adds modified blurb files

LICENSE

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+name: National Library of Medicine Terms and Conditions
+short_name: NLM_LICENSE
+
+
+National Library of Medicine Terms and Conditions
+
+INTRODUCTION
+
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+
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+
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README.md

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+---
+language: en
+license: other
+multilinguality: monolingual
+pretty_name: BioASQ Task B
+---
+
+
+# Dataset Card for BioASQ Task B
+
+## Dataset Description
+
+- **Homepage:** http://participants-area.bioasq.org/datasets/
+- **Pubmed:** True
+- **Public:** False
+- **Tasks:** Question Answering
+
+
+The BioASQ corpus contains multiple question
+answering tasks annotated by biomedical experts, including yes/no, factoid, list,
+and summary questions. Pertaining to our objective of comparing neural language
+models, we focus on the the yes/no questions (Task 7b), and leave the inclusion
+of other tasks to future work. Each question is paired with a reference text
+containing multiple sentences from a PubMed abstract and a yes/no answer. We use
+the official train/dev/test split of 670/75/140 questions.
+
+See 'Domain-Specific Language Model Pretraining for Biomedical
+Natural Language Processing'
+
+
+## Citation Information
+
+```
+@article{tsatsaronis2015overview,
+	title        = {
+		An overview of the BIOASQ large-scale biomedical semantic indexing and
+		question answering competition
+	},
+	author       = {
+		Tsatsaronis, George and Balikas, Georgios and Malakasiotis, Prodromos
+        and Partalas, Ioannis and Zschunke, Matthias and Alvers, Michael R and
+		Weissenborn, Dirk and Krithara, Anastasia and Petridis, Sergios and
+		Polychronopoulos, Dimitris and others
+	},
+	year         = 2015,
+	journal      = {BMC bioinformatics},
+	publisher    = {BioMed Central Ltd},
+	volume       = 16,
+	number       = 1,
+	pages        = 138
+}
+```

bigbiohub.py

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+from dataclasses import dataclass
+from enum import Enum
+import datasets
+from types import SimpleNamespace
+
+
+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"),
+                    }
+                ],
+            }
+        ],
+    }
+)

blurb.py

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+# 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
+
+#from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_DATASETNAME = "blurb"
+_DISPLAYNAME = "BLURB"
+
+_LANGUAGES = [Lang.EN]
+_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"  # Licenses.GPL_3p0
+
+
+_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):
+    """BIOSSES : Biomedical Semantic Similarity Estimation System"""
+
+    DEFAULT_CONFIG_NAME = "biosses_source"
+    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")),
+                "word_idx": datasets.Value("int32"),
+                "type": datasets.Value("string"),
+                "tags": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=[
+                            "O",
+                            "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 = [t[0] if t[0] in ["I", "O"] else "I" 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):
+                if len(item) != 2:
+                    print("ERROR", len(item), item, split)
+                    continue
+                toks, tags = item
+                for word_idx in range(len(toks)):
+                    yield uid, {
+                        "id": uid,
+                        "tokens": toks,
+                        "type": ner_types[self.config.name],
+                        "tags": tags,
+                        "word_idx": word_idx,
+                    }
+                    uid += 1