docs: update README and CONTRIBUTING for dataset_id-based API and new tasks#44
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docs: update README and CONTRIBUTING for dataset_id-based API and new tasks#44
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Introduce the MELO (Multilingual Entity Linking of Occupations) and MELS (Multilingual Entity Linking of Skills) benchmarks as new ranking tasks. MELO provides 42 evaluation datasets spanning 21 languages for job title normalization into ESCO, built from crosswalks between national occupation taxonomies and ESCO published by official EU labor organizations. MELS follows the same methodology but targets skill normalization, covering 5 languages with 8 datasets. - Add MELORanking task class with 42 datasets across 21 languages - Add MELSRanking task class with 8 datasets across 5 languages - Implement get_dataset_languages() for both tasks, supporting monolingual and cross-lingual dataset variants - Add Austria and Belgium datasets to MELO (6 additional dataset IDs) - Add unit tests for dataset ID filtering and language mapping - Update README and example scripts with new tasks
…erview, metrics overview, and example of running multiple models
…nd commit formatting
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Summary
dataset → language → task → task_group → task_typeaggregation chain,LanguageAggregationMode(4 modes),ExecutionMode(lazy/all), available ranking/classification metrics, andevaluate_multiple_models(). Move the tasks & models tables above the Usage Guide for better discoverability and add class names. Fix the checkpointing code example (wrong variable name, formatting).load_dataset(dataset_id, split)signature (replacingload_monolingual_data), document cross-lingual/multi-dataset task overrides (languages_to_dataset_ids,get_dataset_languages), update test examples to usetask.datasets[dataset_id]instead oftask.lang_datasets[Language.EN], add sections on CI/CD workflows, conventional commit format, and keeping forks up-to-date with upstream.MELORanking,MELSRanking) in all four benchmark example scripts.Closes #43