efforts toward this direction (e.g., BLUE), we have created 
-BLURB (short for Biomedical Language Understanding and Reasoning Benchmark). 
-BLURB comprises of a comprehensive benchmark for PubMed-based biomedical NLP 
-applications, as well as a leaderboard for tracking progress by the community. 
-BLURB includes thirteen publicly available datasets in six diverse tasks. To 
-avoid placing undue emphasis on tasks with many available datasets, such as 
-named entity recognition (NER), BLURB reports the macro average across all tasks 
-as the main score. The BLURB leaderboard is model-agnostic. Any system capable 
-of producing the test predictions using the same training and development data 
-can participate. The main goal of BLURB is to lower the entry barrier in 
-biomedical NLP and help accelerate progress in this vitally important field for 
-positive societal and human impact."""
-
-import re
-import pandas
-import datasets
-
-from .bigbiohub import BigBioConfig
-from .bigbiohub import Tasks
-
-_DATASETNAME = "blurb"
-_DISPLAYNAME = "BLURB"
-
-_LANGUAGES = ["English"]
-_PUBMED = True
-_LOCAL = False
-_CITATION = """\
-@article{gu2021domain,
-    title = {
-        Domain-specific language model pretraining for biomedical natural
-        language processing
-    },
-    author = {
-        Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and
-        Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao,
-        Jianfeng and Poon, Hoifung
-    },
-    year = 2021,
-    journal = {ACM Transactions on Computing for Healthcare (HEALTH)},
-    publisher = {ACM New York, NY},
-    volume = 3,
-    number = 1,
-    pages = {1--23}
-}
-"""
-
-
-_BC2GM_DESCRIPTION = """\
-The BioCreative II Gene Mention task. The training corpus for the current task \
-consists mainly of the training and testing corpora (text collections) from the \
-BCI task, and the testing corpus for the current task consists of an additional \
-5,000 sentences that were held 'in reserve' from the previous task. In the \
-current corpus, tokenization is not provided; instead participants are asked to \
-identify a gene mention in a sentence by giving its start and end characters. As \
-before, the training set consists of a set of sentences, and for each sentence a \
-set of gene mentions (GENE annotations).
-
-- Homepage: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-ii/task-1a-gene-mention-tagging/
-- Repository: https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/
-- Paper: Overview of BioCreative II gene mention recognition
-         https://link.springer.com/article/10.1186/gb-2008-9-s2-s2
-"""
-
-_BC5_CHEM_DESCRIPTION = """\
-The corpus consists of three separate sets of articles with diseases, chemicals \
-and their relations annotated. The training (500 articles) and development (500 \
-articles) sets were released to task participants in advance to support \
-text-mining method development. The test set (500 articles) was used for final \
-system performance evaluation.
-
-- Homepage: https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus
-- Repository: https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/
-- Paper: BioCreative V CDR task corpus: a resource for chemical disease relation extraction
-         https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/
-"""
-
-_BC5_DISEASE_DESCRIPTION = """\
-The corpus consists of three separate sets of articles with diseases, chemicals \
-and their relations annotated. The training (500 articles) and development (500 \
-articles) sets were released to task participants in advance to support \
-text-mining method development. The test set (500 articles) was used for final \
-system performance evaluation.
-
-- Homepage: https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus
-- Repository: https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/
-- Paper: BioCreative V CDR task corpus: a resource for chemical disease relation extraction
-         https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/
-"""
-
-_JNLPBA_DESCRIPTION = """\
-The BioNLP / JNLPBA Shared Task 2004 involves the identification and classification \
-of technical terms referring to concepts of interest to biologists in the domain of \
-molecular biology. The task was organized by GENIA Project based on the annotations \
-of the GENIA Term corpus (version 3.02).
-
-- Homepage: http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004
-- Repository: https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/
-- Paper: Introduction to the Bio-entity Recognition Task at JNLPBA
-         https://aclanthology.org/W04-1213
-"""
-
-_NCBI_DISEASE_DESCRIPTION = """\
-[T]he NCBI disease corpus contains 6,892 disease mentions, which are mapped to \
-790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the \
-rest contain an OMIM identifier. We were able to link 91% of the mentions to a \
-single disease concept, while the rest are described as a combination of \
-concepts.
-
-- Homepage: https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/
-- Repository: https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/
-- Paper: NCBI disease corpus: a resource for disease name recognition and concept normalization
-         https://pubmed.ncbi.nlm.nih.gov/24393765/
-"""
-
-_EBM_PICO_DESCRIPTION = """"""
-
-_CHEMPROT_DESCRIPTION = """"""
-_DDI_DESCRIPTION = """"""
-_GAD_DESCRIPTION = """"""
-
-_BIOSSES_DESCRIPTION = """"""
-
-_HOC_DESCRIPTION = """"""
-
-_PUBMEDQA_DESCRIPTION = """"""
-_BIOASQ_DESCRIPTION = """"""
-
-_DESCRIPTION = {
-    "bc2gm": _BC2GM_DESCRIPTION,
-    "bc5disease": _BC5_DISEASE_DESCRIPTION,
-    "bc5chem": _BC5_CHEM_DESCRIPTION,
-    "jnlpba": _JNLPBA_DESCRIPTION,
-    "ncbi_disease": _NCBI_DISEASE_DESCRIPTION,
-}
-
-_HOMEPAGE = "https://microsoft.github.io/BLURB/tasks.html"
-
-_LICENSE = "MIXED"
-
-
-_URLs = {
-    "bc2gm": [
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/BC2GM-IOB/train.tsv",
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/BC2GM-IOB/devel.tsv",
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/BC2GM-IOB/test.tsv",
-    ],
-    "bc5disease": [
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/BC5CDR-disease-IOB/train.tsv",
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/BC5CDR-disease-IOB/devel.tsv",
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/BC5CDR-disease-IOB/test.tsv",
-    ],
-    "bc5chem": [
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/BC5CDR-chem-IOB/train.tsv",
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/BC5CDR-chem-IOB/devel.tsv",
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/BC5CDR-chem-IOB/test.tsv",
-    ],
-    "jnlpba": [
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/JNLPBA/train.tsv",
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/JNLPBA/devel.tsv",
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/JNLPBA/test.tsv",
-    ],
-    "ncbi_disease": [
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/NCBI-disease-IOB/train.tsv",
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/NCBI-disease-IOB/devel.tsv",
-        "https://raw.githubusercontent.com/cambridgeltl/MTL-Bioinformatics-2016/master/data/NCBI-disease-IOB/test.tsv",
-    ],
-}
-
-_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
-_SOURCE_VERSION = "1.0.0"
-_BIGBIO_VERSION = "1.0.0"
-
-
-class BlurbDataset(datasets.GeneratorBasedBuilder):
-    """Source splits for BLURB data (train/val/test) for easy access."""
-
-    DEFAULT_CONFIG_NAME = "bc5chem"
-    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
-
-    BUILDER_CONFIGS = [
-        BigBioConfig(
-            name="bc5chem",
-            version=SOURCE_VERSION,
-            description="BC5CDR Chemical IO Tagging",
-            schema="ner",
-            subset_id="bc5chem",
-        ),
-        BigBioConfig(
-            name="bc5disease",
-            version=SOURCE_VERSION,
-            description="BC5CDR Chemical IO Tagging",
-            schema="ner",
-            subset_id="bc5disease",
-        ),
-        BigBioConfig(
-            name="bc2gm",
-            version=SOURCE_VERSION,
-            description="BC2 Gene IO Tagging",
-            schema="ner",
-            subset_id="bc2gm",
-        ),
-        BigBioConfig(
-            name="jnlpba",
-            version=SOURCE_VERSION,
-            description="JNLPBA Protein, DNA, RNA, Cell Type, Cell Line IO Tagging",
-            schema="ner",
-            subset_id="jnlpba",
-        ),
-        BigBioConfig(
-            name="ncbi_disease",
-            version=SOURCE_VERSION,
-            description="NCBI Disease IO Tagging",
-            schema="ner",
-            subset_id="ncbi_disease",
-        ),
-    ]
-
-    def _info(self):
-
-        ner_features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "type": datasets.Value("string"),
-                "ner_tags": datasets.Sequence(
-                    datasets.features.ClassLabel(
-                        names=[
-                            "O",
-                            "B",
-                            "I",
-                        ]
-                    )
-                ),
-            }
-        )
-        if self.config.schema == "ner":
-            return datasets.DatasetInfo(
-                description=_DESCRIPTION[self.config.name],
-                features=ner_features,
-                supervised_keys=None,
-                homepage=_HOMEPAGE,
-                license=str(_LICENSE),
-                citation=_CITATION,
-            )
-
-    def _split_generators(self, dl_manager):
-
-        my_urls = _URLs[self.config.name]
-        dl_dir = dl_manager.download_and_extract(my_urls)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": dl_dir[0],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": dl_dir[1],
-                    "split": "validation",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": dl_dir[2],
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _load_iob(self, fpath):
-        """
-        Assumes input CoNLL file is a single entity type.
-        """
-        with open(fpath, "r") as file:
-            tagged = []
-            for line in file:
-                if line.strip() == "":
-                    toks, tags = zip(*tagged)
-                    # transform tags
-                    tags = tags = [t[0] for t in tags]
-                    yield (toks, tags)
-                    tagged = []
-                    continue
-                tagged.append(re.split("\s", line.strip()))
-
-            if tagged:
-                toks, tags = zip(*tagged)
-                tags = [t[0] for t in tags]
-                yield (toks, tags)
-
-    def _generate_examples(self, filepath, split):
-
-        if self.config.schema == "ner":
-
-            # Types for each NER dataset. Note BLURB's JNLPBA collapses all mentions into a
-            # single entity type, which creates some ambiguity for prompting based on type
-            ner_types = {
-                "bc2gm": "gene",
-                "bc5chem": "chemical",
-                "bc5disease": "disease",
-                "jnlpba": "protein, DNA, RNA, cell line, or cell type",
-                "ncbi_disease": "disease",
-            }
-
-            uid = 0
-            for item in self._load_iob(filepath):
-                toks, tags = item
-                yield uid, {
-                    "id": uid,
-                    "tokens": toks,
-                    "type": ner_types[self.config.name],
-                    "ner_tags": tags,
-                }
-                uid += 1

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