| { |
| "split_name": "fa", |
| "queries": 200, |
| "corpus": 10000, |
| "qrels": 427, |
| "source_positive_qrels": 427, |
| "source_non_positive_qrels": 1664, |
| "query_limit": 200, |
| "doc_limit": 10000, |
| "dedupe_query_texts": true, |
| "dedupe_doc_texts": true, |
| "qrels_score_policy": "score > 0 kept as qrels; score <= 0 excluded and used as hard-negative corpus candidates", |
| "query_selection": { |
| "skipped_queries_without_positive_qrels": 0, |
| "skipped_duplicate_query_ids": 0, |
| "skipped_duplicate_query_texts": 0 |
| }, |
| "corpus_selection": { |
| "positive_doc_count": 426, |
| "source_hard_negative_doc_candidates": 1656, |
| "kept_hard_negative_docs": 1656, |
| "missing_positive_docs": 0, |
| "missing_hard_negative_docs": 0, |
| "duplicate_doc_texts_removed": 0, |
| "hard_negative_sampling_policy": "query_round_robin" |
| }, |
| "qrels_selection": { |
| "qrels_per_query_cap": 100, |
| "removed_qrels_over_cap": 0, |
| "qrels_cap_policy": "positive qrels are capped per query to the BM25 candidate top_k" |
| }, |
| "bm25": { |
| "config": { |
| "backend": "bm25s", |
| "algorithm": "okapi", |
| "source": "computed_bm25s", |
| "tokenizer": "regex", |
| "tokenizer_name": null, |
| "stemmer_algorithm": "english", |
| "top_k": 100, |
| "k1": 1.5, |
| "b": 0.75, |
| "show_progress": false, |
| "auto_selected": false, |
| "auto_detected_language": null, |
| "auto_detection_language_counts": null, |
| "auto_detection_sample_size": null |
| }, |
| "ndcg_at_10": 0.5787921084448737, |
| "candidate_coverage": { |
| "top_k": 100, |
| "query_count": 200, |
| "query_with_relevance_count": 200, |
| "covered_query_count": 200, |
| "query_coverage": 1.0, |
| "relevant_count": 427, |
| "covered_relevant_count": 427, |
| "relevant_coverage": 1.0 |
| }, |
| "forced_queries": 14, |
| "forced_doc_count": 17, |
| "missing_positive_doc_count_after_forcing": 0 |
| }, |
| "description": "NanoMIRACL rebuilt from hotchpotch/miracl-hf-unified dev queries, preserving all source positive passages for each sampled query and using source negatives as hard negative corpus candidates before random corpus fill.", |
| "source_dataset_id": "hotchpotch/miracl-hf-unified", |
| "source_dataset_revision": "21ad00eb467639e927b5badb7c49f4947c6c24ca", |
| "source_dataset_subset": "fa_queries", |
| "source_eval_split": "dev", |
| "source_split_policy": "Use `dev` from `fa_queries` for each language.", |
| "source_query_selection_policy": "Take the first 200 queries with at least one source positive; fewer if the source split has fewer eligible queries." |
| } |
|
|
