From 66090e3fa59c3a03fd4728c30c993e1ae9046cc7 Mon Sep 17 00:00:00 2001 From: Shubhanshu Mishra Date: Mon, 11 Apr 2022 16:26:36 -0500 Subject: [PATCH 1/6] Fixes #113 - Add Chebi (Chapti) --- biodatasets/chebi/chebi.py | 271 +++++++++++++++++++++++++++++++++++++ 1 file changed, 271 insertions(+) create mode 100644 biodatasets/chebi/chebi.py diff --git a/biodatasets/chebi/chebi.py b/biodatasets/chebi/chebi.py new file mode 100644 index 00000000..4f5c0166 --- /dev/null +++ b/biodatasets/chebi/chebi.py @@ -0,0 +1,271 @@ +# 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 utils import schemas +from utils.configs import BigBioConfig +from utils.constants import Tasks + +# TODO: Add BibTeX citation +_CITATION = """\ +@article{, + author = {}, + title = {}, + journal = {}, + volume = {}, + year = {}, + url = {}, + doi = {}, + biburl = {}, + bibsource = {} +} +""" + +# TODO: create a module level variable with your dataset name (should match script name) +# E.g. Hallmarks of Cancer: [dataset_name] --> hallmarks_of_cancer +_DATASETNAME = "[dataset_name]" + +# TODO: Add description of the dataset here +# You can copy an official description +_DESCRIPTION = """\ +This dataset is designed for XXX NLP task. +""" + +# TODO: Add a link to an official homepage for the dataset here (if possible) +_HOMEPAGE = "" + +# TODO: Add the licence for the dataset here (if possible) +# Note that this doesn't have to be a common open source license. +# Some datasets have custom licenses. In this case, simply put the full license terms +# into `_LICENSE` +_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: "url or list of urls or ... ", +} + +# TODO: add supported task by dataset. One dataset may support multiple tasks +_SUPPORTED_TASKS = [] # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] + +# TODO: set this to a version that is associated with the dataset. if none exists use "1.0.0" +# This version doesn't have to be consistent with semantic versioning. Anything that is +# provided by the original dataset as a version goes. +_SOURCE_VERSION = "" + +_BIGBIO_VERSION = "1.0.0" + + +# TODO: Name the dataset class to match the script name using CamelCase instead of snake_case +# Append "Dataset" to the class name: BioASQ --> BioasqDataset +class NewDataset(datasets.GeneratorBasedBuilder): + """TODO: Short description of my dataset.""" + + SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) + BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) + + # You will be able to load the "source" or "bigbio" configurations with + # ds_source = datasets.load_dataset('my_dataset', name='source') + # ds_bigbio = datasets.load_dataset('my_dataset', name='bigbio') + + # For local datasets you can make use of the `data_dir` and `data_files` kwargs + # https://huggingface.co/docs/datasets/add_dataset.html#downloading-data-files-and-organizing-splits + # ds_source = datasets.load_dataset('my_dataset', name='source', data_dir="/path/to/data/files") + # ds_bigbio = datasets.load_dataset('my_dataset', name='bigbio', data_dir="/path/to/data/files") + + # TODO: For each dataset, implement Config for Source and BigBio; + # If dataset contains more than one subset (see examples/bioasq.py) implement for EACH of them. + # Each of them should contain: + # - name: should be unique for each dataset config eg. bioasq10b_(source|bigbio)_[bigbio_schema_name] + # - version: option = (SOURCE_VERSION|BIGBIO_VERSION) + # - description: one line description for the dataset + # - schema: options = (source|bigbio_[bigbio_schema_name]) + # - subset_id: subset id is the canonical name for the dataset (eg. bioasq10b) + # where [bigbio_schema_name] = (kb, pairs, qa, text, t2t, entailment) + + BUILDER_CONFIGS = [ + BigBioConfig( + name="[dataset_name]_source", + version=SOURCE_VERSION, + description="[dataset_name] source schema", + schema="source", + subset_id="[dataset_name]", + ), + BigBioConfig( + name="[dataset_name]_bigbio_[bigbio_schema_name]", + version=BIGBIO_VERSION, + description="[dataset_name] BigBio schema", + schema="bigbio_[bigbio_schema_name]", + subset_id="[dataset_name]", + ), + ] + + DEFAULT_CONFIG_NAME = "[dataset_name]_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": + # TODO: Create your source schema here + raise NotImplementedError() + + # EX: Arbitrary NER type dataset + # features = datasets.Features( + # { + # "doc_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"), + # } + # ], + # } + # ) + + # Choose the appropriate bigbio schema for your task and copy it here. You can find information on the schemas in the CONTRIBUTING guide. + + # In rare cases you may get a dataset that supports multiple tasks requiring multiple schemas. In that case you can define multiple bigbio configs with a bigbio_[bigbio_schema_name] format. + + # For example bigbio_kb, bigbio_t2t + elif self.config.schema == "bigbio_[bigbio_schema_name]": + # e.g. features = schemas.kb_features + # TODO: Choose your big-bio schema here + raise NotImplementedError() + + 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 66a2609910d3144c5b6ea94420e4204e411308e8 Mon Sep 17 00:00:00 2001 From: Shubhanshu Mishra Date: Tue, 12 Apr 2022 10:18:46 -0500 Subject: [PATCH 2/6] Updated info. Waiting on questions. --- biodatasets/chebi/chebi.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/biodatasets/chebi/chebi.py b/biodatasets/chebi/chebi.py index 4f5c0166..234cda27 100644 --- a/biodatasets/chebi/chebi.py +++ b/biodatasets/chebi/chebi.py @@ -56,22 +56,24 @@ # TODO: create a module level variable with your dataset name (should match script name) # E.g. Hallmarks of Cancer: [dataset_name] --> hallmarks_of_cancer -_DATASETNAME = "[dataset_name]" +_DATASETNAME = "chebi" # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ -This dataset is designed for XXX NLP task. +This dataset contains biomedical named entities in patents. +It is sourced from the project Chemical Entities of Biological Interest (ChEBI), which is a freely available database of chemical compounds and other small molecular entities. The dataset is the Chapti dataset of ChEBI - http://chebi.cvs.sourceforge.net/viewvc/chebi/chapati/ + """ # TODO: Add a link to an official homepage for the dataset here (if possible) -_HOMEPAGE = "" +_HOMEPAGE = "http://chebi.cvs.sourceforge.net/viewvc/chebi/chapati/" # TODO: Add the licence for the dataset here (if possible) # Note that this doesn't have to be a common open source license. # Some datasets have custom licenses. In this case, simply put the full license terms # into `_LICENSE` -_LICENSE = "" +_LICENSE = "Creative Commons" # TODO: Add links to the urls needed to download your dataset files. # For local datasets, this variable can be an empty dictionary. @@ -85,12 +87,15 @@ } # TODO: add supported task by dataset. One dataset may support multiple tasks -_SUPPORTED_TASKS = [] # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] +_SUPPORTED_TASKS = [ + Tasks.NAMED_ENTITY_RECOGNITION, + Tasks.NAMED_ENTITY_DISAMBIGUATION, +] # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] # TODO: set this to a version that is associated with the dataset. if none exists use "1.0.0" # This version doesn't have to be consistent with semantic versioning. Anything that is # provided by the original dataset as a version goes. -_SOURCE_VERSION = "" +_SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" @@ -206,7 +211,9 @@ def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: # 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.") + raise ValueError( + "This is a local dataset. Please pass the data_dir kwarg to load_dataset." + ) else: data_dir = self.config.data_dir From 6afa559945e79e8e64a70b2280e2963bc346296d Mon Sep 17 00:00:00 2001 From: Shubhanshu Mishra Date: Thu, 28 Apr 2022 08:33:06 -0500 Subject: [PATCH 3/6] Added working code for ChEBI --- biodatasets/chebi/chebi.py | 333 +++++++++++++++++-------------------- 1 file changed, 155 insertions(+), 178 deletions(-) diff --git a/biodatasets/chebi/chebi.py b/biodatasets/chebi/chebi.py index 234cda27..0b47b29d 100644 --- a/biodatasets/chebi/chebi.py +++ b/biodatasets/chebi/chebi.py @@ -14,174 +14,128 @@ # 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) +ChEBI Chapti contains the results of a collaboration between the European Patent Office and +the ChEBI team. The goal of the project was to identify chemicals within patents and cross- +reference them to ChEBI. The teams manually annotated chemicals in a set of 40 patents. +This was used to measure the performance of the various text-mining tools. This set of +40 patents is distributed in this directory. The results of this work can be seen on the ChEBI +website. + +This work is distributed under the Creative Commons license: http://creativecommons.org/licenses/by/3.0/ """ import os -from typing import List, Tuple, Dict +from pathlib import Path +from typing import Any, List, Tuple, Dict import datasets +from lxml import etree from utils import schemas from utils.configs import BigBioConfig from utils.constants import Tasks -# TODO: Add BibTeX citation _CITATION = """\ @article{, - author = {}, - title = {}, - journal = {}, - volume = {}, - year = {}, - url = {}, - doi = {}, - biburl = {}, - bibsource = {} + title = {ChEBI: a database and ontology for chemical entities of biological interest}, + author = {Degtyarenko, Kirill and de Matos, Paula and Ennis, Marcus and Hastings, Janna and Zbinden, Martin and McNaught, Alan and Alcántara, Rafael and Darsow, Michael and Guedj, Mickaël and Ashburner, Michael}, + doi = {10.1093/nar/gkm791}, + number = {Database issue}, + volume = {36}, + month = {January}, + year = {2008}, + journal = {Nucleic acids research}, + issn = {0305-1048}, + pages = {D344—50}, + url = {https://europepmc.org/articles/PMC2238832}, + biburl = {https://aclanthology.org/W19-5008.bib}, + bibsource = {https://aclanthology.org/W19-5008/} } """ -# TODO: create a module level variable with your dataset name (should match script name) -# E.g. Hallmarks of Cancer: [dataset_name] --> hallmarks_of_cancer _DATASETNAME = "chebi" -# TODO: Add description of the dataset here -# You can copy an official description _DESCRIPTION = """\ -This dataset contains biomedical named entities in patents. -It is sourced from the project Chemical Entities of Biological Interest (ChEBI), which is a freely available database of chemical compounds and other small molecular entities. The dataset is the Chapti dataset of ChEBI - http://chebi.cvs.sourceforge.net/viewvc/chebi/chapati/ - +ChEBI Chapti contains the results of a collaboration between the European Patent Office and +the ChEBI team. The goal of the project was to identify chemicals within patents and cross- +reference them to ChEBI. The teams manually annotated chemicals in a set of 40 patents. +This was used to measure the performance of the various text-mining tools. This set of +40 patents is distributed in this directory. The results of this work can be seen on the ChEBI +website. """ -# TODO: Add a link to an official homepage for the dataset here (if possible) _HOMEPAGE = "http://chebi.cvs.sourceforge.net/viewvc/chebi/chapati/" -# TODO: Add the licence for the dataset here (if possible) -# Note that this doesn't have to be a common open source license. -# Some datasets have custom licenses. In this case, simply put the full license terms -# into `_LICENSE` -_LICENSE = "Creative Commons" - -# TODO: Add links to the urls needed to download your dataset files. -# For local datasets, this variable can be an empty dictionary. +_LICENSE = "Creative Commons License Attribution-ShareAlike 4.0 International" -# 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: "url or list of urls or ... ", + # The original dataset is hosted on CVS on sourceforge. Hence I have downloaded and reuploded it as tar.gz format. + # Converted via the following command: + # cvs -z3 -d:pserver:anonymous@a.cvs.sourceforge.net:/cvsroot/chebi co chapati/patentsGoldStandard/PatentAnnotations_GoldStandard.tgz + # mkdir -p ./MoNERo + # pushd ./MoNERo && 7z x ../MoNERo_2019.7z && popd + # tar -czf MoNERo.tar.gz ./MoNERo + _DATASETNAME: "https://github.com/bigscience-workshop/biomedical/files/8568960/PatentAnnotations_GoldStandard.tar.gz", } -# TODO: add supported task by dataset. One dataset may support multiple tasks -_SUPPORTED_TASKS = [ - Tasks.NAMED_ENTITY_RECOGNITION, - Tasks.NAMED_ENTITY_DISAMBIGUATION, -] # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] +_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION] -# TODO: set this to a version that is associated with the dataset. if none exists use "1.0.0" -# This version doesn't have to be consistent with semantic versioning. Anything that is -# provided by the original dataset as a version goes. _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" -# TODO: Name the dataset class to match the script name using CamelCase instead of snake_case -# Append "Dataset" to the class name: BioASQ --> BioasqDataset -class NewDataset(datasets.GeneratorBasedBuilder): - """TODO: Short description of my dataset.""" +class ChebiDataset(datasets.GeneratorBasedBuilder): + """ChEBI Chapti: Patents dataset for NER and Entity Linking.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) - # You will be able to load the "source" or "bigbio" configurations with - # ds_source = datasets.load_dataset('my_dataset', name='source') - # ds_bigbio = datasets.load_dataset('my_dataset', name='bigbio') - - # For local datasets you can make use of the `data_dir` and `data_files` kwargs - # https://huggingface.co/docs/datasets/add_dataset.html#downloading-data-files-and-organizing-splits - # ds_source = datasets.load_dataset('my_dataset', name='source', data_dir="/path/to/data/files") - # ds_bigbio = datasets.load_dataset('my_dataset', name='bigbio', data_dir="/path/to/data/files") - - # TODO: For each dataset, implement Config for Source and BigBio; - # If dataset contains more than one subset (see examples/bioasq.py) implement for EACH of them. - # Each of them should contain: - # - name: should be unique for each dataset config eg. bioasq10b_(source|bigbio)_[bigbio_schema_name] - # - version: option = (SOURCE_VERSION|BIGBIO_VERSION) - # - description: one line description for the dataset - # - schema: options = (source|bigbio_[bigbio_schema_name]) - # - subset_id: subset id is the canonical name for the dataset (eg. bioasq10b) - # where [bigbio_schema_name] = (kb, pairs, qa, text, t2t, entailment) - BUILDER_CONFIGS = [ BigBioConfig( - name="[dataset_name]_source", + name=f"{_DATASETNAME}_source", version=SOURCE_VERSION, - description="[dataset_name] source schema", + description=f"{_DATASETNAME} source schema", schema="source", - subset_id="[dataset_name]", + subset_id=f"{_DATASETNAME}", ), BigBioConfig( - name="[dataset_name]_bigbio_[bigbio_schema_name]", + name=f"{_DATASETNAME}_bigbio_kb", version=BIGBIO_VERSION, - description="[dataset_name] BigBio schema", - schema="bigbio_[bigbio_schema_name]", - subset_id="[dataset_name]", + description=f"{_DATASETNAME} BigBio schema", + schema="bigbio_kb", + subset_id=f"{_DATASETNAME}", ), ] - DEFAULT_CONFIG_NAME = "[dataset_name]_source" + DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_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": - # TODO: Create your source schema here - raise NotImplementedError() - - # EX: Arbitrary NER type dataset - # features = datasets.Features( - # { - # "doc_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"), - # } - # ], - # } - # ) - - # Choose the appropriate bigbio schema for your task and copy it here. You can find information on the schemas in the CONTRIBUTING guide. - - # In rare cases you may get a dataset that supports multiple tasks requiring multiple schemas. In that case you can define multiple bigbio configs with a bigbio_[bigbio_schema_name] format. + features = datasets.Features( + { + "doc_id": datasets.Value("string"), + "text": datasets.Value("string"), + "entities": [ + { + "phrase": datasets.Value("string"), + "start": datasets.Value("int64"), + "end": datasets.Value("int64"), + "attrs": { + "chebi-id": datasets.Value("string"), + "comment": datasets.Value("string"), + "epochem-id": datasets.Value("string"), + "id": datasets.Value("string"), + "name": datasets.Value("string"), + "relevant": datasets.Value("string"), + "type": datasets.Value("string"), + }, + } + ], + } + ) - # For example bigbio_kb, bigbio_t2t - elif self.config.schema == "bigbio_[bigbio_schema_name]": - # e.g. features = schemas.kb_features - # TODO: Choose your big-bio schema here - raise NotImplementedError() + elif self.config.schema == "bigbio_kb": + features = schemas.kb_features return datasets.DatasetInfo( description=_DESCRIPTION, @@ -193,86 +147,109 @@ 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. + data_dir = Path(data_dir) / "scrapbook" + file_paths = list(data_dir.glob("./*/source.xml")) + print(len(file_paths)) 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"), + "file_paths": file_paths, "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]: + def _generate_examples(self, file_paths, 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: + for filepath in file_paths: + key, example = self._read_example_from_file(filepath) 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: + elif self.config.schema == "bigbio_kb": + for filepath in file_paths: + key, example = self._read_example_from_file_in_kb_schema(filepath) 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 + def _parse_paragraph(self, para, start=0): + para_text = [] + entities = [] + for e in para.iter(): + para_text.append(e.text) + if e.tag == "ne": + entity = { + "phrase": e.text, + "start": start, + "end": start + len(e.text), + "attrs": dict(e.attrib), + } + entities.append(entity) + start += len(e.text) + if e.tail: + para_text.append(e.tail) + start += len(e.tail) + return "".join(para_text), entities + + def _read_example_from_file(self, filepath: Path) -> Tuple[str, Dict]: + with open(filepath, encoding="utf-8") as fp: + xml = etree.fromstring(fp.read().encode("utf-8")) + key = filepath.parent.name + document_text = [] + entities = [] + start = 0 + for para in xml.iter("P", "p"): + # print(para.text) + para_text, para_entities = self._parse_paragraph(para, start=start) + document_text.append(para_text) + start += len(para_text) + entities.extend(para_entities) + document_text = "".join(document_text) + example = {"doc_id": key, "text": document_text, "entities": entities} + return key, example + + def _parse_example_to_kb_schema(self, example) -> Dict[str, Any]: + text = example["text"] + doc_id = example["doc_id"] + passages = [ + { + "id": f"{doc_id}-P0", + "type": "abstract", + "text": [text], + "offsets": [[0, len(text)]], + } + ] + entities = [] + for i, e in enumerate(example["entities"]): + entity = { + "id": f"{doc_id}-E{i}", + "text": [e["phrase"]], + "offsets": [[e["start"], e["end"]]], + "type": e["attrs"]["type"], + "normalized": [{"db_name": "chebi", "db_id": e["attrs"]["chebi-id"]}], + } + entities.append(entity) + data = { + "id": doc_id, + "document_id": doc_id, + "passages": passages, + "entities": entities, + "relations": [], + "events": [], + "coreferences": [], + } + return data + + def _read_example_from_file_in_kb_schema(self, filepath: Path) -> Tuple[str, Dict]: + key, example = self._read_example_from_file(filepath) + example = self._parse_example_to_kb_schema(example) + return key, example -# 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 b4cf675b23724e47521966668a8ebc1f551ddf04 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mario=20S=C3=A4nger?= Date: Sat, 26 Oct 2024 09:31:48 +0200 Subject: [PATCH 4/6] refactor: Refactor implementation of chebi corpus to new hub-style --- bigbio/hub/hub_repos/chebi/README.md | 43 ++ bigbio/hub/hub_repos/chebi/bigbiohub.py | 592 ++++++++++++++++++ .../hub/hub_repos}/chebi/chebi.py | 82 ++- 3 files changed, 672 insertions(+), 45 deletions(-) create mode 100644 bigbio/hub/hub_repos/chebi/README.md create mode 100644 bigbio/hub/hub_repos/chebi/bigbiohub.py rename {biodatasets => bigbio/hub/hub_repos}/chebi/chebi.py (78%) diff --git a/bigbio/hub/hub_repos/chebi/README.md b/bigbio/hub/hub_repos/chebi/README.md new file mode 100644 index 00000000..f0e96121 --- /dev/null +++ b/bigbio/hub/hub_repos/chebi/README.md @@ -0,0 +1,43 @@ + +--- +language: +- en +bigbio_language: +- English +license: unknown +multilinguality: monolingual +bigbio_license_shortname: UNKNOWN +pretty_name: PPR +homepage: https://github.com/DMCB-GIST/PPRcorpus +bigbio_pubmed: True +bigbio_public: True +bigbio_tasks: +- NAMED_ENTITY_RECOGNITION +- RELATION_EXTRACTION +--- + + +# Dataset Card for PPR + +## Dataset Description + +- **Homepage:** https://github.com/DMCB-GIST/PPRcorpus +- **Pubmed:** True +- **Public:** True +- **Tasks:** NER,RE + +The Plant-Phenotype corpus is a text corpus with human annotations of plants, phenotypes, and their relations on a corpus in 600 PubMed abstracts. + +## Citation Information + +``` +@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}, +} +``` diff --git a/bigbio/hub/hub_repos/chebi/bigbiohub.py b/bigbio/hub/hub_repos/chebi/bigbiohub.py new file mode 100644 index 00000000..a4792b4b --- /dev/null +++ b/bigbio/hub/hub_repos/chebi/bigbiohub.py @@ -0,0 +1,592 @@ +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="") + + +@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"), + } + ], + } + ], + } +) + + +TASK_TO_SCHEMA = { + Tasks.NAMED_ENTITY_RECOGNITION.name: "KB", + Tasks.NAMED_ENTITY_DISAMBIGUATION.name: "KB", + Tasks.EVENT_EXTRACTION.name: "KB", + Tasks.RELATION_EXTRACTION.name: "KB", + Tasks.COREFERENCE_RESOLUTION.name: "KB", + Tasks.QUESTION_ANSWERING.name: "QA", + Tasks.TEXTUAL_ENTAILMENT.name: "TE", + Tasks.SEMANTIC_SIMILARITY.name: "PAIRS", + Tasks.TEXT_PAIRS_CLASSIFICATION.name: "PAIRS", + Tasks.PARAPHRASING.name: "T2T", + Tasks.TRANSLATION.name: "T2T", + Tasks.SUMMARIZATION.name: "T2T", + Tasks.TEXT_CLASSIFICATION.name: "TEXT", +} + +SCHEMA_TO_TASKS = defaultdict(set) +for task, schema in TASK_TO_SCHEMA.items(): + SCHEMA_TO_TASKS[schema].add(task) +SCHEMA_TO_TASKS = dict(SCHEMA_TO_TASKS) + +VALID_TASKS = set(TASK_TO_SCHEMA.keys()) +VALID_SCHEMAS = set(TASK_TO_SCHEMA.values()) + +SCHEMA_TO_FEATURES = { + "KB": kb_features, + "QA": qa_features, + "TE": entailment_features, + "T2T": text2text_features, + "TEXT": text_features, + "PAIRS": pairs_features, +} + + +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) + try: + with annotation_file.open() as f: + ann_lines.extend(f.readlines()) + except Exception: + continue + + 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 diff --git a/biodatasets/chebi/chebi.py b/bigbio/hub/hub_repos/chebi/chebi.py similarity index 78% rename from biodatasets/chebi/chebi.py rename to bigbio/hub/hub_repos/chebi/chebi.py index 0b47b29d..2a2c0c1c 100644 --- a/biodatasets/chebi/chebi.py +++ b/bigbio/hub/hub_repos/chebi/chebi.py @@ -13,46 +13,39 @@ # See the License for the specific language governing permissions and # limitations under the License. -""" -ChEBI Chapti contains the results of a collaboration between the European Patent Office and -the ChEBI team. The goal of the project was to identify chemicals within patents and cross- -reference them to ChEBI. The teams manually annotated chemicals in a set of 40 patents. -This was used to measure the performance of the various text-mining tools. This set of -40 patents is distributed in this directory. The results of this work can be seen on the ChEBI -website. - -This work is distributed under the Creative Commons license: http://creativecommons.org/licenses/by/3.0/ -""" - -import os from pathlib import Path -from typing import Any, List, Tuple, Dict +from typing import Any, Dict, List, Tuple import datasets from lxml import etree -from utils import schemas -from utils.configs import BigBioConfig -from utils.constants import Tasks + +from .bigbiohub import BigBioConfig, Tasks, kb_features + +_LANGUAGES = ["English"] +_PUBMED = False +_LOCAL = False _CITATION = """\ @article{, - title = {ChEBI: a database and ontology for chemical entities of biological interest}, - author = {Degtyarenko, Kirill and de Matos, Paula and Ennis, Marcus and Hastings, Janna and Zbinden, Martin and McNaught, Alan and Alcántara, Rafael and Darsow, Michael and Guedj, Mickaël and Ashburner, Michael}, - doi = {10.1093/nar/gkm791}, - number = {Database issue}, - volume = {36}, - month = {January}, - year = {2008}, - journal = {Nucleic acids research}, - issn = {0305-1048}, - pages = {D344—50}, - url = {https://europepmc.org/articles/PMC2238832}, - biburl = {https://aclanthology.org/W19-5008.bib}, - bibsource = {https://aclanthology.org/W19-5008/} +title = {ChEBI: a database and ontology for chemical entities of biological interest}, +author = {Degtyarenko, Kirill and de Matos, Paula and Ennis, Marcus and Hastings, Janna and Zbinden, Martin and \ + McNaught, Alan and Alcántara, Rafael and Darsow, Michael and Guedj, Mickaël and Ashburner, Michael}, +doi = {10.1093/nar/gkm791}, +number = {Database issue}, +volume = {36}, +month = {January}, +year = {2008}, +journal = {Nucleic acids research}, +issn = {0305-1048}, +pages = {D344—50}, +url = {https://europepmc.org/articles/PMC2238832}, +biburl = {https://aclanthology.org/W19-5008.bib}, +bibsource = {https://aclanthology.org/W19-5008/} } """ _DATASETNAME = "chebi" +_DISPLAYNAME = "Chebi" _DESCRIPTION = """\ ChEBI Chapti contains the results of a collaboration between the European Patent Office and @@ -64,23 +57,23 @@ """ _HOMEPAGE = "http://chebi.cvs.sourceforge.net/viewvc/chebi/chapati/" +_LICENSE = "CC_BY_SA_4p0" -_LICENSE = "Creative Commons License Attribution-ShareAlike 4.0 International" - +DATA_URL = "https://github.com/bigscience-workshop/biomedical/files/8568960/PatentAnnotations_GoldStandard.tar.gz" _URLS = { # The original dataset is hosted on CVS on sourceforge. Hence I have downloaded and reuploded it as tar.gz format. # Converted via the following command: - # cvs -z3 -d:pserver:anonymous@a.cvs.sourceforge.net:/cvsroot/chebi co chapati/patentsGoldStandard/PatentAnnotations_GoldStandard.tgz + # cvs -z3 -d:pserver:anonymous@a.cvs.sourceforge.net:/cvsroot/chebi co \ + # chapati/patentsGoldStandard/PatentAnnotations_GoldStandard.tgz # mkdir -p ./MoNERo # pushd ./MoNERo && 7z x ../MoNERo_2019.7z && popd # tar -czf MoNERo.tar.gz ./MoNERo - _DATASETNAME: "https://github.com/bigscience-workshop/biomedical/files/8568960/PatentAnnotations_GoldStandard.tar.gz", + _DATASETNAME: DATA_URL, } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION] _SOURCE_VERSION = "1.0.0" - _BIGBIO_VERSION = "1.0.0" @@ -133,9 +126,10 @@ def _info(self) -> datasets.DatasetInfo: ], } ) - elif self.config.schema == "bigbio_kb": - features = schemas.kb_features + features = kb_features + else: + raise NotImplementedError(f"Schema {self.config.schema} is not supported") return datasets.DatasetInfo( description=_DESCRIPTION, @@ -150,13 +144,11 @@ def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: urls = _URLS[_DATASETNAME] data_dir = dl_manager.download_and_extract(urls) data_dir = Path(data_dir) / "scrapbook" - file_paths = list(data_dir.glob("./*/source.xml")) - print(len(file_paths)) + file_paths = list(sorted(data_dir.glob("./*/source.xml"))) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, - # Whatever you put in gen_kwargs will be passed to _generate_examples gen_kwargs={ "file_paths": file_paths, "split": "train", @@ -203,14 +195,16 @@ def _read_example_from_file(self, filepath: Path) -> Tuple[str, Dict]: document_text = [] entities = [] start = 0 + for para in xml.iter("P", "p"): - # print(para.text) para_text, para_entities = self._parse_paragraph(para, start=start) document_text.append(para_text) start += len(para_text) entities.extend(para_entities) + document_text = "".join(document_text) example = {"doc_id": key, "text": document_text, "entities": entities} + return key, example def _parse_example_to_kb_schema(self, example) -> Dict[str, Any]: @@ -231,7 +225,9 @@ def _parse_example_to_kb_schema(self, example) -> Dict[str, Any]: "text": [e["phrase"]], "offsets": [[e["start"], e["end"]]], "type": e["attrs"]["type"], - "normalized": [{"db_name": "chebi", "db_id": e["attrs"]["chebi-id"]}], + "normalized": [ + {"db_name": "chebi", "db_id": chebi_id.strip()} for chebi_id in e["attrs"]["chebi-id"].split(",") + ], } entities.append(entity) data = { @@ -249,7 +245,3 @@ def _read_example_from_file_in_kb_schema(self, filepath: Path) -> Tuple[str, Dic key, example = self._read_example_from_file(filepath) example = self._parse_example_to_kb_schema(example) return key, example - - -if __name__ == "__main__": - datasets.load_dataset(__file__) From 1793774bbabe65c00dec81da6e45e5b020b26159 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mario=20S=C3=A4nger?= Date: Sat, 26 Oct 2024 09:32:17 +0200 Subject: [PATCH 5/6] fix: Remove init files for consistency --- bigbio/hub/hub_repos/czi_drsm/__init__.py | 0 bigbio/hub/hub_repos/flambe/__init__.py | 0 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 bigbio/hub/hub_repos/czi_drsm/__init__.py delete mode 100644 bigbio/hub/hub_repos/flambe/__init__.py diff --git a/bigbio/hub/hub_repos/czi_drsm/__init__.py b/bigbio/hub/hub_repos/czi_drsm/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/bigbio/hub/hub_repos/flambe/__init__.py b/bigbio/hub/hub_repos/flambe/__init__.py deleted file mode 100644 index e69de29b..00000000 From 44c413e4884ce3fb18ba0448aba5d26c3e5cd846 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mario=20S=C3=A4nger?= Date: Sat, 26 Oct 2024 09:44:04 +0200 Subject: [PATCH 6/6] fix: Update README.md --- bigbio/hub/hub_repos/chebi/README.md | 54 +++++++++++++++++----------- bigbio/hub/hub_repos/chebi/chebi.py | 2 +- 2 files changed, 34 insertions(+), 22 deletions(-) diff --git a/bigbio/hub/hub_repos/chebi/README.md b/bigbio/hub/hub_repos/chebi/README.md index f0e96121..17f50aae 100644 --- a/bigbio/hub/hub_repos/chebi/README.md +++ b/bigbio/hub/hub_repos/chebi/README.md @@ -4,40 +4,52 @@ language: - en bigbio_language: - English -license: unknown +license: CC_BY_SA_4p0 multilinguality: monolingual -bigbio_license_shortname: UNKNOWN -pretty_name: PPR -homepage: https://github.com/DMCB-GIST/PPRcorpus -bigbio_pubmed: True -bigbio_public: True +bigbio_license_shortname: CC_BY_SA_4p0 +pretty_name: Chebi +homepage: https://sourceforge.net/projects/chebi/ +bigbio_pubmed: False +bigbio_public: False bigbio_tasks: - NAMED_ENTITY_RECOGNITION -- RELATION_EXTRACTION +- NAMED_ENTITY_DISAMBIGUATION --- -# Dataset Card for PPR +# Dataset Card for Chebi ## Dataset Description -- **Homepage:** https://github.com/DMCB-GIST/PPRcorpus -- **Pubmed:** True -- **Public:** True -- **Tasks:** NER,RE +- **Homepage:** https://sourceforge.net/projects/chebi/ +- **Pubmed:** False +- **Public:** False +- **Tasks:** NER,NED -The Plant-Phenotype corpus is a text corpus with human annotations of plants, phenotypes, and their relations on a corpus in 600 PubMed abstracts. +ChEBI Chapti contains the results of a collaboration between the European Patent Office and +the ChEBI team. The goal of the project was to identify chemicals within patents and cross- +reference them to ChEBI. The teams manually annotated chemicals in a set of 40 patents. +This was used to measure the performance of the various text-mining tools. This set of +40 patents is distributed in this directory. The results of this work can be seen on the ChEBI +website. ## Citation Information ``` -@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}, +@article{, +title = {ChEBI: a database and ontology for chemical entities of biological interest}, +author = {Degtyarenko, Kirill and de Matos, Paula and Ennis, Marcus and Hastings, Janna and Zbinden, Martin and \ + McNaught, Alan and Alcántara, Rafael and Darsow, Michael and Guedj, Mickaël and Ashburner, Michael}, +doi = {10.1093/nar/gkm791}, +number = {Database issue}, +volume = {36}, +month = {January}, +year = {2008}, +journal = {Nucleic acids research}, +issn = {0305-1048}, +pages = {D344—50}, +url = {https://europepmc.org/articles/PMC2238832}, +biburl = {https://aclanthology.org/W19-5008.bib}, +bibsource = {https://aclanthology.org/W19-5008/} } ``` diff --git a/bigbio/hub/hub_repos/chebi/chebi.py b/bigbio/hub/hub_repos/chebi/chebi.py index 2a2c0c1c..d947156a 100644 --- a/bigbio/hub/hub_repos/chebi/chebi.py +++ b/bigbio/hub/hub_repos/chebi/chebi.py @@ -56,7 +56,7 @@ website. """ -_HOMEPAGE = "http://chebi.cvs.sourceforge.net/viewvc/chebi/chapati/" +_HOMEPAGE = "https://sourceforge.net/projects/chebi/" _LICENSE = "CC_BY_SA_4p0" DATA_URL = "https://github.com/bigscience-workshop/biomedical/files/8568960/PatentAnnotations_GoldStandard.tar.gz"