From 58a85c8aaa9d4f7a9cd204df03ccbf5581f973db Mon Sep 17 00:00:00 2001 From: David Kartchner Date: Thu, 26 May 2022 23:54:43 -0600 Subject: [PATCH 01/10] first half of dataloader --- .../biodatasets/plant_phenotype/__init__.py | 0 .../plant_phenotype/plant_phenotype.py | 256 ++++++++++++++++++ 2 files changed, 256 insertions(+) create mode 100644 bigbio/biodatasets/plant_phenotype/__init__.py create mode 100644 bigbio/biodatasets/plant_phenotype/plant_phenotype.py diff --git a/bigbio/biodatasets/plant_phenotype/__init__.py b/bigbio/biodatasets/plant_phenotype/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/bigbio/biodatasets/plant_phenotype/plant_phenotype.py b/bigbio/biodatasets/plant_phenotype/plant_phenotype.py new file mode 100644 index 00000000..cbcf1bb9 --- /dev/null +++ b/bigbio/biodatasets/plant_phenotype/plant_phenotype.py @@ -0,0 +1,256 @@ +# 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. + +""" +This template serves as a starting point for contributing a dataset to the BigScience Biomedical repo. + +When modifying it for your dataset, look for TODO items that offer specific instructions. + +Full documentation on writing dataset loading scripts can be found here: +https://huggingface.co/docs/datasets/add_dataset.html + +To create a dataset loading script you will create a class and implement 3 methods: + * `_info`: Establishes the schema for the dataset, and returns a datasets.DatasetInfo object. + * `_split_generators`: Downloads and extracts data for each split (e.g. train/val/test) or associate local data with each split. + * `_generate_examples`: Creates examples from data on disk that conform to each schema defined in `_info`. + +TODO: Before submitting your script, delete this doc string and replace it with a description of your dataset. + + +[bigbio_schema_name] = (kb, pairs, qa, text, t2t, entailment) +""" + +import os +from typing import List, Tuple, Dict + +import datasets +from bigbio.utils import schemas +from bigbio.utils.configs import BigBioConfig +from bigbio.utils.constants import Tasks + +_LOCAL = False + +_CITATION = """\ +@article{cho2022plant, + author = {Cho, Hyejin and Kim, Baeksoo and Choi, Wonjun and Lee, Doheon and Lee, Hyunju}, + title = {Plant phenotype relationship corpus for biomedical relationships between plants and phenotypes}, + journal = {Scientific Data}, + volume = {9}, + year = {2022}, + publisher = {Nature Publishing Group}, + doi = {https://doi.org/10.1038/s41597-022-01350-1}, +} +""" + +_DATASETNAME = "plant_phenotype" + +# TODO: Add description of the dataset here +# You can copy an official description +_DESCRIPTION = """\ + + +Corpus: + +Annotators: + +Annotation Quality: +""" + +_HOMEPAGE = "https://github.com/DMCB-GIST/PPRcorpus" + +_LICENSE = "" + +# TODO: Add links to the urls needed to download your dataset files. +# For local datasets, this variable can be an empty dictionary. + +# For publicly available datasets you will most likely end up passing these URLs to dl_manager in _split_generators. +# In most cases the URLs will be the same for the source and bigbio config. +# However, if you need to access different files for each config you can have multiple entries in this dict. +# This can be an arbitrarily nested dict/list of URLs (see below in `_split_generators` method) +_URLS = { + _DATASETNAME: [ + "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_dev_corpus.txt", + "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_test_corpus.txt", + "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_train_corpus.txt", + ], +} + +_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] + + +_SOURCE_VERSION = "1.0.0" + +_BIGBIO_VERSION = "1.0.0" + + + +class PlantPhenotypeDataset(datasets.GeneratorBasedBuilder): + """\ + Plant-Phenotype is dataset for named-entity recognition and relation extraction of plants and their induced phenotypes + """ + + SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) + BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) + + BUILDER_CONFIGS = [ + BigBioConfig( + name="plant_phenotype_source", + version=SOURCE_VERSION, + description="Plant Phenotype source schema", + schema="source", + subset_id="plant_phenotype", + ), + BigBioConfig( + name="plant_phenotype_bigbio_kb", + version=BIGBIO_VERSION, + description="Plant Phenotype BigBio schema", + schema="bigbio_kb", + subset_id="plant_phenotype", + ), + ] + + DEFAULT_CONFIG_NAME = "plant_phenotype_source" + + def _info(self) -> datasets.DatasetInfo: + + # Create the source schema; this schema will keep all keys/information/labels as close to the original dataset as possible. + + # You can arbitrarily nest lists and dictionaries. + # For iterables, use lists over tuples or `datasets.Sequence` + + if self.config.schema == "source": + + features = datasets.Features( + { + "pmid": datasets.Value("string"), + "section": datasets.Value("int64"), + "passage_id": datasets.Value("string"), + "text": datasets.Value("string"), + "entities": [ + { + "offsets": [datasets.Value("int64")], + "text": datasets.Value("string"), + "type": datasets.Value("string"), + "entity_id": datasets.Value("string"), + } + ], + "relations": [ + { + "type": datasets.Value("string"), + "entity1_offsets": datasets.Sequence(datasets.Value("int64")), + "entity1_text": datasets.Value("string"), + "entity1_type": datasets.Value("string"), + "entity2_offsets": datasets.Sequence(datasets.Value("int64")), + "entity2_text": datasets.Value("string"), + "entity2_type": datasets.Value("string"), + } + ] + } + ) + + elif self.config.schema == "bigbio_kb": + features = schemas.kb_features + + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: + """Returns SplitGenerators.""" + # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration + + # If you need to access the "source" or "bigbio" config choice, that will be in self.config.name + + # LOCAL DATASETS: You do not need the dl_manager; you can ignore this argument. Make sure `gen_kwargs` in the return gets passed the right filepath + + # PUBLIC DATASETS: Assign your data-dir based on the dl_manager. + + # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs; many examples use the download_and_extract method; see the DownloadManager docs here: https://huggingface.co/docs/datasets/package_reference/builder_classes.html#datasets.DownloadManager + + # dl_manager can accept any type of nested list/dict and will give back the same structure with the url replaced with the path to local files. + + # TODO: KEEP if your dataset is PUBLIC; remove if not + urls = _URLS[_DATASETNAME] + data_dir = dl_manager.download_and_extract(urls) + + # TODO: KEEP if your dataset is LOCAL; remove if NOT + if self.config.data_dir is None: + raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.") + else: + data_dir = self.config.data_dir + + # Not all datasets have predefined canonical train/val/test splits. + # If your dataset has no predefined splits, use datasets.Split.TRAIN for all of the data. + + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + # Whatever you put in gen_kwargs will be passed to _generate_examples + gen_kwargs={ + "filepath": os.path.join(data_dir, "train.jsonl"), + "split": "train", + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepath": os.path.join(data_dir, "test.jsonl"), + "split": "test", + }, + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + gen_kwargs={ + "filepath": os.path.join(data_dir, "dev.jsonl"), + "split": "dev", + }, + ), + ] + + # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` + + # TODO: change the args of this function to match the keys in `gen_kwargs`. You may add any necessary kwargs. + + def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]: + """Yields examples as (key, example) tuples.""" + # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. + + # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. + + # NOTE: For local datasets you will have access to self.config.data_dir and self.config.data_files + + if self.config.schema == "source": + # TODO: yield (key, example) tuples in the original dataset schema + for key, example in thing: + yield key, example + + elif self.config.schema == "bigbio_[bigbio_schema_name]": + # TODO: yield (key, example) tuples in the bigbio schema + for key, example in thing: + yield key, example + + +# This template is based on the following template from the datasets package: +# https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py + + +# This allows you to run your dataloader with `python [dataset_name].py` during development +# TODO: Remove this before making your PR +if __name__ == "__main__": + datasets.load_dataset(__file__) From 23097edf0ef3d0315e3bb1b77368fa72e73ca082 Mon Sep 17 00:00:00 2001 From: David Kartchner Date: Tue, 12 Jul 2022 15:00:23 -0400 Subject: [PATCH 02/10] updates to plant-phenotype script --- .../plant_phenotype/plant_phenotype.py | 293 ++++++++++++++---- 1 file changed, 236 insertions(+), 57 deletions(-) diff --git a/bigbio/biodatasets/plant_phenotype/plant_phenotype.py b/bigbio/biodatasets/plant_phenotype/plant_phenotype.py index cbcf1bb9..bb99a397 100644 --- a/bigbio/biodatasets/plant_phenotype/plant_phenotype.py +++ b/bigbio/biodatasets/plant_phenotype/plant_phenotype.py @@ -33,6 +33,7 @@ """ import os +import itertools as it from typing import List, Tuple, Dict import datasets @@ -81,9 +82,9 @@ # This can be an arbitrarily nested dict/list of URLs (see below in `_split_generators` method) _URLS = { _DATASETNAME: [ - "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_dev_corpus.txt", - "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_test_corpus.txt", "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_train_corpus.txt", + "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_dev_corpus.txt", + "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_test_corpus.txt", ], } @@ -134,25 +135,33 @@ def _info(self) -> datasets.DatasetInfo: features = datasets.Features( { - "pmid": datasets.Value("string"), - "section": datasets.Value("int64"), "passage_id": datasets.Value("string"), - "text": datasets.Value("string"), + "pmid": datasets.Value("string"), + "section": datasets.Value("int32"), + "text": datasets.Value("string"), + # "passages": [ + # { + # 'id': datasets.Value("string"), + # 'text': datasets.Value("string"), + # 'offsets': datasets.sequence(datasets.Value("int32")), + # } + # ] + "entities": [ { - "offsets": [datasets.Value("int64")], + "offsets": datasets.sequence(datasets.Value("int32")), "text": datasets.Value("string"), "type": datasets.Value("string"), - "entity_id": datasets.Value("string"), + # "entity_id": datasets.Value("string"), } ], "relations": [ { - "type": datasets.Value("string"), - "entity1_offsets": datasets.Sequence(datasets.Value("int64")), + "relation_type": datasets.Value("string"), + "entity1_offsets": datasets.Sequence(datasets.Value("int32")), "entity1_text": datasets.Value("string"), "entity1_type": datasets.Value("string"), - "entity2_offsets": datasets.Sequence(datasets.Value("int64")), + "entity2_offsets": datasets.Sequence(datasets.Value("int32")), "entity2_text": datasets.Value("string"), "entity2_type": datasets.Value("string"), } @@ -173,77 +182,247 @@ def _info(self) -> datasets.DatasetInfo: def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" - # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration - - # If you need to access the "source" or "bigbio" config choice, that will be in self.config.name - - # LOCAL DATASETS: You do not need the dl_manager; you can ignore this argument. Make sure `gen_kwargs` in the return gets passed the right filepath - - # PUBLIC DATASETS: Assign your data-dir based on the dl_manager. - - # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs; many examples use the download_and_extract method; see the DownloadManager docs here: https://huggingface.co/docs/datasets/package_reference/builder_classes.html#datasets.DownloadManager - - # dl_manager can accept any type of nested list/dict and will give back the same structure with the url replaced with the path to local files. - - # TODO: KEEP if your dataset is PUBLIC; remove if not + urls = _URLS[_DATASETNAME] - data_dir = dl_manager.download_and_extract(urls) - - # TODO: KEEP if your dataset is LOCAL; remove if NOT - if self.config.data_dir is None: - raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.") - else: - data_dir = self.config.data_dir - - # Not all datasets have predefined canonical train/val/test splits. - # If your dataset has no predefined splits, use datasets.Split.TRAIN for all of the data. + train, dev, test = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # Whatever you put in gen_kwargs will be passed to _generate_examples gen_kwargs={ - "filepath": os.path.join(data_dir, "train.jsonl"), - "split": "train", + "filepath": train, }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ - "filepath": os.path.join(data_dir, "test.jsonl"), - "split": "test", + "filepath": test, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ - "filepath": os.path.join(data_dir, "dev.jsonl"), - "split": "dev", + "filepath": dev, }, ), ] - # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` - # TODO: change the args of this function to match the keys in `gen_kwargs`. You may add any necessary kwargs. - - def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]: + def _generate_examples(self, filepath,) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" - # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. - - # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. - # NOTE: For local datasets you will have access to self.config.data_dir and self.config.data_files - - if self.config.schema == "source": - # TODO: yield (key, example) tuples in the original dataset schema - for key, example in thing: - yield key, example - - elif self.config.schema == "bigbio_[bigbio_schema_name]": - # TODO: yield (key, example) tuples in the bigbio schema - for key, example in thing: - yield key, example + with open(filepath, 'r') as f: + chunks = f.read().strip().split('\n\n') + + + #EDIT + if self.config.schema == 'source': + for id_, doc in self._generate_source_examples(chunks): + yield id_, doc + + + #EDIT + elif self.config.schema == 'bigbio_kb': + for id_, doc in self._generate_bigbio_kb_examples(chunks): + yield id_, doc + + def _generate_whole_documents(self, chunks): + ''' + Collect individual sentence annotations into whole abstracts + ''' + prev_pmid = -1 + pmid = "" + doc_chunks = [] + + for c in chunks: + lines = c.split('\n') + dataset_id, passage_text = lines[0].split('\t') + annotations = [l.split('\t') for l in lines[1:]] + if len(annotations) == 0: + continue + + + # Get info on passage + pmid, section = dataset_id.split('_') + if prev_pmid == -1: + prev_pmid = pmid + if prev_pmid != pmid: + out = { + 'pmid': prev_pmid, + 'doc_chunks': doc_chunks, + } + # Reset everything for next PMID + prev_pmid = pmid + + yield out + + + + doc_chunks.append({'passage':passage_text, 'annotations':annotations, "sentence_id": dataset_id}) + + # Take care of last document + yield { + 'pmid': pmid, + 'doc_chunks': doc_chunks, + } + + def _generate_source_examples(self, chunks): + ''' + Generate examples in format of source schema + ''' + + for c in chunks: + lines = c.split('\n') + passage_id, passage_text = lines[0].split('\t') + annotations = [l.split('\t') for l in lines[1:]] + if len(annotations) == 0: + continue + + # Get info on passage + pmid, section = passage_id.split('_') + section = int(section) + pmid = annotations[0][0] + + # Grab entities and relations + entities = [] + relations = [] + for a in annotations: + if len(a) == 5: + entities.append({ + "offsets": (int(a[1]), int(a[2])), + "text": a[3], + "type": a[4], + }) + + elif len(a) == 10: + relations.append( + { + "relation_type": a[1], + "entity1_offsets": (int(a[2]),int(a[3])), + "entity1_text": a[4], + "entity1_type": a[5], + "entity2_offsets": (int(a[6]), int(a[7])), + "entity2_text": a[8], + "entity2_type": a[9], + } + ) + else: + # This is a special case that occurs for a single data point + relations.append( + { + "relation_type": a[1], + "entity1_offsets": (int(a[2]),int(a[3])), + "entity1_text": a[4], + "entity1_type": a[5], + "entity2_offsets": (int(a[8]), int(a[9])), + "entity2_text": a[10], + "entity2_type": a[11], + } + ) + + # Consolidate into document + document = { + 'passage_id': passage_id, + 'pmid': pmid, + 'section': section, + 'text': passage_text, + 'entities': entities, + 'relations': relations, + } + + yield passage_id, document + + def _generate_bigbio_kb_examples(self, chunks): + ''' + Generator for training examples in bigbio_kb schema format + ''' + uid = it.count(1) + for document in self._generate_whole_documents(chunks): + pmid = document['pmid'] + offset_delta = 0 + id_ = str(next(uid)) + + passages = [] + entities = [] + relations = [] + + # Iterate through each section of the article + for c in document['doc_chunks']: + passage = c['passage'] + annotations = c['annotations'] + + passages.append( + { + "id": str(next(uid)), + "text": [passage], + "offsets": [(offset_delta, offset_delta + len(passage) )], + } + ) + + + entities_sublist = [] + for a in annotations: + if len(a) == 5: + entities_sublist.append({ + "id": str(next(uid)), + "type": a[4], + "text": [a[3]], + "offsets": [(int(a[1]) + offset_delta, int(a[2]) + offset_delta)], + }) + + + + # Create mapping of offsets to entity_id + ent2id = {tuple(x['offsets']): x['id'] for x in entities_sublist} + + for a in annotations: + if len(a) == 10: + e1_offsets = [(int(a[2]) + offset_delta, int(a[3]) + offset_delta)] + e2_offsets = [(int(a[6]) + offset_delta, int(a[7]) + offset_delta)] + relations.append( + { + "id": str(next(uid)), + "type": a[1], + 'arg1_id': ent2id[tuple(e1_offsets)], + 'arg2_id': ent2id[tuple(e2_offsets)] + } + ) + + + # Special case for a single annotation + elif len(a) > 10: + e1_offsets = [(int(a[2]) + offset_delta, int(a[3]) + offset_delta)] + e2_offsets = [(int(a[8]) + offset_delta, int(a[9]) + offset_delta)] + relations.append( + { + "id": str(next(uid)), + "type": a[1], + 'arg1_id': ent2id[tuple(e1_offsets)], + 'arg2_id': ent2id[tuple(e2_offsets)] + } + ) + + + + offset_delta += len(passage) + 1 + + + doc = { + 'id': id_, + 'document_id': pmid, + 'passages': passages, + 'entities': entities, + 'relations': relations, + } + + yield id_, doc + + passages = [] + entities = [] + relations = [] + id_ = next(uid) + # This template is based on the following template from the datasets package: From 429e9c0dee7d0d5258b12ae16f9ea8e6ceae196f Mon Sep 17 00:00:00 2001 From: David Kartchner Date: Mon, 25 Jul 2022 17:40:55 -0400 Subject: [PATCH 03/10] Complete plant-phenotype dataset loader --- .../plant_phenotype/plant_phenotype.py | 366 +++++++++--------- 1 file changed, 178 insertions(+), 188 deletions(-) diff --git a/bigbio/biodatasets/plant_phenotype/plant_phenotype.py b/bigbio/biodatasets/plant_phenotype/plant_phenotype.py index bb99a397..3c63f44d 100644 --- a/bigbio/biodatasets/plant_phenotype/plant_phenotype.py +++ b/bigbio/biodatasets/plant_phenotype/plant_phenotype.py @@ -13,23 +13,19 @@ # See the License for the specific language governing permissions and # limitations under the License. -""" -This template serves as a starting point for contributing a dataset to the BigScience Biomedical repo. - -When modifying it for your dataset, look for TODO items that offer specific instructions. - -Full documentation on writing dataset loading scripts can be found here: -https://huggingface.co/docs/datasets/add_dataset.html - -To create a dataset loading script you will create a class and implement 3 methods: - * `_info`: Establishes the schema for the dataset, and returns a datasets.DatasetInfo object. - * `_split_generators`: Downloads and extracts data for each split (e.g. train/val/test) or associate local data with each split. - * `_generate_examples`: Creates examples from data on disk that conform to each schema defined in `_info`. - -TODO: Before submitting your script, delete this doc string and replace it with a description of your dataset. - - -[bigbio_schema_name] = (kb, pairs, qa, text, t2t, entailment) +"""\ +Here, we present the plant-phenotype relationship (PPR) corpus, +a resource established to support the development and evaluation +of various tasks and to extract new information in the biomedical domain. +Using the proposed guidelines, we manually annotated 600 abstracts from +the PubMed database. In the PPR corpus, 5668 plant and 11,282 phenotype +mentions were annotated, and 9709 relationships between them including +“Increase,” “Decrease,” “Association,” and “Negative” were annotated. +As the PPR corpus is split into three types—train, development, and test +sets—we suggest that our corpus will be invaluable for advancing and +evaluating text-mining techniques for biomedical NLP tasks. + +[bigbio_schema_name] = kb """ import os @@ -57,34 +53,20 @@ _DATASETNAME = "plant_phenotype" -# TODO: Add description of the dataset here -# You can copy an official description _DESCRIPTION = """\ - - -Corpus: - -Annotators: - -Annotation Quality: +The Plant-Phenotype corpus is a text corpus with human annotations of plants, +phenotypes, and their relations on a corpus in 600 PubMed abstracts. """ _HOMEPAGE = "https://github.com/DMCB-GIST/PPRcorpus" _LICENSE = "" -# TODO: Add links to the urls needed to download your dataset files. -# For local datasets, this variable can be an empty dictionary. - -# For publicly available datasets you will most likely end up passing these URLs to dl_manager in _split_generators. -# In most cases the URLs will be the same for the source and bigbio config. -# However, if you need to access different files for each config you can have multiple entries in this dict. -# This can be an arbitrarily nested dict/list of URLs (see below in `_split_generators` method) _URLS = { _DATASETNAME: [ - "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_train_corpus.txt", - "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_dev_corpus.txt", - "https://github.com/DMCB-GIST/PPRcorpus/blob/main/corpus/PPR_test_corpus.txt", + "https://raw.githubusercontent.com/davidkartchner/PPRcorpus/main/corpus/PPR_dev_corpus.txt", + "https://raw.githubusercontent.com/davidkartchner/PPRcorpus/main/corpus/PPR_dev_corpus.txt", + "https://raw.githubusercontent.com/davidkartchner/PPRcorpus/main/corpus/PPR_test_corpus.txt", ], } @@ -96,7 +78,6 @@ _BIGBIO_VERSION = "1.0.0" - class PlantPhenotypeDataset(datasets.GeneratorBasedBuilder): """\ Plant-Phenotype is dataset for named-entity recognition and relation extraction of plants and their induced phenotypes @@ -134,39 +115,42 @@ def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( - { - "passage_id": datasets.Value("string"), - "pmid": datasets.Value("string"), - "section": datasets.Value("int32"), - "text": datasets.Value("string"), - # "passages": [ - # { - # 'id': datasets.Value("string"), - # 'text': datasets.Value("string"), - # 'offsets': datasets.sequence(datasets.Value("int32")), - # } - # ] - - "entities": [ - { - "offsets": datasets.sequence(datasets.Value("int32")), - "text": datasets.Value("string"), - "type": datasets.Value("string"), - # "entity_id": datasets.Value("string"), - } - ], - "relations": [ - { - "relation_type": datasets.Value("string"), - "entity1_offsets": datasets.Sequence(datasets.Value("int32")), - "entity1_text": datasets.Value("string"), - "entity1_type": datasets.Value("string"), - "entity2_offsets": datasets.Sequence(datasets.Value("int32")), - "entity2_text": datasets.Value("string"), - "entity2_type": datasets.Value("string"), - } - ] - } + { + "passage_id": datasets.Value("string"), + "pmid": datasets.Value("string"), + "section": datasets.Value("int32"), + "text": datasets.Value("string"), + # "passages": [ + # { + # 'id': datasets.Value("string"), + # 'text': datasets.Value("string"), + # 'offsets': datasets.sequence(datasets.Value("int32")), + # } + # ] + "entities": [ + { + "offsets": datasets.Sequence(datasets.Value("int32")), + "text": datasets.Value("string"), + "type": datasets.Value("string"), + # "entity_id": datasets.Value("string"), + } + ], + "relations": [ + { + "relation_type": datasets.Value("string"), + "entity1_offsets": datasets.Sequence( + datasets.Value("int32") + ), + "entity1_text": datasets.Value("string"), + "entity1_type": datasets.Value("string"), + "entity2_offsets": datasets.Sequence( + datasets.Value("int32") + ), + "entity2_text": datasets.Value("string"), + "entity2_type": datasets.Value("string"), + } + ], + } ) elif self.config.schema == "bigbio_kb": @@ -182,7 +166,7 @@ def _info(self) -> datasets.DatasetInfo: def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" - + urls = _URLS[_DATASETNAME] train, dev, test = dl_manager.download_and_extract(urls) @@ -208,79 +192,83 @@ def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: ), ] - - def _generate_examples(self, filepath,) -> Tuple[int, Dict]: + def _generate_examples( + self, + filepath, + ) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" - with open(filepath, 'r') as f: - chunks = f.read().strip().split('\n\n') + with open(filepath, "r") as f: + chunks = f.read().strip().split("\n\n") - - #EDIT - if self.config.schema == 'source': + # EDIT + if self.config.schema == "source": for id_, doc in self._generate_source_examples(chunks): yield id_, doc - - - #EDIT - elif self.config.schema == 'bigbio_kb': + + # EDIT + elif self.config.schema == "bigbio_kb": for id_, doc in self._generate_bigbio_kb_examples(chunks): yield id_, doc def _generate_whole_documents(self, chunks): - ''' + """ Collect individual sentence annotations into whole abstracts - ''' + """ prev_pmid = -1 pmid = "" doc_chunks = [] - + for c in chunks: - lines = c.split('\n') - dataset_id, passage_text = lines[0].split('\t') - annotations = [l.split('\t') for l in lines[1:]] + lines = c.split("\n") + dataset_id, passage_text = lines[0].split("\t") + annotations = [l.split("\t") for l in lines[1:]] if len(annotations) == 0: continue - - + # Get info on passage - pmid, section = dataset_id.split('_') + pmid, section = dataset_id.split("_") if prev_pmid == -1: prev_pmid = pmid if prev_pmid != pmid: out = { - 'pmid': prev_pmid, - 'doc_chunks': doc_chunks, + "pmid": prev_pmid, + "doc_chunks": doc_chunks, } # Reset everything for next PMID prev_pmid = pmid - + passages = [] + all_annotations = [] + yield out - - - - doc_chunks.append({'passage':passage_text, 'annotations':annotations, "sentence_id": dataset_id}) - + + doc_chunks.append( + { + "passage": passage_text, + "annotations": annotations, + "sentence_id": dataset_id, + } + ) + # Take care of last document yield { - 'pmid': pmid, - 'doc_chunks': doc_chunks, - } - + "pmid": pmid, + "doc_chunks": doc_chunks, + } + def _generate_source_examples(self, chunks): - ''' + """ Generate examples in format of source schema - ''' - + """ for c in chunks: - lines = c.split('\n') - passage_id, passage_text = lines[0].split('\t') - annotations = [l.split('\t') for l in lines[1:]] + lines = c.split("\n") + passage_id, passage_text = lines[0].split("\t") + annotations = [l.split("\t") for l in lines[1:]] if len(annotations) == 0: continue # Get info on passage - pmid, section = passage_id.split('_') + pmid, section = passage_id.split("_") section = int(section) pmid = annotations[0][0] @@ -289,144 +277,146 @@ def _generate_source_examples(self, chunks): relations = [] for a in annotations: if len(a) == 5: - entities.append({ - "offsets": (int(a[1]), int(a[2])), - "text": a[3], - "type": a[4], - }) + entities.append( + { + "offsets": (int(a[1]), int(a[2])), + "text": a[3], + "type": a[4], + } + ) elif len(a) == 10: relations.append( { - "relation_type": a[1], - "entity1_offsets": (int(a[2]),int(a[3])), - "entity1_text": a[4], - "entity1_type": a[5], - "entity2_offsets": (int(a[6]), int(a[7])), - "entity2_text": a[8], - "entity2_type": a[9], - } + "relation_type": a[1], + "entity1_offsets": (int(a[2]), int(a[3])), + "entity1_text": a[4], + "entity1_type": a[5], + "entity2_offsets": (int(a[6]), int(a[7])), + "entity2_text": a[8], + "entity2_type": a[9], + } ) else: # This is a special case that occurs for a single data point relations.append( { - "relation_type": a[1], - "entity1_offsets": (int(a[2]),int(a[3])), - "entity1_text": a[4], - "entity1_type": a[5], - "entity2_offsets": (int(a[8]), int(a[9])), - "entity2_text": a[10], - "entity2_type": a[11], - } + "relation_type": a[1], + "entity1_offsets": (int(a[2]), int(a[3])), + "entity1_text": a[4], + "entity1_type": a[5], + "entity2_offsets": (int(a[8]), int(a[9])), + "entity2_text": a[10], + "entity2_type": a[11], + } ) - + # Consolidate into document document = { - 'passage_id': passage_id, - 'pmid': pmid, - 'section': section, - 'text': passage_text, - 'entities': entities, - 'relations': relations, + "passage_id": passage_id, + "pmid": pmid, + "section": section, + "text": passage_text, + "entities": entities, + "relations": relations, } - - yield passage_id, document + + yield passage_id, document def _generate_bigbio_kb_examples(self, chunks): - ''' + """ Generator for training examples in bigbio_kb schema format - ''' + """ uid = it.count(1) for document in self._generate_whole_documents(chunks): - pmid = document['pmid'] + pmid = document["pmid"] offset_delta = 0 id_ = str(next(uid)) - + passages = [] entities = [] relations = [] # Iterate through each section of the article - for c in document['doc_chunks']: - passage = c['passage'] - annotations = c['annotations'] + for c in document["doc_chunks"]: + passage = c["passage"] + annotations = c["annotations"] passages.append( { "id": str(next(uid)), "text": [passage], - "offsets": [(offset_delta, offset_delta + len(passage) )], + "offsets": [(offset_delta, offset_delta + len(passage))], } ) - entities_sublist = [] for a in annotations: if len(a) == 5: - entities_sublist.append({ - "id": str(next(uid)), - "type": a[4], - "text": [a[3]], - "offsets": [(int(a[1]) + offset_delta, int(a[2]) + offset_delta)], - }) - - - + entities_sublist.append( + { + "id": str(next(uid)), + "type": a[4], + "text": [a[3]], + "offsets": [ + (int(a[1]) + offset_delta, int(a[2]) + offset_delta) + ], + } + ) + # Create mapping of offsets to entity_id - ent2id = {tuple(x['offsets']): x['id'] for x in entities_sublist} - + ent2id = {tuple(x["offsets"]): x["id"] for x in entities_sublist} + for a in annotations: if len(a) == 10: - e1_offsets = [(int(a[2]) + offset_delta, int(a[3]) + offset_delta)] - e2_offsets = [(int(a[6]) + offset_delta, int(a[7]) + offset_delta)] + e1_offsets = [ + (int(a[2]) + offset_delta, int(a[3]) + offset_delta) + ] + e2_offsets = [ + (int(a[6]) + offset_delta, int(a[7]) + offset_delta) + ] relations.append( { "id": str(next(uid)), "type": a[1], - 'arg1_id': ent2id[tuple(e1_offsets)], - 'arg2_id': ent2id[tuple(e2_offsets)] - } + "arg1_id": ent2id[tuple(e1_offsets)], + "arg2_id": ent2id[tuple(e2_offsets)], + } ) - - # Special case for a single annotation + # Special case for a single annotation elif len(a) > 10: - e1_offsets = [(int(a[2]) + offset_delta, int(a[3]) + offset_delta)] - e2_offsets = [(int(a[8]) + offset_delta, int(a[9]) + offset_delta)] + e1_offsets = [ + (int(a[2]) + offset_delta, int(a[3]) + offset_delta) + ] + e2_offsets = [ + (int(a[8]) + offset_delta, int(a[9]) + offset_delta) + ] relations.append( { "id": str(next(uid)), "type": a[1], - 'arg1_id': ent2id[tuple(e1_offsets)], - 'arg2_id': ent2id[tuple(e2_offsets)] - } + "arg1_id": ent2id[tuple(e1_offsets)], + "arg2_id": ent2id[tuple(e2_offsets)], + } ) - - offset_delta += len(passage) + 1 - doc = { - 'id': id_, - 'document_id': pmid, - 'passages': passages, - 'entities': entities, - 'relations': relations, - } - + "id": id_, + "document_id": pmid, + "passages": passages, + "entities": entities, + "relations": relations, + } + yield id_, doc - + passages = [] entities = [] relations = [] id_ = next(uid) - - - -# This template is based on the following template from the datasets package: -# https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py # This allows you to run your dataloader with `python [dataset_name].py` during development From 88d4f3d17f40a2ce481135eab855549370eb8a33 Mon Sep 17 00:00:00 2001 From: David Kartchner Date: Tue, 26 Jul 2022 14:48:25 -0400 Subject: [PATCH 04/10] unit test fixes --- .../plant_phenotype/plant_phenotype.py | 26 ++++++++++++++----- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/bigbio/biodatasets/plant_phenotype/plant_phenotype.py b/bigbio/biodatasets/plant_phenotype/plant_phenotype.py index 3c63f44d..9a99a3f0 100644 --- a/bigbio/biodatasets/plant_phenotype/plant_phenotype.py +++ b/bigbio/biodatasets/plant_phenotype/plant_phenotype.py @@ -35,8 +35,11 @@ import datasets from bigbio.utils import schemas from bigbio.utils.configs import BigBioConfig -from bigbio.utils.constants import Tasks +from bigbio.utils.constants import Lang, Tasks +from bigbio.utils.license import Licenses +_LANGUAGES = [Lang.EN] +_PUBMED = True _LOCAL = False _CITATION = """\ @@ -52,6 +55,7 @@ """ _DATASETNAME = "plant_phenotype" +_DISPLAYNAME = "Plant-Phenotype" _DESCRIPTION = """\ The Plant-Phenotype corpus is a text corpus with human annotations of plants, @@ -60,7 +64,7 @@ _HOMEPAGE = "https://github.com/DMCB-GIST/PPRcorpus" -_LICENSE = "" +_LICENSE = Licenses.UNKNOWN _URLS = { _DATASETNAME: [ @@ -160,7 +164,7 @@ def _info(self) -> datasets.DatasetInfo: description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, - license=_LICENSE, + license=str(_LICENSE), citation=_CITATION, ) @@ -235,12 +239,11 @@ def _generate_whole_documents(self, chunks): "pmid": prev_pmid, "doc_chunks": doc_chunks, } + yield out + # Reset everything for next PMID prev_pmid = pmid - passages = [] - all_annotations = [] - - yield out + doc_chunks = [] doc_chunks.append( { @@ -342,14 +345,17 @@ def _generate_bigbio_kb_examples(self, chunks): passage = c["passage"] annotations = c["annotations"] + # Extract passages passages.append( { "id": str(next(uid)), "text": [passage], + "type": "sentence", "offsets": [(offset_delta, offset_delta + len(passage))], } ) + # Extract entities entities_sublist = [] for a in annotations: if len(a) == 5: @@ -361,12 +367,15 @@ def _generate_bigbio_kb_examples(self, chunks): "offsets": [ (int(a[1]) + offset_delta, int(a[2]) + offset_delta) ], + "normalized": [], } ) # Create mapping of offsets to entity_id ent2id = {tuple(x["offsets"]): x["id"] for x in entities_sublist} + entities.extend(entities_sublist) + # Extract relations for a in annotations: if len(a) == 10: e1_offsets = [ @@ -381,6 +390,7 @@ def _generate_bigbio_kb_examples(self, chunks): "type": a[1], "arg1_id": ent2id[tuple(e1_offsets)], "arg2_id": ent2id[tuple(e2_offsets)], + "normalized": [], } ) @@ -409,6 +419,8 @@ def _generate_bigbio_kb_examples(self, chunks): "passages": passages, "entities": entities, "relations": relations, + "events": [], + "coreferences": [], } yield id_, doc From d802d18a86a7b5ec5ecea1cd59649a5df526debc Mon Sep 17 00:00:00 2001 From: David Kartchner Date: Tue, 26 Jul 2022 14:57:27 -0400 Subject: [PATCH 05/10] fix formatting --- .../plant_phenotype/plant_phenotype.py | 28 +++++-------------- 1 file changed, 7 insertions(+), 21 deletions(-) diff --git a/bigbio/biodatasets/plant_phenotype/plant_phenotype.py b/bigbio/biodatasets/plant_phenotype/plant_phenotype.py index 9a99a3f0..0a3006a5 100644 --- a/bigbio/biodatasets/plant_phenotype/plant_phenotype.py +++ b/bigbio/biodatasets/plant_phenotype/plant_phenotype.py @@ -142,14 +142,10 @@ def _info(self) -> datasets.DatasetInfo: "relations": [ { "relation_type": datasets.Value("string"), - "entity1_offsets": datasets.Sequence( - datasets.Value("int32") - ), + "entity1_offsets": datasets.Sequence(datasets.Value("int32")), "entity1_text": datasets.Value("string"), "entity1_type": datasets.Value("string"), - "entity2_offsets": datasets.Sequence( - datasets.Value("int32") - ), + "entity2_offsets": datasets.Sequence(datasets.Value("int32")), "entity2_text": datasets.Value("string"), "entity2_type": datasets.Value("string"), } @@ -364,9 +360,7 @@ def _generate_bigbio_kb_examples(self, chunks): "id": str(next(uid)), "type": a[4], "text": [a[3]], - "offsets": [ - (int(a[1]) + offset_delta, int(a[2]) + offset_delta) - ], + "offsets": [(int(a[1]) + offset_delta, int(a[2]) + offset_delta)], "normalized": [], } ) @@ -378,12 +372,8 @@ def _generate_bigbio_kb_examples(self, chunks): # Extract relations for a in annotations: if len(a) == 10: - e1_offsets = [ - (int(a[2]) + offset_delta, int(a[3]) + offset_delta) - ] - e2_offsets = [ - (int(a[6]) + offset_delta, int(a[7]) + offset_delta) - ] + e1_offsets = [(int(a[2]) + offset_delta, int(a[3]) + offset_delta)] + e2_offsets = [(int(a[6]) + offset_delta, int(a[7]) + offset_delta)] relations.append( { "id": str(next(uid)), @@ -396,12 +386,8 @@ def _generate_bigbio_kb_examples(self, chunks): # Special case for a single annotation elif len(a) > 10: - e1_offsets = [ - (int(a[2]) + offset_delta, int(a[3]) + offset_delta) - ] - e2_offsets = [ - (int(a[8]) + offset_delta, int(a[9]) + offset_delta) - ] + e1_offsets = [(int(a[2]) + offset_delta, int(a[3]) + offset_delta)] + e2_offsets = [(int(a[8]) + offset_delta, int(a[9]) + offset_delta)] relations.append( { "id": str(next(uid)), From f43aaff48369da98176052e7da0e723d00e4da80 Mon Sep 17 00:00:00 2001 From: Jason Alan Fries Date: Thu, 14 Jul 2022 13:34:55 -0700 Subject: [PATCH 06/10] Updates to readme to include tutotrials, updated datasets, and misc content tweaks (#730) Small changes only --- README.md | 115 +++++++++++++++++++++++++++++++----------------------- 1 file changed, 66 insertions(+), 49 deletions(-) diff --git a/README.md b/README.md index d7801119..d9306168 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,29 @@ -# BigBIO: Biomedical Datasets +# BigBIO: Biomedical Dataset Library -BigBIO (BigScience Biomedical) is an open library of biomedical dataloaders built using Huggingface's (🤗) [`datasets` library](https://huggingface.co/docs/datasets/) for data-centric machine learning. Our goals include +BigBIO (BigScience Biomedical) is an open library of biomedical dataloaders built using Huggingface's (🤗) [`datasets` library](https://huggingface.co/docs/datasets/) for data-centric machine learning. Our goals include: - Lightweight, programmatic access to biomedical datasets at scale -- Promote reproducibility in data processing +- Promoting reproducibility in data processing - Better documentation for dataset provenance, licensing, and other key attributes -- Easier generation of meta-datasets (e.g., prompting, masssive MTL) +- Easier generation of meta-datasets for natural language prompting, multi-task learning -Currently BigBIO provides support for +Currently BigBIO provides support for: -- more than 120 biomedical datasets -- 10 languages +- 126+ biomedical datasets +- 10+ languages +- 12 task categories - Harmonized dataset schemas by task type - Metadata on *licensing*, *coarse/fine-grained task types*, *domain*, and more! ### Documentation -- Tutorials: *Coming Soon!* +- Tutorials + - [Materializing Meta-datasets](https://github.com/bigscience-workshop/biomedical/blob/master/notebooks/materializing_meta_datasets/materializing-meta-datasets.ipynb) + - [Prompt Engineering and Evaluation](https://github.com/bigscience-workshop/biomedical/tree/master/notebooks/promptengineering) + - BigBIO Report Card (Unit tests, Benchmarks, and more) + - Streamlit Visualization Demo +- [BigBIO Data Cards]() - [Volunteer Project Board](https://github.com/orgs/bigscience-workshop/projects/6): Implement or suggest new datasets - [Contributor Guide](CONTRIBUTING.md) - [Task Schema Overview](task_schemas.md) @@ -243,55 +249,66 @@ dsd = load_dataset(**load_dataset_kwargs) BigBIO includes support for almost all datasets included in other popular English biomedical benchmarks. -| Task Type | Dataset | BigBIO (Ours)| BLUE | BLURB | In-BoXBART | Requires DUA | -|:---------:|:-------------:|:---------------------:|:-----:|:-----:|:----------:|:------------:| -| NER | BC5-chem | ✓ | ✓ | ✓ | ✓ | | -| NER | BC5-disease | ✓ | ✓ | ✓ | ✓ | | -| NER | NCBI-disease | ✓ | | ✓ | ✓ | | -| RE | ChemProt | ✓ | ✓ | ✓ | ✓ | | -| RE | DDI | ✓ | ✓ | ✓ | ✓ | | -| STS | BIOSSES | ✓ | ✓ | ✓ | | | -| DC | HoC | ✓ | ✓ | ✓ | ✓ | | -| QA | PubMedQA | ✓ | | ✓ | | | -| QA | BioASQ | ✓ | | ✓ | ✓ | ✓ | -| TE | MedSTS | TBD | ✓ | | | ✓ | -| NER | ShARe/CLEFE | TBD | ✓ | | | ✓ | -| NER | i2b2-2010 | ✓ | ✓ | | | ✓ | -| NLI | MedNLI | ✓ | ✓ | | | ✓ | -| NER | BC2GM | ✓ | | ✓ | ✓ | | -| NER | JNLPBA | ✓ | | ✓ | ✓ | | -| NER | EBM PICO | ✓ | | ✓ | | | -| RE | GAD | ✓ | | ✓ | | | -| SR | Accelerometer | Private | | | ✓ | | -| SR | Acromegaly | Private | | | ✓ | | -| NER | AnatEM | ✓ | | | ✓ | | -| SR | Cooking | Private | | | ✓ | | -| NER | BC4CHEMD | ✓ | | | ✓ | | -| NER | BioNLP09 | ✓ | | | ✓ | | -| NER | BioNLP11EPI | ✓ | | | ✓ | | -| NER | BioNLP11ID | ✓ | | | ✓ | | -| NER | BioNLP13CG | ✓ | | | ✓ | | -| NER | BioNLP13GE | ✓ | | | ✓ | | -| NER | BioNLP13PC | ✓ | | | ✓ | | -| SR | COVID | Private | | | ✓ | | -| NER | CRAFT | TBD | | | ✓ | | -| DI | DI-2006 | ✓ | | | ✓ | | -| NER | Ex-PTM | ✓ | | | ✓ | | -| POS | Genia | ✓ | | | ✓ | | -| SR | HRT | Private | | | ✓ | | -| RFI | RFHD-2014 | TBD | | | ✓ | | -| NER | Linnaeus | ✓ | | | ✓ | | -| SA | Medical Drugs | ✓ | | | ✓ | | +| Task Type | Dataset | BigBIO (ours) | BLUE | BLURB | BoX | DUA | +|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:| +| NER | BC2GM | ✓ | | ✓ | ✓ | | +| NER | BC5-chem | ✓ | ✓ | ✓ | ✓ | | +| NER | BC5-disease | ✓ | ✓ | ✓ | ✓ | | +| NER | EBM PICO | ✓ | | ✓ | | | +| NER | JNLPBA | ✓ | | ✓ | ✓ | | +| NER | NCBI-disease | ✓ | | ✓ | ✓ | | +| RE | ChemProt | ✓ | ✓ | ✓ | ✓ | | +| RE | DDI | ✓ | ✓ | ✓ | ✓ | | +| RE | GAD | ✓ | | ✓ | | | +| QA | PubMedQA | ✓ | | ✓ | ✓ | | +| QA | BioASQ | ✓ | | ✓ | ✓ | ✓ | +| DC | HoC | ✓ | ✓ | ✓ | ✓ | | +| STS | BIOSSES | ✓ | ✓ | ✓ | | | +| STS | MedSTS | * | ✓ | | | ✓ | +| NER | n2c2 2010 | ✓ | ✓ | | ✓ | ✓ | +| NER | ShARe/CLEF 2013 | * | ✓ | | | ✓ | +| NLI | MedNLI | ✓ | ✓ | | | ✓ | +| NER | n2c2 deid 2006 | ✓ | | | ✓ | ✓ | +| DC | n2c2 RFHD 2014 | ✓ | | | ✓ | ✓ | +| NER | AnatEM | ✓ | | | ✓ | | +| NER | BC4CHEMD | ✓ | | | ✓ | | +| NER | BioNLP09 | ✓ | | | ✓ | | +| NER | BioNLP11EPI | ✓ | | | ✓ | | +| NER | BioNLP11ID | ✓ | | | ✓ | | +| NER | BioNLP13CG | ✓ | | | ✓ | | +| NER | BioNLP13GE | ✓ | | | ✓ | | +| NER | BioNLP13PC | ✓ | | | ✓ | | +| NER | CRAFT | * | | | ✓ | | +| NER | Ex-PTM | ✓ | | | ✓ | | +| NER | Linnaeus | ✓ | | | ✓ | | +| POS | GENIA | * | | | ✓ | | +| SA | Medical Drugs | ✓ | | | ✓ | | +| SR | COVID | | | | private | | +| SR | Cooking | | | | private | | +| SR | HRT | | | | private | | +| SR | Accelerometer | | | | private | | +| SR | Acromegaly | | | | private | | ## Citing If you use BigBIO in your work, please cite ``` -TBD +@article{fries2022bigbio, + title = { + BigBIO: A Framework for Data-Centric Biomedical Natural Language + Processing + }, + author = { + Fries, Jason Alan and Weber, Leon and Seelam, Natasha and Altay, + Gabriel and Datta, Debajyoti and Garda, Samuele and Kang, Myungsun + and Su, Ruisi and Kusa, Wojciech and Cahyawijaya, Samuel and others + }, + journal = {arXiv preprint arXiv:2206.15076}, + year = 2022 +} ``` - ## Acknowledgements BigBIO is a open source, community effort made possible through the efforts of many volunteers as part of BigScience and the [Biomedical Hackathon](HACKATHON.md). From c6dabd79a7ce2862c686a31e881f5e32aa2b2ab0 Mon Sep 17 00:00:00 2001 From: barthfab <88676348+barthfab@users.noreply.github.com> Date: Sat, 23 Jul 2022 19:35:05 +0200 Subject: [PATCH 07/10] typo (#732) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d9306168..16362e1f 100644 --- a/README.md +++ b/README.md @@ -213,7 +213,7 @@ written to be used with the [datasets](https://huggingface.co/docs/datasets/) package. -All of these scripts live in dataset specific directories in the `biogbio/biodatasets` folder. +All of these scripts live in dataset specific directories in the `bigbio/biodatasets` folder. If you cloned the repo, you will find the `bigbio` directory at the top level of the repo. If you pip installed the package from github, the `bigbio` directory will be in the `site-packages` directory of your python environment. 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