FEA-Bench / testbed /embeddings-benchmark__mteb /mteb /tasks /Clustering /multilingual /MLSUMClusteringP2P.py
| from __future__ import annotations | |
| import datasets | |
| import numpy as np | |
| from mteb.abstasks import AbsTaskClustering, MultilingualTask, TaskMetadata | |
| _LANGUAGES = { | |
| "de": ["deu-Latn"], | |
| "fr": ["fra-Latn"], | |
| "ru": ["rus-Cyrl"], | |
| "es": ["spa-Latn"], | |
| } | |
| # Did not include turkish (tu) samples because all `topics` values are set to "unknown". | |
| # Which results in a v-measure of 1 as all texts are considered to be in one cluster. | |
| class MLSUMClusteringP2P(AbsTaskClustering, MultilingualTask): | |
| metadata = TaskMetadata( | |
| name="MLSUMClusteringP2P", | |
| description="Clustering of newspaper article contents and titles from MLSUM dataset. Clustering of 10 sets on the newpaper article topics.", | |
| reference="https://huggingface.co/datasets/mlsum", | |
| dataset={ | |
| "path": "reciTAL/mlsum", | |
| "revision": "b5d54f8f3b61ae17845046286940f03c6bc79bc7", | |
| "trust_remote_code": True, | |
| }, | |
| type="Clustering", | |
| category="p2p", | |
| eval_splits=["validation", "test"], | |
| eval_langs=_LANGUAGES, | |
| main_score="v_measure", | |
| date=("2010-01-01", "2018-09-30"), | |
| form=["written"], | |
| domains=["News"], | |
| task_subtypes=["Topic classification"], | |
| license="Not specified", | |
| socioeconomic_status="mixed", | |
| annotations_creators="derived", | |
| dialect=[], | |
| text_creation="found", | |
| bibtex_citation="""@article{scialom2020mlsum, | |
| title={MLSUM: The Multilingual Summarization Corpus}, | |
| author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo}, | |
| journal={arXiv preprint arXiv:2004.14900}, | |
| year={2020} | |
| }""", | |
| n_samples={"validation": 38561, "test": 41206}, | |
| avg_character_length={"validation": 4613, "test": 4810}, | |
| ) | |
| def load_data(self, **kwargs): | |
| """Load dataset from HuggingFace hub and convert it to the standard format.""" | |
| if self.data_loaded: | |
| return | |
| self.dataset = {} | |
| for lang in self.hf_subsets: | |
| self.dataset[lang] = datasets.load_dataset( | |
| name=lang, | |
| **self.metadata_dict["dataset"], | |
| ) | |
| self.dataset_transform(lang) | |
| self.data_loaded = True | |
| def _create_description(self, example): | |
| example["text"] = example["title"] + " " + example["text"] | |
| return example | |
| def dataset_transform(self, lang): | |
| """Convert to standard format""" | |
| _dataset = self.dataset[lang] | |
| _dataset.pop("train") | |
| _dataset = _dataset.map(self._create_description) | |
| _dataset = _dataset.remove_columns(["summary", "url", "date", "title"]) | |
| for eval_split in self.metadata.eval_splits: | |
| texts = _dataset[eval_split]["text"] | |
| topics = _dataset[eval_split]["topic"] | |
| new_format = { | |
| "sentences": [split.tolist() for split in np.array_split(texts, 10)], | |
| "labels": [split.tolist() for split in np.array_split(topics, 10)], | |
| } | |
| _dataset[eval_split] = datasets.Dataset.from_dict(new_format) | |
| self.dataset[lang] = _dataset | |