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import json |
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import logging |
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import os |
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from collections import defaultdict |
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from copy import copy |
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from typing import Any, Dict, Iterable, List |
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import datasets |
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from datasets import GeneratorBasedBuilder |
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logger = logging.getLogger(__name__) |
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_DESCRIPTION = """\ |
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SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated \\ |
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with labels and rationales. This version differs from `allenai/scifact` on HF because we do not have separate splits \\ |
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for claims and a corpus, instead we combine documents with claims that it supports or refutes, note that there are \\ |
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also some documents that do not have any claims associated with them as well as there are some claims that do not \\ |
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have any evidence. In the latter case we assign all such claims to the DUMMY document with ID -1 and without any text \\ |
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(i.e. abstract sentences). |
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""" |
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DATA_URL = "https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz" |
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SUBDIR = "data" |
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VARIANT_DOCUMENTS = "as_documents" |
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VARIANT_CLAIMS = "as_claims" |
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class ScifactConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Scifact.""" |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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class SciFact(GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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ScifactConfig( |
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name=VARIANT_DOCUMENTS, |
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description="Documents that serve as evidence for some claims that are split into train, test, dev", |
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), |
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ScifactConfig( |
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name=VARIANT_CLAIMS, |
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description="Documents that serve as evidence for some claims that are split into train, test, dev", |
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), |
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] |
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def _info(self): |
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if self.config.name == VARIANT_DOCUMENTS: |
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features = { |
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"doc_id": datasets.Value("int32"), |
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"title": datasets.Value("string"), |
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"abstract": datasets.features.Sequence( |
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datasets.Value("string") |
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), |
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"structured": datasets.Value( |
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"bool" |
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), |
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"claims": datasets.features.Sequence( |
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feature={ |
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"id": datasets.Value(dtype="int32", id=None), |
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"claim": datasets.Value(dtype="string", id=None), |
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"evidence": datasets.features.Sequence( |
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feature={ |
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"label": datasets.Value(dtype="string", id=None), |
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"sentences": datasets.features.Sequence( |
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datasets.Value(dtype="int32", id=None) |
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), |
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} |
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), |
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} |
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), |
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} |
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elif self.config.name == VARIANT_CLAIMS: |
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features = { |
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"id": datasets.Value("int32"), |
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"claim": datasets.Value(dtype="string", id=None), |
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"cited_docs": datasets.features.Sequence( |
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feature={ |
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"doc_id": datasets.Value(dtype="int32", id=None), |
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"title": datasets.Value("string"), |
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"abstract": datasets.features.Sequence( |
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datasets.Value("string") |
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), |
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"structured": datasets.Value( |
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"bool" |
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), |
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"evidence": datasets.features.Sequence( |
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feature={ |
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"label": datasets.Value(dtype="string", id=None), |
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"sentences": datasets.features.Sequence( |
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datasets.Value(dtype="int32", id=None) |
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), |
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} |
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), |
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} |
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), |
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} |
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else: |
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raise ValueError(f"unknown dataset variant: {self.config.name}") |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features(features), |
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supervised_keys=None, |
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homepage="https://scifact.apps.allenai.org/", |
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) |
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def _generate_examples(self, claims_filepath: str, corpus_filepath: str): |
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"""Yields examples.""" |
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with open(claims_filepath) as f: |
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claim_data = [json.loads(line) for line in f.readlines()] |
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with open(corpus_filepath) as f: |
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corpus_docs = [json.loads(line) for line in f.readlines()] |
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if self.config.name == VARIANT_DOCUMENTS: |
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doc_id2claims = defaultdict(list) |
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for claim in claim_data: |
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cited_doc_ids = claim.pop("cited_doc_ids", [-1]) |
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evidence = claim.pop("evidence", dict()) |
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for cited_doc_id in cited_doc_ids: |
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current_claim = claim.copy() |
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current_claim["evidence"] = evidence.get(str(cited_doc_id), []) |
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doc_id2claims[cited_doc_id].append(current_claim) |
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dummy_doc = {"doc_id": -1, "title": "", "abstract": [], "structured": False} |
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corpus_docs = [dummy_doc] + corpus_docs |
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for id_, doc in enumerate(corpus_docs): |
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doc = doc.copy() |
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doc["claims"] = doc_id2claims.get(doc["doc_id"], []) |
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yield id_, doc |
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elif self.config.name == VARIANT_CLAIMS: |
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doc_id2doc = {doc["doc_id"]: doc for doc in corpus_docs} |
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for _id, claim in enumerate(claim_data): |
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evidence = claim.pop("evidence", {}) |
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cited_doc_ids = claim.pop("cited_doc_ids", []) |
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claim["cited_docs"] = [] |
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for cited_doc_id in cited_doc_ids: |
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doc = copy(doc_id2doc[cited_doc_id]) |
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doc["evidence"] = evidence.get(str(cited_doc_id), []) |
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claim["cited_docs"].append(doc) |
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yield _id, claim |
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else: |
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raise ValueError(f"unknown dataset variant: {self.config.name}") |
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def _split_generators(self, dl_manager): |
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"""We handle string, list and dicts in datafiles.""" |
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if dl_manager.manual_dir is None: |
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data_dir = os.path.join(dl_manager.download_and_extract(DATA_URL), SUBDIR) |
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else: |
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data_dir = os.path.abspath(dl_manager.manual_dir) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"claims_filepath": os.path.join(data_dir, "claims_train.jsonl"), |
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"corpus_filepath": os.path.join(data_dir, "corpus.jsonl"), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"claims_filepath": os.path.join(data_dir, "claims_dev.jsonl"), |
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"corpus_filepath": os.path.join(data_dir, "corpus.jsonl"), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"claims_filepath": os.path.join(data_dir, "claims_test.jsonl"), |
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"corpus_filepath": os.path.join(data_dir, "corpus.jsonl"), |
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}, |
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), |
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] |
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def _convert_to_output_eval_format( |
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self, data: Iterable[Dict[str, Any]] |
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) -> List[Dict[str, Any]]: |
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"""Output should have the format as specified here: |
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https://github.com/allenai/scifact/blob/68b98a56d93e0f9da0d2aab4e6c3294699a0f72e/doc/evaluation.md#submission-format |
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Each claim is represented as Dict with: |
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"id": int An integer claim ID. |
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"evidence": Dict[str, Dict] The evidence for the claim. |
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"doc_id": Dict[str, Any] The sentences and label for a single document. |
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"sentences": List[int] |
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"label": str |
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""" |
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if self.config.name == VARIANT_DOCUMENTS: |
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claim2doc2sent_with_label = dict() |
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for document in data: |
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doc_id = document["doc_id"] |
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if len(document["claims"]["claim"]) == 0: |
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continue |
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for idx in range(len(document["claims"]["claim"])): |
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claim_id = document["claims"]["id"][idx] |
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claim_text = document["claims"]["claim"][idx] |
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claim_evidence = document["claims"]["evidence"][idx] |
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if claim_id not in claim2doc2sent_with_label: |
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claim2doc2sent_with_label[claim_id] = dict() |
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if doc_id not in claim2doc2sent_with_label[claim_id]: |
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if len(claim_evidence["label"]) > 0: |
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ev_label = claim_evidence["label"][0] |
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claim2doc2sent_with_label[claim_id][doc_id] = { |
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"label": ev_label, |
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"sentences": [], |
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} |
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for ev_sentences in claim_evidence["sentences"]: |
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claim2doc2sent_with_label[claim_id][doc_id]["sentences"].extend( |
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ev_sentences |
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) |
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outputs = [] |
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for claim_id in claim2doc2sent_with_label: |
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claim_dict = {"id": claim_id, "evidence": dict()} |
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for doc_id in claim2doc2sent_with_label[claim_id]: |
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claim_dict["evidence"][doc_id] = { |
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"sentences": claim2doc2sent_with_label[claim_id][doc_id]["sentences"], |
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"label": claim2doc2sent_with_label[claim_id][doc_id]["label"], |
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} |
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outputs.append((int(claim_id), claim_dict.copy())) |
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outputs_sorted_by_claim_ids = [ |
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claim for claim_id, claim in sorted(outputs, key=lambda x: x[0]) |
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] |
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return outputs_sorted_by_claim_ids |
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elif self.config.name == VARIANT_CLAIMS: |
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raise NotImplementedError( |
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f"_convert_to_output_eval_format is not yet implemented for dataset variant {self.config.name}" |
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) |
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else: |
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raise ValueError(f"unknown dataset variant: {self.config.name}") |
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