| from mteb.abstasks import AbsTaskRetrieval, TaskMetadata |
|
|
|
|
| class DanFever(AbsTaskRetrieval): |
| metadata = TaskMetadata( |
| name="DanFEVER", |
| dataset={ |
| "path": "strombergnlp/danfever", |
| "revision": "5d01e3f6a661d48e127ab5d7e3aaa0dc8331438a", |
| }, |
| description="A Danish dataset intended for misinformation research. It follows the same format as the English FEVER dataset.", |
| reference="https://aclanthology.org/2021.nodalida-main.47/", |
| type="Retrieval", |
| category="p2p", |
| eval_splits=["train"], |
| eval_langs=["dan-Latn"], |
| main_score="ndcg_at_10", |
| date=("2020-01-01", "2021-12-31"), |
| form=["spoken"], |
| domains=["Encyclopaedic", "Non-fiction"], |
| license="CC BY-SA 4.0", |
| socioeconomic_status="mixed", |
| annotations_creators="human-annotated", |
| dialect=[], |
| text_creation="found", |
| bibtex_citation=""" |
| @inproceedings{norregaard-derczynski-2021-danfever, |
| title = "{D}an{FEVER}: claim verification dataset for {D}anish", |
| author = "N{\o}rregaard, Jeppe and |
| Derczynski, Leon", |
| editor = "Dobnik, Simon and |
| {\O}vrelid, Lilja", |
| booktitle = "Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)", |
| month = may # " 31--2 " # jun, |
| year = "2021", |
| address = "Reykjavik, Iceland (Online)", |
| publisher = {Link{\"o}ping University Electronic Press, Sweden}, |
| url = "https://aclanthology.org/2021.nodalida-main.47", |
| pages = "422--428", |
| abstract = "We present a dataset, DanFEVER, intended for multilingual misinformation research. The dataset is in Danish and has the same format as the well-known English FEVER dataset. It can be used for testing methods in multilingual settings, as well as for creating models in production for the Danish language.", |
| } |
| """, |
| n_samples={"train": 8897}, |
| avg_character_length={"train": 124.84}, |
| task_subtypes=["Claim verification"], |
| ) |
|
|
| def dataset_transform(self) -> None: |
| """And transform to a retrieval datset, which have the following attributes |
| |
| self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document data like title and text |
| self.queries = Dict[query_id, str] #id => query |
| self.relevant_docs = Dict[query_id, Dict[[doc_id, score]] |
| """ |
| self.corpus = {} |
| self.relevant_docs = {} |
| self.queries = {} |
| text2id = {} |
|
|
| for split in self.dataset: |
| self.corpus[split] = {} |
| self.relevant_docs[split] = {} |
| self.queries[split] = {} |
|
|
| ds = self.dataset[split] |
| claims = ds["claim"] |
| evidences = ds["evidence_extract"] |
| labels = ds["label"] |
| class_labels = ds.features["label"].names |
|
|
| for claim, evidence, label_id in zip(claims, evidences, labels): |
| claim_is_supported = class_labels[label_id] == "Supported" |
|
|
| sim = ( |
| 1 if claim_is_supported else 0 |
| ) |
|
|
| if claim not in text2id: |
| text2id[claim] = str(len(text2id)) |
| if evidence not in text2id: |
| text2id[evidence] = len(text2id) |
|
|
| claim_id = str(text2id[claim]) |
| evidence_id = str(text2id[evidence]) |
|
|
| self.queries[split][claim_id] = claim |
| self.corpus[split][evidence_id] = {"title": "", "text": evidence} |
|
|
| if claim_id not in self.relevant_docs[split]: |
| self.relevant_docs[split][claim_id] = {} |
|
|
| self.relevant_docs[split][claim_id][evidence_id] = sim |
|
|
