FEA-Bench / testbed /embeddings-benchmark__mteb /mteb /tasks /Retrieval /multilingual /MIRACLRetrieval.py
| from __future__ import annotations | |
| import datasets | |
| from mteb.abstasks.TaskMetadata import TaskMetadata | |
| from ....abstasks import MultilingualTask | |
| from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval | |
| _EVAL_SPLIT = "test" | |
| _LANGS = {"de": ["deu-Latn"], "es": ["spa-Latn"]} | |
| def _load_miracl_data( | |
| path: str, langs: list, split: str, cache_dir: str = None, revision: str = None | |
| ): | |
| queries = {lang: {split: {}} for lang in langs} | |
| corpus = {lang: {split: {}} for lang in langs} | |
| relevant_docs = {lang: {split: {}} for lang in langs} | |
| for lang in langs: | |
| data = datasets.load_dataset( | |
| path, | |
| lang, | |
| split=split, | |
| cache_dir=cache_dir, | |
| revision=revision, | |
| ) | |
| # Generate unique IDs for queries and documents | |
| query_id_counter = 1 | |
| document_id_counter = 1 | |
| for row in data: | |
| query_text = row["query"] | |
| positive_texts = row["positive"] | |
| negative_texts = row["negative"] | |
| # Assign unique ID to the query | |
| query_id = f"Q{query_id_counter}" | |
| queries[lang][split][query_id] = query_text | |
| query_id_counter += 1 | |
| # Add positive and negative texts to corpus with unique IDs | |
| for text in positive_texts + negative_texts: | |
| doc_id = f"D{document_id_counter}" | |
| corpus[lang][split][doc_id] = {"text": text} | |
| document_id_counter += 1 | |
| # Add relevant document information to relevant_docs for positive texts only | |
| if text in positive_texts: | |
| if query_id not in relevant_docs[lang][split]: | |
| relevant_docs[lang][split][query_id] = {} | |
| relevant_docs[lang][split][query_id][doc_id] = 1 | |
| corpus = datasets.DatasetDict(corpus) | |
| queries = datasets.DatasetDict(queries) | |
| relevant_docs = datasets.DatasetDict(relevant_docs) | |
| return corpus, queries, relevant_docs | |
| class MIRACLRetrieval(MultilingualTask, AbsTaskRetrieval): | |
| metadata = TaskMetadata( | |
| name="MIRACLRetrieval", | |
| description="MIRACLRetrieval", | |
| reference=None, | |
| dataset={ | |
| "path": "jinaai/miracl", | |
| "revision": "d28a029f35c4ff7f616df47b0edf54e6882395e6", | |
| }, | |
| type="Retrieval", | |
| category="s2p", | |
| eval_splits=[_EVAL_SPLIT], | |
| eval_langs=_LANGS, | |
| main_score="ndcg_at_10", | |
| date=None, | |
| form=None, | |
| domains=None, | |
| task_subtypes=None, | |
| license=None, | |
| socioeconomic_status=None, | |
| annotations_creators=None, | |
| dialect=None, | |
| text_creation=None, | |
| bibtex_citation=None, | |
| n_samples=None, | |
| avg_character_length=None, | |
| ) | |
| def load_data(self, **kwargs): | |
| if self.data_loaded: | |
| return | |
| self.corpus, self.queries, self.relevant_docs = _load_miracl_data( | |
| path=self.metadata_dict["dataset"]["path"], | |
| langs=self.hf_subsets, | |
| split=self.metadata_dict["eval_splits"][0], | |
| cache_dir=kwargs.get("cache_dir", None), | |
| revision=self.metadata_dict["dataset"]["revision"], | |
| ) | |
| self.data_loaded = True | |