Update parquet files

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text
-# Image files - uncompressed
-*.bmp filter=lfs diff=lfs merge=lfs -text
-*.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-*.tiff filter=lfs diff=lfs merge=lfs -text
-# Image files - compressed
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.jpeg filter=lfs diff=lfs merge=lfs -text
-*.webp filter=lfs diff=lfs merge=lfs -text

bigbiohub.py

@@ -1,556 +0,0 @@
-from collections import defaultdict
-from dataclasses import dataclass
-from enum import Enum
-import logging
-from pathlib import Path
-from types import SimpleNamespace
-from typing import TYPE_CHECKING, Dict, Iterable, List, Tuple
-
-import datasets
-
-if TYPE_CHECKING:
-    import bioc
-
-logger = logging.getLogger(__name__)
-
-
-BigBioValues = SimpleNamespace(NULL="<BB_NULL_STR>")
-
-
-@dataclass
-class BigBioConfig(datasets.BuilderConfig):
-    """BuilderConfig for BigBio."""
-
-    name: str = None
-    version: datasets.Version = None
-    description: str = None
-    schema: str = None
-    subset_id: str = None
-
-
-class Tasks(Enum):
-    NAMED_ENTITY_RECOGNITION = "NER"
-    NAMED_ENTITY_DISAMBIGUATION = "NED"
-    EVENT_EXTRACTION = "EE"
-    RELATION_EXTRACTION = "RE"
-    COREFERENCE_RESOLUTION = "COREF"
-    QUESTION_ANSWERING = "QA"
-    TEXTUAL_ENTAILMENT = "TE"
-    SEMANTIC_SIMILARITY = "STS"
-    TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS"
-    PARAPHRASING = "PARA"
-    TRANSLATION = "TRANSL"
-    SUMMARIZATION = "SUM"
-    TEXT_CLASSIFICATION = "TXTCLASS"
-
-
-entailment_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "premise": datasets.Value("string"),
-        "hypothesis": datasets.Value("string"),
-        "label": datasets.Value("string"),
-    }
-)
-
-pairs_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "document_id": datasets.Value("string"),
-        "text_1": datasets.Value("string"),
-        "text_2": datasets.Value("string"),
-        "label": datasets.Value("string"),
-    }
-)
-
-qa_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "question_id": datasets.Value("string"),
-        "document_id": datasets.Value("string"),
-        "question": datasets.Value("string"),
-        "type": datasets.Value("string"),
-        "choices": [datasets.Value("string")],
-        "context": datasets.Value("string"),
-        "answer": datasets.Sequence(datasets.Value("string")),
-    }
-)
-
-text_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "document_id": datasets.Value("string"),
-        "text": datasets.Value("string"),
-        "labels": [datasets.Value("string")],
-    }
-)
-
-text2text_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "document_id": datasets.Value("string"),
-        "text_1": datasets.Value("string"),
-        "text_2": datasets.Value("string"),
-        "text_1_name": datasets.Value("string"),
-        "text_2_name": datasets.Value("string"),
-    }
-)
-
-kb_features = datasets.Features(
-    {
-        "id": datasets.Value("string"),
-        "document_id": datasets.Value("string"),
-        "passages": [
-            {
-                "id": datasets.Value("string"),
-                "type": datasets.Value("string"),
-                "text": datasets.Sequence(datasets.Value("string")),
-                "offsets": datasets.Sequence([datasets.Value("int32")]),
-            }
-        ],
-        "entities": [
-            {
-                "id": datasets.Value("string"),
-                "type": datasets.Value("string"),
-                "text": datasets.Sequence(datasets.Value("string")),
-                "offsets": datasets.Sequence([datasets.Value("int32")]),
-                "normalized": [
-                    {
-                        "db_name": datasets.Value("string"),
-                        "db_id": datasets.Value("string"),
-                    }
-                ],
-            }
-        ],
-        "events": [
-            {
-                "id": datasets.Value("string"),
-                "type": datasets.Value("string"),
-                # refers to the text_bound_annotation of the trigger
-                "trigger": {
-                    "text": datasets.Sequence(datasets.Value("string")),
-                    "offsets": datasets.Sequence([datasets.Value("int32")]),
-                },
-                "arguments": [
-                    {
-                        "role": datasets.Value("string"),
-                        "ref_id": datasets.Value("string"),
-                    }
-                ],
-            }
-        ],
-        "coreferences": [
-            {
-                "id": datasets.Value("string"),
-                "entity_ids": datasets.Sequence(datasets.Value("string")),
-            }
-        ],
-        "relations": [
-            {
-                "id": datasets.Value("string"),
-                "type": datasets.Value("string"),
-                "arg1_id": datasets.Value("string"),
-                "arg2_id": datasets.Value("string"),
-                "normalized": [
-                    {
-                        "db_name": datasets.Value("string"),
-                        "db_id": datasets.Value("string"),
-                    }
-                ],
-            }
-        ],
-    }
-)
-
-
-def get_texts_and_offsets_from_bioc_ann(ann: "bioc.BioCAnnotation") -> Tuple:
-
-    offsets = [(loc.offset, loc.offset + loc.length) for loc in ann.locations]
-
-    text = ann.text
-
-    if len(offsets) > 1:
-        i = 0
-        texts = []
-        for start, end in offsets:
-            chunk_len = end - start
-            texts.append(text[i : chunk_len + i])
-            i += chunk_len
-            while i < len(text) and text[i] == " ":
-                i += 1
-    else:
-        texts = [text]
-
-    return offsets, texts
-
-
-def remove_prefix(a: str, prefix: str) -> str:
-    if a.startswith(prefix):
-        a = a[len(prefix) :]
-    return a
-
-
-def parse_brat_file(
-    txt_file: Path,
-    annotation_file_suffixes: List[str] = None,
-    parse_notes: bool = False,
-) -> Dict:
-    """
-    Parse a brat file into the schema defined below.
-    `txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
-    Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
-    e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
-    Will include annotator notes, when `parse_notes == True`.
-    brat_features = datasets.Features(
-        {
-            "id": datasets.Value("string"),
-            "document_id": datasets.Value("string"),
-            "text": datasets.Value("string"),
-            "text_bound_annotations": [  # T line in brat, e.g. type or event trigger
-                {
-                    "offsets": datasets.Sequence([datasets.Value("int32")]),
-                    "text": datasets.Sequence(datasets.Value("string")),
-                    "type": datasets.Value("string"),
-                    "id": datasets.Value("string"),
-                }
-            ],
-            "events": [  # E line in brat
-                {
-                    "trigger": datasets.Value(
-                        "string"
-                    ),  # refers to the text_bound_annotation of the trigger,
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "arguments": datasets.Sequence(
-                        {
-                            "role": datasets.Value("string"),
-                            "ref_id": datasets.Value("string"),
-                        }
-                    ),
-                }
-            ],
-            "relations": [  # R line in brat
-                {
-                    "id": datasets.Value("string"),
-                    "head": {
-                        "ref_id": datasets.Value("string"),
-                        "role": datasets.Value("string"),
-                    },
-                    "tail": {
-                        "ref_id": datasets.Value("string"),
-                        "role": datasets.Value("string"),
-                    },
-                    "type": datasets.Value("string"),
-                }
-            ],
-            "equivalences": [  # Equiv line in brat
-                {
-                    "id": datasets.Value("string"),
-                    "ref_ids": datasets.Sequence(datasets.Value("string")),
-                }
-            ],
-            "attributes": [  # M or A lines in brat
-                {
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "ref_id": datasets.Value("string"),
-                    "value": datasets.Value("string"),
-                }
-            ],
-            "normalizations": [  # N lines in brat
-                {
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "ref_id": datasets.Value("string"),
-                    "resource_name": datasets.Value(
-                        "string"
-                    ),  # Name of the resource, e.g. "Wikipedia"
-                    "cuid": datasets.Value(
-                        "string"
-                    ),  # ID in the resource, e.g. 534366
-                    "text": datasets.Value(
-                        "string"
-                    ),  # Human readable description/name of the entity, e.g. "Barack Obama"
-                }
-            ],
-            ### OPTIONAL: Only included when `parse_notes == True`
-            "notes": [  # # lines in brat
-                {
-                    "id": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "ref_id": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                }
-            ],
-        },
-        )
-    """
-
-    example = {}
-    example["document_id"] = txt_file.with_suffix("").name
-    with txt_file.open() as f:
-        example["text"] = f.read()
-
-    # If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
-    # for event extraction
-    if annotation_file_suffixes is None:
-        annotation_file_suffixes = [".a1", ".a2", ".ann"]
-
-    if len(annotation_file_suffixes) == 0:
-        raise AssertionError(
-            "At least one suffix for the to-be-read annotation files should be given!"
-        )
-
-    ann_lines = []
-    for suffix in annotation_file_suffixes:
-        annotation_file = txt_file.with_suffix(suffix)
-        if annotation_file.exists():
-            with annotation_file.open() as f:
-                ann_lines.extend(f.readlines())
-
-    example["text_bound_annotations"] = []
-    example["events"] = []
-    example["relations"] = []
-    example["equivalences"] = []
-    example["attributes"] = []
-    example["normalizations"] = []
-
-    if parse_notes:
-        example["notes"] = []
-
-    for line in ann_lines:
-        line = line.strip()
-        if not line:
-            continue
-
-        if line.startswith("T"):  # Text bound
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["type"] = fields[1].split()[0]
-            ann["offsets"] = []
-            span_str = remove_prefix(fields[1], (ann["type"] + " "))
-            text = fields[2]
-            for span in span_str.split(";"):
-                start, end = span.split()
-                ann["offsets"].append([int(start), int(end)])
-
-            # Heuristically split text of discontiguous entities into chunks
-            ann["text"] = []
-            if len(ann["offsets"]) > 1:
-                i = 0
-                for start, end in ann["offsets"]:
-                    chunk_len = end - start
-                    ann["text"].append(text[i : chunk_len + i])
-                    i += chunk_len
-                    while i < len(text) and text[i] == " ":
-                        i += 1
-            else:
-                ann["text"] = [text]
-
-            example["text_bound_annotations"].append(ann)
-
-        elif line.startswith("E"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-
-            ann["type"], ann["trigger"] = fields[1].split()[0].split(":")
-
-            ann["arguments"] = []
-            for role_ref_id in fields[1].split()[1:]:
-                argument = {
-                    "role": (role_ref_id.split(":"))[0],
-                    "ref_id": (role_ref_id.split(":"))[1],
-                }
-                ann["arguments"].append(argument)
-
-            example["events"].append(ann)
-
-        elif line.startswith("R"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["type"] = fields[1].split()[0]
-
-            ann["head"] = {
-                "role": fields[1].split()[1].split(":")[0],
-                "ref_id": fields[1].split()[1].split(":")[1],
-            }
-            ann["tail"] = {
-                "role": fields[1].split()[2].split(":")[0],
-                "ref_id": fields[1].split()[2].split(":")[1],
-            }
-
-            example["relations"].append(ann)
-
-        # '*' seems to be the legacy way to mark equivalences,
-        # but I couldn't find any info on the current way
-        # this might have to be adapted dependent on the brat version
-        # of the annotation
-        elif line.startswith("*"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["ref_ids"] = fields[1].split()[1:]
-
-            example["equivalences"].append(ann)
-
-        elif line.startswith("A") or line.startswith("M"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-
-            info = fields[1].split()
-            ann["type"] = info[0]
-            ann["ref_id"] = info[1]
-
-            if len(info) > 2:
-                ann["value"] = info[2]
-            else:
-                ann["value"] = ""
-
-            example["attributes"].append(ann)
-
-        elif line.startswith("N"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["text"] = fields[2]
-
-            info = fields[1].split()
-
-            ann["type"] = info[0]
-            ann["ref_id"] = info[1]
-            ann["resource_name"] = info[2].split(":")[0]
-            ann["cuid"] = info[2].split(":")[1]
-            example["normalizations"].append(ann)
-
-        elif parse_notes and line.startswith("#"):
-            ann = {}
-            fields = line.split("\t")
-
-            ann["id"] = fields[0]
-            ann["text"] = fields[2] if len(fields) == 3 else BigBioValues.NULL
-
-            info = fields[1].split()
-
-            ann["type"] = info[0]
-            ann["ref_id"] = info[1]
-            example["notes"].append(ann)
-
-    return example
-
-
-def brat_parse_to_bigbio_kb(brat_parse: Dict) -> Dict:
-    """
-    Transform a brat parse (conforming to the standard brat schema) obtained with
-    `parse_brat_file` into a dictionary conforming to the `bigbio-kb` schema (as defined in ../schemas/kb.py)
-    :param brat_parse:
-    """
-
-    unified_example = {}
-
-    # Prefix all ids with document id to ensure global uniqueness,
-    # because brat ids are only unique within their document
-    id_prefix = brat_parse["document_id"] + "_"
-
-    # identical
-    unified_example["document_id"] = brat_parse["document_id"]
-    unified_example["passages"] = [
-        {
-            "id": id_prefix + "_text",
-            "type": "abstract",
-            "text": [brat_parse["text"]],
-            "offsets": [[0, len(brat_parse["text"])]],
-        }
-    ]
-
-    # get normalizations
-    ref_id_to_normalizations = defaultdict(list)
-    for normalization in brat_parse["normalizations"]:
-        ref_id_to_normalizations[normalization["ref_id"]].append(
-            {
-                "db_name": normalization["resource_name"],
-                "db_id": normalization["cuid"],
-            }
-        )
-
-    # separate entities and event triggers
-    unified_example["events"] = []
-    non_event_ann = brat_parse["text_bound_annotations"].copy()
-    for event in brat_parse["events"]:
-        event = event.copy()
-        event["id"] = id_prefix + event["id"]
-        trigger = next(
-            tr
-            for tr in brat_parse["text_bound_annotations"]
-            if tr["id"] == event["trigger"]
-        )
-        if trigger in non_event_ann:
-            non_event_ann.remove(trigger)
-        event["trigger"] = {
-            "text": trigger["text"].copy(),
-            "offsets": trigger["offsets"].copy(),
-        }
-        for argument in event["arguments"]:
-            argument["ref_id"] = id_prefix + argument["ref_id"]
-
-        unified_example["events"].append(event)
-
-    unified_example["entities"] = []
-    anno_ids = [ref_id["id"] for ref_id in non_event_ann]
-    for ann in non_event_ann:
-        entity_ann = ann.copy()
-        entity_ann["id"] = id_prefix + entity_ann["id"]
-        entity_ann["normalized"] = ref_id_to_normalizations[ann["id"]]
-        unified_example["entities"].append(entity_ann)
-
-    # massage relations
-    unified_example["relations"] = []
-    skipped_relations = set()
-    for ann in brat_parse["relations"]:
-        if (
-            ann["head"]["ref_id"] not in anno_ids
-            or ann["tail"]["ref_id"] not in anno_ids
-        ):
-            skipped_relations.add(ann["id"])
-            continue
-        unified_example["relations"].append(
-            {
-                "arg1_id": id_prefix + ann["head"]["ref_id"],
-                "arg2_id": id_prefix + ann["tail"]["ref_id"],
-                "id": id_prefix + ann["id"],
-                "type": ann["type"],
-                "normalized": [],
-            }
-        )
-    if len(skipped_relations) > 0:
-        example_id = brat_parse["document_id"]
-        logger.info(
-            f"Example:{example_id}: The `bigbio_kb` schema allows `relations` only between entities."
-            f" Skip (for now): "
-            f"{list(skipped_relations)}"
-        )
-
-    # get coreferences
-    unified_example["coreferences"] = []
-    for i, ann in enumerate(brat_parse["equivalences"], start=1):
-        is_entity_cluster = True
-        for ref_id in ann["ref_ids"]:
-            if not ref_id.startswith("T"):  # not textbound -> no entity
-                is_entity_cluster = False
-            elif ref_id not in anno_ids:  # event trigger -> no entity
-                is_entity_cluster = False
-        if is_entity_cluster:
-            entity_ids = [id_prefix + i for i in ann["ref_ids"]]
-            unified_example["coreferences"].append(
-                {"id": id_prefix + str(i), "entity_ids": entity_ids}
-            )
-    return unified_example

lll.py

@@ -1,329 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Datasets Authors and Simon Ott, github: nomisto
-#
-# 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.
-
-"""
-The LLL05 challenge task is to learn rules to extract protein/gene interactions from biology abstracts from the Medline
-bibliography database. The goal of the challenge is to test the ability of the participating IE systems to identify the
-interactions and the gene/proteins that interact. The participants will test their IE patterns on a test set with the
-aim of extracting the correct agent and target.The challenge focuses on information extraction of gene interactions in
-Bacillus subtilis. Extracting gene interaction is the most popular event IE task in biology. Bacillus subtilis (Bs) is
-a model bacterium and many papers have been published on direct gene interactions involved in sporulation. The gene
-interactions are generally mentioned in the abstract and the full text of the paper is not needed. Extracting gene
-interaction means, extracting the agent (proteins) and the target (genes) of all couples of genic interactions from
-sentences.
-"""
-
-# NOTE:
-# word stop offsets are increased by one to be consistent with python slicing.
-# test set does not include entity relation information
-
-import itertools as it
-from typing import List
-
-import datasets
-
-from .bigbiohub import kb_features
-from .bigbiohub import BigBioConfig
-from .bigbiohub import Tasks
-from .bigbiohub import BigBioValues
-
-_LANGUAGES = ['English']
-_PUBMED = True
-_LOCAL = False
-_CITATION = """\
-    @article{article,
-    author = {Nédellec, C.},
-    year = {2005},
-    month = {01},
-    pages = {},
-    title = {Learning Language in Logic - Genic Interaction Extraction Challenge},
-    journal = {Proceedings of the Learning Language in Logic 2005 Workshop at the \
-        International Conference on Machine Learning}
-}
-"""
-
-_DATASETNAME = "lll"
-_DISPLAYNAME = "LLL05"
-
-_DESCRIPTION = """\
-The LLL05 challenge task is to learn rules to extract protein/gene interactions from biology abstracts from the Medline
-bibliography database. The goal of the challenge is to test the ability of the participating IE systems to identify the
-interactions and the gene/proteins that interact. The participants will test their IE patterns on a test set with the
-aim of extracting the correct agent and target.The challenge focuses on information extraction of gene interactions in
-Bacillus subtilis. Extracting gene interaction is the most popular event IE task in biology. Bacillus subtilis (Bs) is
-a model bacterium and many papers have been published on direct gene interactions involved in sporulation. The gene
-interactions are generally mentioned in the abstract and the full text of the paper is not needed. Extracting gene
-interaction means, extracting the agent (proteins) and the target (genes) of all couples of genic interactions from
-sentences.
-"""
-
-_HOMEPAGE = "http://genome.jouy.inra.fr/texte/LLLchallenge"
-
-_LICENSE = 'License information unavailable'
-
-_URLS = {
-    _DATASETNAME: [
-        "http://genome.jouy.inra.fr/texte/LLLchallenge/data/LLLChalenge05/data/train/task2/genic_interaction_linguistic_data.txt",  # noqa
-        "http://genome.jouy.inra.fr/texte/LLLchallenge/data/LLLChalenge05/data/train/task2/genic_interaction_linguistic_data_coref.txt",  # noqa
-        "http://genome.jouy.inra.fr/texte/LLLchallenge/data/LLLChalenge05/data/test/task2/enriched_test_data.txt",  # noqa
-    ]
-}
-
-_SUPPORTED_TASKS = [Tasks.RELATION_EXTRACTION]
-
-_SOURCE_VERSION = "1.0.0"
-
-_BIGBIO_VERSION = "1.0.0"
-
-
-class LLLDataset(datasets.GeneratorBasedBuilder):
-    """LLL dataset for gene interaction extraction (RE)"""
-
-    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
-
-    BUILDER_CONFIGS = [
-        BigBioConfig(
-            name="lll_source",
-            version=SOURCE_VERSION,
-            description="LLL source schema",
-            schema="source",
-            subset_id="lll",
-        ),
-        BigBioConfig(
-            name="lll_bigbio_kb",
-            version=BIGBIO_VERSION,
-            description="LLL BigBio schema",
-            schema="bigbio_kb",
-            subset_id="lll",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "lll_source"
-
-    def _info(self) -> datasets.DatasetInfo:
-
-        if self.config.schema == "source":
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "sentence": datasets.Value("string"),
-                    "words": [
-                        {
-                            "id": datasets.Value("string"),
-                            "text": datasets.Value("string"),
-                            "offsets": datasets.Sequence(datasets.Value("int32")),
-                        }
-                    ],
-                    "genic_interactions": [
-                        {
-                            "ref_id1": datasets.Value("string"),
-                            "ref_id2": datasets.Value("string"),
-                        }
-                    ],
-                    "agents": [
-                        {
-                            "ref_id": datasets.Value("string"),
-                        }
-                    ],
-                    "targets": [
-                        {
-                            "ref_id": datasets.Value("string"),
-                        }
-                    ],
-                    "lemmas": [
-                        {
-                            "ref_id": datasets.Value("string"),
-                            "lemma": datasets.Value("string"),
-                        }
-                    ],
-                    "syntactic_relations": [
-                        {
-                            "type": datasets.Value("string"),
-                            "ref_id1": datasets.Value("string"),
-                            "ref_id2": datasets.Value("string"),
-                        }
-                    ],
-                }
-            )
-
-        elif self.config.schema == "bigbio_kb":
-            features = kb_features
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=str(_LICENSE),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
-
-        urls = _URLS[_DATASETNAME]
-        train_path, train_coref_path, test_path = dl_manager.download_and_extract(urls)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "data_paths": [train_path, train_coref_path],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"data_paths": [test_path], "split": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, data_paths, split):
-
-        if self.config.schema == "source":
-            for path in data_paths:
-                with open(path, encoding="utf8") as documents:
-                    for document in self._generate_parsed_documents(documents, split):
-                        yield document["id"], document
-
-        elif self.config.schema == "bigbio_kb":
-            uid = it.count(0)
-            for path in data_paths:
-                with open(path, encoding="utf8") as documents:
-                    for document in self._generate_parsed_documents(documents, split):
-                        document_ = {}
-                        document_["id"] = next(uid)
-                        document_["document_id"] = document["id"]
-
-                        document_["passages"] = [
-                            {
-                                "id": next(uid),
-                                "type": BigBioValues.NULL,
-                                "text": [document["sentence"]],
-                                "offsets": [[0, len(document["sentence"])]],
-                            }
-                        ]
-
-                        id_to_word = {i["id"]: i for i in document["words"]}
-                        document_["entities"] = []
-                        for agent in document["agents"]:
-                            word = id_to_word[agent["ref_id"]]
-                            document_["entities"].append(
-                                {
-                                    "id": f"{document_['id']}-agent-{word['id']}",
-                                    "type": "agent",
-                                    "text": [word["text"]],
-                                    "offsets": [
-                                        [word["offsets"][0], word["offsets"][1]]
-                                    ],
-                                    "normalized": [],
-                                }
-                            )
-                        for agent in document["targets"]:
-                            word = id_to_word[agent["ref_id"]]
-                            document_["entities"].append(
-                                {
-                                    "id": f"{document_['id']}-target-{word['id']}",
-                                    "type": "target",
-                                    "text": [word["text"]],
-                                    "offsets": [
-                                        [word["offsets"][0], word["offsets"][1]]
-                                    ],
-                                    "normalized": [],
-                                }
-                            )
-
-                        document_["relations"] = [
-                            {
-                                "id": next(uid),
-                                "type": "genic_interaction",
-                                "arg1_id": f"{document_['id']}-agent-{relation['ref_id1']}",
-                                "arg2_id": f"{document_['id']}-target-{relation['ref_id2']}",
-                                "normalized": [],
-                            }
-                            for relation in document["genic_interactions"]
-                        ]
-
-                        document_["events"] = []
-                        document_["coreferences"] = []
-                        yield document_["document_id"], document_
-
-    def _generate_parsed_documents(self, fstream, split):
-        for raw_document in self._generate_raw_documents(fstream):
-            yield self._parse_document(raw_document, split)
-
-    def _generate_raw_documents(self, fstream):
-        raw_document = []
-        for line in fstream:
-            if "%" in line:
-                continue
-            elif line.strip():
-                raw_document.append(line.strip())
-            elif raw_document:
-                if raw_document:
-                    yield raw_document
-                raw_document = []
-        # needed for last document
-        if raw_document:
-            yield raw_document
-
-    def _parse_document(self, raw_document, split):
-        document = {}
-        for line in raw_document:
-            key, value = line.split("\t", 1)
-            if key in ["ID", "sentence"]:
-                document[key.lower()] = value
-            elif key in [
-                "words",
-                "genic_interactions",
-                "agents",
-                "targets",
-                "lemmas",
-                "syntactic_relations",
-            ]:
-                document[key.lower()] = self._parse_elements(value, key)
-            else:
-                raise NotImplementedError()
-
-        # Needed as testset does not contain agents, targets and genic_interactions (dataset was part of a challenge)
-        if split == "test":
-            document.setdefault("genic_interactions", [])
-            document.setdefault("agents", [])
-            document.setdefault("targets", [])
-
-        return document
-
-    def _parse_elements(self, values, type):
-        return [self._parse_element(atom, type) for atom in values.split("\t")]
-
-    def _parse_element(self, atom, type):
-        # Sorry for that abomination, parses the arguments from atoms like rel(arg1, ..., argn)
-        args = atom.split("(", 1)[1][:-1].split(",")
-        if type == "words":
-            # fix offsets for python slicing
-            return {
-                "id": args[0],
-                "text": args[1].strip("'"),
-                "offsets": [int(args[2]), int(args[3]) + 1],
-            }
-        elif type == "genic_interactions":
-            return {"ref_id1": args[0], "ref_id2": args[1]}
-        elif type == "agents":
-            return {"ref_id": args[0]}
-        elif type == "targets":
-            return {"ref_id": args[0]}
-        elif type == "lemmas":
-            return {"ref_id": args[0], "lemma": args[1].strip("'")}
-        elif type == "syntactic_relations":
-            return {"type": args[0].strip("'"), "ref_id1": args[1], "ref_id2": args[2]}

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