From 0435fcd55a5af1978610c4c5969e26b38e76c975 Mon Sep 17 00:00:00 2001 From: Shubhanshu Mishra Date: Mon, 9 Dec 2024 08:04:38 -0800 Subject: [PATCH] Closes #67 - Add Monero (#516) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Fixes #67 - Add monero * Working setup for Monero. Tested via data loading. * fix: remove main call * Fixed entity offset * refactor: Refactor and improve implementation of MoNERo to hub-style integration * style: Fix code formatting --------- Co-authored-by: Natasha Seelam Co-authored-by: Mario Sänger --- bigbio/hub/hub_repos/monero/README.md | 48 ++ bigbio/hub/hub_repos/monero/bigbiohub.py | 592 +++++++++++++++++++++++ bigbio/hub/hub_repos/monero/monero.py | 291 +++++++++++ 3 files changed, 931 insertions(+) create mode 100644 bigbio/hub/hub_repos/monero/README.md create mode 100644 bigbio/hub/hub_repos/monero/bigbiohub.py create mode 100644 bigbio/hub/hub_repos/monero/monero.py diff --git a/bigbio/hub/hub_repos/monero/README.md b/bigbio/hub/hub_repos/monero/README.md new file mode 100644 index 00000000..8b13ba15 --- /dev/null +++ b/bigbio/hub/hub_repos/monero/README.md @@ -0,0 +1,48 @@ + +--- +language: +- ro +bigbio_language: +- Romanian +license: cc-by-sa-4.0 +multilinguality: monolingual +bigbio_license_shortname: CC_BY_SA_4p0 +pretty_name: MoNERo +homepage: https://www.racai.ro/en/tools/text/ +bigbio_pubmed: False +bigbio_public: True +bigbio_tasks: +- NAMED_ENTITY_RECOGNITION +--- + + +# Dataset Card for MoNERo + +## Dataset Description + +- **Homepage:** https://www.racai.ro/en/tools/text/ +- **Pubmed:** False +- **Public:** True +- **Tasks:** NER + +MoNERo: a Biomedical Gold Standard Corpus for the Romanian Language for part of speech tagging and named entity recognition. + + +## Citation Information + +``` +@inproceedings{, + title = {{M}o{NER}o: a Biomedical Gold Standard Corpus for the {R}omanian Language}, + author = {Mitrofan, Maria and Barbu Mititelu, Verginica and Mitrofan, Grigorina}, + booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task", + month = aug, + year = "2019", + address = "Florence, Italy", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W19-5008", + doi = "10.18653/v1/W19-5008", + pages = "71--79", + biburl = {https://aclanthology.org/W19-5008.bib}, + bibsource = {https://aclanthology.org/W19-5008/} +} +``` diff --git a/bigbio/hub/hub_repos/monero/bigbiohub.py b/bigbio/hub/hub_repos/monero/bigbiohub.py new file mode 100644 index 00000000..a4792b4b --- /dev/null +++ b/bigbio/hub/hub_repos/monero/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/bigbio/hub/hub_repos/monero/monero.py b/bigbio/hub/hub_repos/monero/monero.py new file mode 100644 index 00000000..7280fdc6 --- /dev/null +++ b/bigbio/hub/hub_repos/monero/monero.py @@ -0,0 +1,291 @@ +# 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. + +import os +from pathlib import Path +from typing import Any, Dict, List, Tuple + +import datasets + +from .bigbiohub import BigBioConfig, Tasks, kb_features + +_LANGUAGES = ["Romanian"] +_PUBMED = False +_LOCAL = False + +_CITATION = """\ +@inproceedings{, + title = {{M}o{NER}o: a Biomedical Gold Standard Corpus for the {R}omanian Language}, + author = {Mitrofan, Maria and Barbu Mititelu, Verginica and Mitrofan, Grigorina}, + booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task", + month = aug, + year = "2019", + address = "Florence, Italy", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W19-5008", + doi = "10.18653/v1/W19-5008", + pages = "71--79", + biburl = {https://aclanthology.org/W19-5008.bib}, + bibsource = {https://aclanthology.org/W19-5008/} +} +""" + +_DATASETNAME = "monero" +_DISPLAYNAME = "MoNERo" + +_DESCRIPTION = """\ +MoNERo: a Biomedical Gold Standard Corpus for the Romanian Language for part of speech tagging and named \ +entity recognition. +""" + +_HOMEPAGE = "https://www.racai.ro/en/tools/text/" +_LICENSE = "CC_BY_SA_4p0" + +_URLS = { + # The original dataset is in 7z format hence I have downloaded and reuploded it as tar.gz format. + # Converted via the following command: + # curl -JLO https://www.racai.ro/media/MoNERo_2019.7z + # 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/8550757/MoNERo.tar.gz", +} + +_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] + +_SOURCE_VERSION = "1.0.0" +_BIGBIO_VERSION = "1.0.0" + + +class MoneroDataset(datasets.GeneratorBasedBuilder): + """MoNERo: a Biomedical Gold Standard Corpus for the Romanian Language for part of speech tagging + and named entity recognition.""" + + SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) + BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) + + BUILDER_CONFIGS = [ + BigBioConfig( + name=f"{_DATASETNAME}_source", + version=SOURCE_VERSION, + description=f"{_DATASETNAME} source schema", + schema="source", + subset_id=f"{_DATASETNAME}", + ), + BigBioConfig( + name=f"{_DATASETNAME}_bigbio_kb", + version=BIGBIO_VERSION, + description=f"{_DATASETNAME} BigBio schema", + schema="bigbio_kb", + subset_id=f"{_DATASETNAME}", + ), + ] + + DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" + + def _info(self) -> datasets.DatasetInfo: + if self.config.schema == "source": + features = datasets.Features( + { + "doc_id": datasets.Value("string"), + "tokens": [datasets.Value("string")], + "lemmas": [datasets.Value("string")], + "ner_tags": [datasets.Value("string")], + "pos_tags": [datasets.Value("string")], + } + ) + + elif self.config.schema == "bigbio_kb": + features = kb_features + else: + raise NotImplementedError(f"Schema {self.config.schema} not supported") + + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: + """Returns SplitGenerators.""" + urls = _URLS[_DATASETNAME] + data_dir = dl_manager.download_and_extract(urls) + + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": Path(os.path.join(data_dir, "MoNERo", "MoNERo.txt")), + "split": "train", + }, + ), + ] + [ + datasets.SplitGenerator( + name=split, + gen_kwargs={ + "filepath": Path(os.path.join(data_dir, "MoNERo", f"MoNERo_{split}.txt")), + "split": split, + }, + ) + for split in ["cardiology", "endocrinology", "diabetes"] + ] + + def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: + """Yields examples as (key, example) tuples.""" + if self.config.schema == "source": + for key, example in self._read_example_from_file(filepath): + yield key, example + + elif self.config.schema == "bigbio_kb": + for key, example in self._read_example_from_file_in_kb_schema(filepath): + yield key, example + + def _read_example_from_file(self, filepath: Path) -> Tuple[str, Dict]: + """Read examples from the given file in source schema""" + with filepath.open("r", encoding="utf8") as fp: + sequences = fp.read().split("\n\n") + + for i, seq in enumerate(sequences): + key = f"docid-{i}" + seq = [line.rstrip().split("\t") for line in seq.rstrip().splitlines()] + + # There are few lines which only have two columns. Skipping those. + seq = [line for line in seq if len(line) == 4] + tokens, lemmas, ner_tags, pos_tags = zip(*seq) + example = { + "doc_id": key, + "tokens": tokens, + "lemmas": lemmas, + "ner_tags": ner_tags, + "pos_tags": pos_tags, + } + yield key, example + + @staticmethod + def _assign_offsets(tokens: List[str]) -> List[Tuple[int, int]]: + """Compute token offsets from list of tokens""" + + offsets = [] + start = 0 + for t in tokens: + s = start + e = s + len(t) + offsets.append((s, e)) + start = e + 1 # Add one to include space. + + return offsets + + @staticmethod + def _extract_entities(ner_tags: List[str]) -> List[Dict]: + """Extract the entity token offsets / indices given the NER tags. + + Note: The dataset contains discontinuous entities, unfortunately, in some cases it's not transparent to + which entity a part (i.e., an I-Tag without having a B-Tag before) should be linked. In this implementation + we append the entity part to previous entity of that type. If there is no previous entity we construct + a new entity from the part. + """ + ner_tags = tuple(ner_tags) + ("O",) + entities = [] + stack = [] + is_discontinuation = False + + for index, ner_tag in enumerate(ner_tags): + if stack and (ner_tag == "O" or ner_tag.startswith("B-")): + entity_type, start_index = stack[0] + entity_type, end_index = stack[-1] + + if not is_discontinuation: + # Standard case - create a new entity + entities.append({"type": entity_type, "offsets": [(start_index, end_index)]}) + else: + # Try to append the offsets to the previous entity of the same type + prev_entity = None + for i in range(len(entities) - 1, 0, -1): + if entities[i]["type"] == entity_type: + prev_entity = entities[i] + break + + if prev_entity: + prev_entity["offsets"].append((start_index, end_index)) + else: + # If can't find a previous entity - create a new one + entities.append({"type": entity_type, "offsets": [(start_index, end_index)]}) + + stack = [] + is_discontinuation = False + + if ner_tag.startswith("I-") and len(stack) == 0 and len(entities) > 0: + # The corpus contains some discontinuous entities + is_discontinuation = True + + if ner_tag.startswith(("B-", "I-")): + _, entity_type = ner_tag.split("-", 1) + stack.append((entity_type, index)) + + return entities + + def _parse_example_to_kb_schema(self, example: Dict) -> Dict[str, Any]: + """Maps a source example to BigBio kb schema""" + + text = " ".join(example["tokens"]) + doc_id = example["doc_id"] + passages = [ + { + "id": f"{doc_id}-P0", + "type": "abstract", + "text": [text], + "offsets": [[0, len(text)]], + } + ] + + offsets = self._assign_offsets(example["tokens"]) + entities_with_token_indices = self._extract_entities(example["ner_tags"]) + + entities = [] + for i, entity_type_and_token_indices in enumerate(entities_with_token_indices): + entity_texts = [] + entity_offsets = [] + + for start_token, end_token in entity_type_and_token_indices["offsets"]: + start_offset, end_offset = offsets[start_token][0], offsets[end_token][1] + entity_offsets.append((start_offset, end_offset)) + entity_texts.append(text[start_offset:end_offset]) + + entity = { + "id": f"{doc_id}-E{i}", + "text": entity_texts, + "offsets": entity_offsets, + "type": entity_type_and_token_indices["type"], + "normalized": [], + } + 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]: + for key, example in self._read_example_from_file(filepath): + example = self._parse_example_to_kb_schema(example) + yield key, example