From d894d44e5327da19a400ba1fd80378f0dfddb750 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Thu, 2 Nov 2023 18:42:49 +0100 Subject: [PATCH] kickstart --- dataset_builders/hf/cdcp/cdcp.py | 168 + dataset_builders/pie/cdcp/cdcp.py | 158 + poetry.lock | 4356 +++++++++-------- pyproject.toml | 1 + src/document/__init__.py | 0 src/document/types.py | 73 + tests/dataset_builders/__init__.py | 0 tests/dataset_builders/pie/__init__.py | 0 tests/dataset_builders/pie/test_cdcp.py | 428 ++ .../fixtures/dataset_builders/cdcp_acl17.zip | Bin 0 -> 5635976 bytes 10 files changed, 3024 insertions(+), 2160 deletions(-) create mode 100644 dataset_builders/hf/cdcp/cdcp.py create mode 100644 dataset_builders/pie/cdcp/cdcp.py create mode 100644 src/document/__init__.py create mode 100644 src/document/types.py create mode 100644 tests/dataset_builders/__init__.py create mode 100644 tests/dataset_builders/pie/__init__.py create mode 100644 tests/dataset_builders/pie/test_cdcp.py create mode 100644 tests/fixtures/dataset_builders/cdcp_acl17.zip diff --git a/dataset_builders/hf/cdcp/cdcp.py b/dataset_builders/hf/cdcp/cdcp.py new file mode 100644 index 00000000..7defc07e --- /dev/null +++ b/dataset_builders/hf/cdcp/cdcp.py @@ -0,0 +1,168 @@ +"""The Cornell eRulemaking Corpus (CDCP) dataset for English Argumentation Mining.""" +import glob +import json +from os.path import abspath, isdir +from pathlib import Path + +import datasets + +_CITATION = """\ +@inproceedings{niculae-etal-2017-argument, + title = "Argument Mining with Structured {SVM}s and {RNN}s", + author = "Niculae, Vlad and + Park, Joonsuk and + Cardie, Claire", + booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = jul, + year = "2017", + address = "Vancouver, Canada", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/P17-1091", + doi = "10.18653/v1/P17-1091", + pages = "985--995", + abstract = "We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20{\\%} of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.", +} +""" + +_DESCRIPTION = "The CDCP dataset for English Argumentation Mining" + +_HOMEPAGE = "" + +_LICENSE = "" + + +# The HuggingFace dataset library don't host the datasets but only point to the original files +# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) +_URL = "https://facultystaff.richmond.edu/~jpark/data/cdcp_acl17.zip" + +_VERSION = datasets.Version("1.0.0") + +_SPAN_CLASS_LABELS = ["fact", "policy", "reference", "testimony", "value"] +_RELATION_CLASS_LABELS = ["evidence", "reason"] + + +class CDCP(datasets.GeneratorBasedBuilder): + """CDCP is a argumentation mining dataset.""" + + BUILDER_CONFIGS = [datasets.BuilderConfig(name="default")] + + DEFAULT_CONFIG_NAME = "default" # type: ignore + + def _info(self): + features = datasets.Features( + { + "id": datasets.Value("string"), + "text": datasets.Value("string"), + "propositions": datasets.Sequence( + { + "start": datasets.Value("int32"), + "end": datasets.Value("int32"), + "label": datasets.ClassLabel(names=_SPAN_CLASS_LABELS), + # urls are replaced with the string "__URL__" in the text. This contains the original url. + "url": datasets.Value("string"), + } + ), + "relations": datasets.Sequence( + { + "head": datasets.Value("int32"), + "tail": datasets.Value("int32"), + "label": datasets.ClassLabel(names=_RELATION_CLASS_LABELS), + } + ), + } + ) + + return datasets.DatasetInfo( + # This is the description that will appear on the datasets page. + description=_DESCRIPTION, + # This defines the different columns of the dataset and their types + features=features, # Here we define them above because they are different between the two configurations + # If there's a common (input, target) tuple from the features, + # specify them here. They'll be used if as_supervised=True in + # builder.as_dataset. + supervised_keys=None, + # Homepage of the dataset for documentation + homepage=_HOMEPAGE, + # License for the dataset if available + license=_LICENSE, + # Citation for the dataset + citation=_CITATION, + ) + + def _split_generators(self, dl_manager): + """Returns SplitGenerators.""" + # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name + + # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs + # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. + # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive + + if dl_manager.manual_dir is not None: + base_path = abspath(dl_manager.manual_dir) + if not isdir(base_path): + base_path = dl_manager.extract(base_path) + else: + base_path = dl_manager.download_and_extract(_URL) + base_path = Path(base_path) / "cdcp" + + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, gen_kwargs={"path": base_path / "train"} + ), + datasets.SplitGenerator( + name=datasets.Split.TEST, gen_kwargs={"path": base_path / "test"} + ), + ] + + def _generate_examples(self, path): + """Yields examples.""" + # This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method. + # It is in charge of opening the given file and yielding (key, example) tuples from the dataset + # The key is not important, it's more here for legacy reason (legacy from tfds) + + _id = 0 + text_file_names = sorted(glob.glob(f"{path}/*.txt")) + for text_file_name in text_file_names: + txt_fn = Path(text_file_name) + ann_fn = txt_fn.with_suffix(".ann.json") + with open(txt_fn, encoding="utf-8") as f: + text = f.read() + with open(ann_fn, encoding="utf-8") as f: + annotations = json.load(f) + # example content of annotations: + # { + # 'evidences': [[[8, 8], 7]], + # 'prop_labels': ['testimony', 'testimony', 'value'], + # 'prop_offsets': [[0, 114], [114, 209], [209, 235]], + # 'reasons': [[[2, 2], 1], [ 0, 0], 2]], + # 'evidences': [[[2, 2], 1], [ 0, 0], 2]], + # 'url': { + # "3": "http://usa.visa.com/personal/using_visa/checkout_fees/", + # "4": "http://usa.visa.com/download/merchants/surcharging-faq-by-merchants.pdf" + # } + # } + propositions = [ + { + "start": start, + "end": end, + "label": label, + "url": annotations["url"].get(str(idx), ""), + } + for idx, ((start, end), label) in enumerate( + zip(annotations["prop_offsets"], annotations["prop_labels"]) + ) + ] + relations = [] + for (tail_first_idx, tail_last_idx), head_idx in annotations["evidences"]: + for tail_idx in range(tail_first_idx, tail_last_idx + 1): + relations.append({"head": head_idx, "tail": tail_idx, "label": "evidence"}) + for (tail_first_idx, tail_last_idx), head_idx in annotations["reasons"]: + for tail_idx in range(tail_first_idx, tail_last_idx + 1): + relations.append({"head": head_idx, "tail": tail_idx, "label": "reason"}) + yield _id, { + "id": txt_fn.stem, + "text": text, + "propositions": propositions, + "relations": relations, + } + _id += 1 diff --git a/dataset_builders/pie/cdcp/cdcp.py b/dataset_builders/pie/cdcp/cdcp.py new file mode 100644 index 00000000..28af654b --- /dev/null +++ b/dataset_builders/pie/cdcp/cdcp.py @@ -0,0 +1,158 @@ +import dataclasses +import logging +from typing import Any, Callable, Dict, List, Optional + +import datasets +import pytorch_ie.data.builder +from pie_utils.document.processors.text_span_trimmer import trim_text_spans +from pytorch_ie.annotations import BinaryRelation, LabeledSpan +from pytorch_ie.core import Annotation, AnnotationList, Document, annotation_field +from pytorch_ie.documents import TextDocumentWithLabeledSpansAndBinaryRelations + + +log = logging.getLogger(__name__) + + +def dl2ld(dict_of_lists): + return [dict(zip(dict_of_lists, t)) for t in zip(*dict_of_lists.values())] + + +def ld2dl(list_of_dicts, keys: Optional[List[str]] = None, as_list: bool = False): + if keys is None: + keys = list_of_dicts[0].keys() + if as_list: + return [[d[k] for d in list_of_dicts] for k in keys] + else: + return {k: [d[k] for d in list_of_dicts] for k in keys} + + +@dataclasses.dataclass(frozen=True) +class Attribute(Annotation): + value: str + annotation: Annotation + + +@dataclasses.dataclass +class CDCPDocument(Document): + text: str + id: Optional[str] = None + metadata: Dict[str, Any] = dataclasses.field(default_factory=dict) + propositions: AnnotationList[LabeledSpan] = annotation_field(target="text") + relations: AnnotationList[BinaryRelation] = annotation_field(target="propositions") + urls: AnnotationList[Attribute] = annotation_field(target="propositions") + + +def example_to_document( + example: Dict[str, Any], + relation_int2str: Callable[[int], str], + proposition_int2str: Callable[[int], str], +): + document = CDCPDocument(id=example["id"], text=example["text"]) + for proposition_dict in dl2ld(example["propositions"]): + proposition = LabeledSpan( + start=proposition_dict["start"], + end=proposition_dict["end"], + label=proposition_int2str(proposition_dict["label"]), + ) + document.propositions.append(proposition) + if proposition_dict.get("url", "") != "": + url = Attribute(annotation=proposition, value=proposition_dict["url"]) + document.urls.append(url) + + for relation_dict in dl2ld(example["relations"]): + relation = BinaryRelation( + head=document.propositions[relation_dict["head"]], + tail=document.propositions[relation_dict["tail"]], + label=relation_int2str(relation_dict["label"]), + ) + document.relations.append(relation) + + return document + + +def document_to_example( + document: CDCPDocument, + relation_str2int: Callable[[str], int], + proposition_str2int: Callable[[str], int], +) -> Dict[str, Any]: + result = {"id": document.id, "text": document.text} + proposition2dict = {} + proposition2idx = {} + for idx, proposition in enumerate(document.propositions): + proposition2dict[proposition] = { + "start": proposition.start, + "end": proposition.end, + "label": proposition_str2int(proposition.label), + "url": "", + } + proposition2idx[proposition] = idx + for url in document.urls: + proposition2dict[url.annotation]["url"] = url.value + + result["propositions"] = ld2dl( + proposition2dict.values(), keys=["start", "end", "label", "url"] + ) + + relations = [ + { + "head": proposition2idx[relation.head], + "tail": proposition2idx[relation.tail], + "label": relation_str2int(relation.label), + } + for relation in document.relations + ] + result["relations"] = ld2dl(relations, keys=["head", "tail", "label"]) + + return result + + +def convert_to_text_document_with_labeled_spans_and_binary_relations( + document: CDCPDocument, + verbose: bool = True, +) -> TextDocumentWithLabeledSpansAndBinaryRelations: + doc_simplified = document.as_type( + TextDocumentWithLabeledSpansAndBinaryRelations, + field_mapping={"propositions": "labeled_spans", "relations": "binary_relations"}, + ) + result = trim_text_spans( + doc_simplified, + layer="labeled_spans", + verbose=verbose, + ) + return result + + +class CDCPConfig(datasets.BuilderConfig): + """BuilderConfig for CDCP.""" + + def __init__(self, **kwargs): + """BuilderConfig for CDCP. + Args: + **kwargs: keyword arguments forwarded to super. + """ + super().__init__(**kwargs) + + +class CDCP(pytorch_ie.data.builder.GeneratorBasedBuilder): + DOCUMENT_TYPE = CDCPDocument + + DOCUMENT_CONVERTERS = { + TextDocumentWithLabeledSpansAndBinaryRelations: convert_to_text_document_with_labeled_spans_and_binary_relations + } + + BASE_DATASET_PATH = "DFKI-SLT/cdcp" + + BUILDER_CONFIGS = [datasets.BuilderConfig(name="default")] + + DEFAULT_CONFIG_NAME = "default" # type: ignore + + def _generate_document_kwargs(self, dataset): + return { + "relation_int2str": 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pytorch-ie = ">=0.26.0,<0.27.0" +pie-utils = { git = "https://github.com/arnebinder/pie-utils.git", tag = "v0.7.0"} [tool.poetry.group.dev.dependencies] torch = {version = "^2.1.0+cpu", source = "pytorch"} diff --git a/src/document/__init__.py b/src/document/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/document/types.py b/src/document/types.py new file mode 100644 index 00000000..e10cc619 --- /dev/null +++ b/src/document/types.py @@ -0,0 +1,73 @@ +import dataclasses +import logging +from typing import Any, Dict, Optional + +from pytorch_ie.annotations import ( + BinaryRelation, + LabeledMultiSpan, + LabeledSpan, + Span, + _post_init_single_label, +) +from pytorch_ie.core import Annotation, AnnotationList, Document, annotation_field +from pytorch_ie.documents import TextBasedDocument, TokenBasedDocument + +logger = logging.getLogger(__name__) + + +# ========================= Annotation Types ========================= # + + +@dataclasses.dataclass(eq=True, frozen=True) +class Attribute(Annotation): + target_annotation: Annotation + label: str + value: Optional[str] = None + score: float = 1.0 + + def __post_init__(self) -> None: + _post_init_single_label(self) + + +# ========================= Document Types ========================= # + + +@dataclasses.dataclass +class TokenDocumentWithLabeledSpans(TokenBasedDocument): + labeled_spans: AnnotationList[LabeledSpan] = annotation_field(target="tokens") + + +@dataclasses.dataclass +class TokenDocumentWithLabeledSpansAndBinaryRelations(TokenDocumentWithLabeledSpans): + binary_relations: AnnotationList[BinaryRelation] = annotation_field(target="labeled_spans") + + +@dataclasses.dataclass +class TokenDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions( + TokenDocumentWithLabeledSpansAndBinaryRelations +): + labeled_partitions: AnnotationList[LabeledSpan] = annotation_field(target="tokens") + + +@dataclasses.dataclass +class BratDocument(Document): + # copied from https://huggingface.co/datasets/pie/brat/blob/main/brat.py + text: str + id: Optional[str] = None + metadata: Dict[str, Any] = dataclasses.field(default_factory=dict) + spans: AnnotationList[LabeledMultiSpan] = annotation_field(target="text") + relations: AnnotationList[BinaryRelation] = annotation_field(target="spans") + span_attributions: AnnotationList[Attribute] = annotation_field(target="spans") + relation_attributions: AnnotationList[Attribute] = annotation_field(target="relations") + + +@dataclasses.dataclass +class BratDocumentWithMergedSpans(Document): + # copied from https://huggingface.co/datasets/pie/brat/blob/main/brat.py + text: str + id: Optional[str] = None + metadata: Dict[str, Any] = dataclasses.field(default_factory=dict) + spans: AnnotationList[LabeledSpan] = annotation_field(target="text") + relations: AnnotationList[BinaryRelation] = annotation_field(target="spans") + span_attributions: AnnotationList[Attribute] = annotation_field(target="spans") + relation_attributions: AnnotationList[Attribute] = annotation_field(target="relations") diff --git a/tests/dataset_builders/__init__.py b/tests/dataset_builders/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/dataset_builders/pie/__init__.py b/tests/dataset_builders/pie/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/dataset_builders/pie/test_cdcp.py b/tests/dataset_builders/pie/test_cdcp.py new file mode 100644 index 00000000..efd21202 --- /dev/null +++ b/tests/dataset_builders/pie/test_cdcp.py @@ -0,0 +1,428 @@ +import dataclasses +from typing import List + +import pytest +from datasets import disable_caching, load_dataset +from pytorch_ie import DatasetDict, tokenize_document +from pytorch_ie.annotations import LabeledSpan +from pytorch_ie.core import AnnotationList, Document, annotation_field +from pytorch_ie.documents import TextBasedDocument, TextDocumentWithLabeledSpansAndBinaryRelations +from transformers import AutoTokenizer, PreTrainedTokenizer + +from dataset_builders.pie.cdcp.cdcp import ( + CDCPDocument, + convert_to_text_document_with_labeled_spans_and_binary_relations, + document_to_example, + example_to_document, CDCP, +) +from tests import FIXTURES_ROOT +from tests.dataset_builders.common import _deep_compare + +from src.document.types import TokenDocumentWithLabeledSpansAndBinaryRelations + +disable_caching() + +DATASET_NAME = "cdcp" +SPLIT_SIZES = {"train": 581, "test": 150} +#HF_DATASET_PATH = "DFKI-SLT/cdcp" +HF_DATASET_PATH = CDCP.BASE_DATASET_PATH +PIE_DATASET_PATH = "pie/cdcp" +DATA_PATH = FIXTURES_ROOT / "dataset_builders" / "cdcp_acl17.zip" + +HF_EXAMPLE_00195 = { + "id": "00195", + "text": "State and local court rules sometimes make default judgments much more likely. For example, " + "when a person who allegedly owes a debt is told to come to court on a work day, they may be " + "forced to choose between a default judgment and their job. I urge the CFPB to find practices " + "that involve scheduling hearings at inconvenient times unfair, deceptive, and abusive, or " + "inconsistent with 1692i.", + "propositions": { + "start": [0, 78, 242], + "end": [78, 242, 391], + "label": [4, 4, 1], + "url": ["", "", ""], + }, + "relations": {"head": [0, 2], "tail": [1, 0], "label": [1, 1]}, +} + + +HF_EXAMPLE_00194 = { + "id": "00194", + "text": "Recently, courts have held that debt collectors can escape 1692i's venue provisions entirely " + "by pursuing debt collection through arbitration instead. As the NAF studies reflect, arbitration " + "has not proven a satisfactory alternative. I urge the CFPB to include in a rule language " + "interpreting 1692i as requiring debt collectors to proceed in court, not through " + "largely-unregulated arbitral forums.", + "propositions": { + "start": [0, 149, 232], + "end": [149, 232, 396], + "label": [0, 4, 1], + "url": ["", "", ""], + }, + "relations": {"head": [2], "tail": [1], "label": [1]}, +} + + +@pytest.fixture(scope="module", params=["train", "test"]) +def split(request): + return request.param + + +@pytest.fixture(scope="module") +def hf_dataset(): + return load_dataset(str(HF_DATASET_PATH), data_dir=DATA_PATH) + + +def test_hf_dataset(hf_dataset): + assert hf_dataset is not None + assert {name: len(ds) for name, ds in hf_dataset.items()} == SPLIT_SIZES + + +@pytest.fixture(scope="module") +def hf_example(hf_dataset, split): + return hf_dataset[split][0] + + +def test_hf_example(hf_example, split): + assert hf_example is not None + if split == "train": + assert hf_example == HF_EXAMPLE_00195 + elif split == "test": + assert hf_example == HF_EXAMPLE_00194 + else: + raise ValueError(f"Unknown split: {split}") + + +@pytest.fixture(scope="module") +def generate_document_kwargs(hf_dataset, split): + return { + "relation_int2str": hf_dataset[split].features["relations"].feature["label"].int2str, + "proposition_int2str": hf_dataset[split].features["propositions"].feature["label"].int2str, + } + + +@pytest.fixture(scope="module") +def convert_back_kwargs(hf_dataset, split): + return { + "relation_str2int": hf_dataset[split].features["relations"].feature["label"].str2int, + "proposition_str2int": hf_dataset[split].features["propositions"].feature["label"].str2int, + } + + +def test_example_to_document(hf_example, generate_document_kwargs): + doc = example_to_document(hf_example, **generate_document_kwargs) + assert doc is not None + + +def test_example_to_document_and_back(hf_example, generate_document_kwargs, convert_back_kwargs): + doc = example_to_document(hf_example, **generate_document_kwargs) + hf_example_back = document_to_example(doc, **convert_back_kwargs) + _deep_compare( + obj=hf_example_back, + obj_expected=hf_example, + ) + + +def _assert_no_span_overlap(document: Document, text_field: str, span_layer: str): + spans = document[span_layer] + text = getattr(document, text_field) + seq = [None] * len(text) + for span in spans: + assert seq[span.start : span.end] == [None] * len(text[span.start : span.end]) + seq[span.start : span.end] = text[span.start : span.end] + + +def test_assert_no_span_overlap(): + @dataclasses.dataclass + class TextDocumentWithEntities(TextBasedDocument): + entities: AnnotationList[LabeledSpan] = annotation_field(target="text") + + doc0 = TextDocumentWithEntities(text="abcdefghijklmnop") + doc0.entities.append(LabeledSpan(start=0, end=4, label="A")) + doc0.entities.append(LabeledSpan(start=4, end=6, label="B")) + + # this should work + _assert_no_span_overlap(document=doc0, text_field="text", span_layer="entities") + + doc1 = TextDocumentWithEntities(text="abcdefghijklmnop") + doc1.entities.append(LabeledSpan(start=0, end=4, label="A")) + doc1.entities.append(LabeledSpan(start=2, end=6, label="B")) + + # this should fail + with pytest.raises(AssertionError): + _assert_no_span_overlap(document=doc1, text_field="text", span_layer="entities") + + +def test_example_to_document_and_back_all( + hf_dataset, generate_document_kwargs, convert_back_kwargs, split +): + for hf_ex in hf_dataset[split]: + doc = example_to_document(hf_ex, **generate_document_kwargs) + _assert_no_span_overlap(document=doc, text_field="text", span_layer="propositions") + hf_example_back = document_to_example(doc, **convert_back_kwargs) + _deep_compare( + obj=hf_example_back, + obj_expected=hf_ex, + ) + + +@pytest.fixture(scope="module") +def dataset() -> DatasetDict: + return DatasetDict.load_dataset(PIE_DATASET_PATH) + + +def test_pie_dataset(dataset): + assert dataset is not None + assert {name: len(ds) for name, ds in dataset.items()} == SPLIT_SIZES + + +@pytest.fixture(scope="module") +def document(dataset, split) -> CDCPDocument: + result = dataset[split][0] + # we can not assert the real document type because it may come from a dataset loading script + # downloaded to a temporary directory and thus have a different type object, although it is + # semantically the same + assert isinstance(result, Document) + return result + + +@pytest.fixture(scope="module") +def dataset_of_text_documents_with_labeled_spans_and_binary_relations( + dataset, +) -> DatasetDict: + converted_dataset = dataset.to_document_type(TextDocumentWithLabeledSpansAndBinaryRelations) + return converted_dataset + + +def test_dataset_of_text_documents_with_labeled_spans_and_binary_relations( + dataset_of_text_documents_with_labeled_spans_and_binary_relations, split +): + assert dataset_of_text_documents_with_labeled_spans_and_binary_relations is not None + # get a document to check + document = dataset_of_text_documents_with_labeled_spans_and_binary_relations[split][0] + assert isinstance(document, TextDocumentWithLabeledSpansAndBinaryRelations) + if split == "train": + assert document.id == "00195" + # check entities + assert len(document.labeled_spans) == 3 + entity_tuples = [(str(ent), ent.label) for ent in document.labeled_spans] + assert entity_tuples[0] == ( + "State and local court rules sometimes make default judgments much more likely.", + "value", + ) + assert entity_tuples[1] == ( + "For example, when a person who allegedly owes a debt is told to come to court on a work day, " + "they may be forced to choose between a default judgment and their job.", + "value", + ) + assert entity_tuples[2] == ( + "I urge the CFPB to find practices that involve scheduling hearings at inconvenient times unfair, " + "deceptive, and abusive, or inconsistent with 1692i.", + "policy", + ) + + # check relations + assert len(document.binary_relations) == 2 + relation_tuples = [ + (str(rel.head), rel.label, str(rel.tail)) for rel in document.binary_relations + ] + assert relation_tuples[0] == ( + "State and local court rules sometimes make default judgments much more likely.", + "reason", + "For example, when a person who allegedly owes a debt is told to come to court on a work day, " + "they may be forced to choose between a default judgment and their job.", + ) + assert relation_tuples[1] == ( + "I urge the CFPB to find practices that involve scheduling hearings at inconvenient times unfair, " + "deceptive, and abusive, or inconsistent with 1692i.", + "reason", + "State and local court rules sometimes make default judgments much more likely.", + ) + + elif split == "test": + assert document.id == "00194" + # check entities + assert len(document.labeled_spans) == 3 + entity_tuples = [(str(ent), ent.label) for ent in document.labeled_spans] + assert entity_tuples[0] == ( + "Recently, courts have held that debt collectors can escape 1692i's venue provisions entirely " + "by pursuing debt collection through arbitration instead.", + "fact", + ) + assert entity_tuples[1] == ( + "As the NAF studies reflect, arbitration has not proven a satisfactory alternative.", + "value", + ) + assert entity_tuples[2] == ( + "I urge the CFPB to include in a rule language interpreting 1692i as requiring debt collectors to proceed " + "in court, not through largely-unregulated arbitral forums.", + "policy", + ) + + # check relations + assert len(document.binary_relations) == 1 + relation_tuples = [ + (str(rel.head), rel.label, str(rel.tail)) for rel in document.binary_relations + ] + assert relation_tuples[0] == ( + "I urge the CFPB to include in a rule language interpreting 1692i as requiring debt collectors to proceed " + "in court, not through largely-unregulated arbitral forums.", + "reason", + "As the NAF studies reflect, arbitration has not proven a satisfactory alternative.", + ) + else: + raise ValueError(f"Unknown Split {split}") + + +def test_convert_to_textdocument_with_entities_and_relations( + document, dataset_of_text_documents_with_labeled_spans_and_binary_relations, split +): + # just check that we get the same as in the converted dataset when explicitly calling the conversion method + converted_doc = convert_to_text_document_with_labeled_spans_and_binary_relations(document) + doc_from_converted_dataset = dataset_of_text_documents_with_labeled_spans_and_binary_relations[ + split + ][0] + assert converted_doc == doc_from_converted_dataset + + +@pytest.fixture(scope="module") +def tokenizer() -> PreTrainedTokenizer: + return AutoTokenizer.from_pretrained("bert-base-uncased") + + +@pytest.fixture(scope="module") +def tokenized_documents_with_labeled_spans_and_binary_relations( + dataset_of_text_documents_with_labeled_spans_and_binary_relations, tokenizer +) -> List[TokenDocumentWithLabeledSpansAndBinaryRelations]: + # get a document to check + doc = dataset_of_text_documents_with_labeled_spans_and_binary_relations["train"][0] + # Note, that this is a list of documents, because the document may be split into chunks + # if the input text is too long. + tokenized_docs = tokenize_document( + doc, + tokenizer=tokenizer, + return_overflowing_tokens=True, + result_document_type=TokenDocumentWithLabeledSpansAndBinaryRelations, + verbose=True, + ) + return tokenized_docs + + +def test_tokenized_documents_with_labeled_spans_and_binary_relations( + tokenized_documents_with_labeled_spans_and_binary_relations, +): + docs = tokenized_documents_with_labeled_spans_and_binary_relations + assert len(docs) == 1 + doc = docs[0] + assert len(doc.tokens) == 84 + assert len(doc.labeled_spans) == 3 + ent = doc.labeled_spans[0] + assert ent.target[ent.start : ent.end] == ( + "state", + "and", + "local", + "court", + "rules", + "sometimes", + "make", + "default", + "judgments", + "much", + "more", + "likely", + ".", + ) + ent = doc.labeled_spans[1] + assert ent.target[ent.start : ent.end] == ( + "for", + "example", + ",", + "when", + "a", + "person", + "who", + "allegedly", + "owes", + "a", + "debt", + "is", + "told", + "to", + "come", + "to", + "court", + "on", + "a", + "work", + "day", + ",", + "they", + "may", + "be", + "forced", + "to", + "choose", + "between", + "a", + "default", + "judgment", + "and", + "their", + "job", + ".", + ) + ent = doc.labeled_spans[2] + assert ent.target[ent.start : ent.end] == ( + "i", + "urge", + "the", + "cf", + "##p", + "##b", + "to", + "find", + "practices", + "that", + "involve", + "scheduling", + "hearings", + "at", + "inc", + "##on", + "##ven", + "##ient", + "times", + "unfair", + ",", + "dec", + "##eptive", + ",", + "and", + "abusive", + ",", + "or", + "inconsistent", + "with", + "1692", + "##i", + ".", + ) + + +def test_tokenized_documents_with_entities_and_relations_all( + dataset_of_text_documents_with_labeled_spans_and_binary_relations, tokenizer +): + for split, docs in 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