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| from dataclasses import dataclass | |
| from enum import Enum | |
| def get_safe_name(name: str): | |
| """Get RFC 1123 compatible safe name""" | |
| name = name.replace('-', '_') | |
| return ''.join( | |
| character.lower() | |
| for character in name | |
| if (character.isalnum() or character == '_')) | |
| dataset_dict = { | |
| "qa": { | |
| "wiki": { | |
| "en": ["wikipedia_20240101", ], | |
| "zh": ["wikipedia_20240101", ] | |
| }, | |
| "web": { | |
| "en": ["mC4", ], | |
| "zh": ["mC4", ] | |
| }, | |
| "news": { | |
| "en": ["CC-News", ], | |
| "zh": ["CC-News", ] | |
| }, | |
| "health": { | |
| "en": ["PubMedQA", ], | |
| "zh": ["Huatuo-26M", ] | |
| }, | |
| "law": { | |
| "en": ["pile-of-law", ], | |
| "zh": ["flk_npc_gov_cn", ] | |
| }, | |
| "finance": { | |
| "en": ["Reuters-Financial", ], | |
| "zh": ["FinCorpus", ] | |
| }, | |
| "arxiv": { | |
| "en": ["Arxiv", ]}, | |
| }, | |
| "long_doc": { | |
| "arxiv": { | |
| "en": ["gpt-3", "llama2", "llm-survey", "gemini"], | |
| }, | |
| "book": { | |
| "en": [ | |
| "origin-of-species_darwin", | |
| "a-brief-history-of-time_stephen-hawking" | |
| ] | |
| }, | |
| "healthcare": { | |
| "en": [ | |
| "pubmed_100k-200k_1", | |
| "pubmed_100k-200k_2", | |
| "pubmed_100k-200k_3", | |
| "pubmed_40k-50k_5-merged", | |
| "pubmed_30k-40k_10-merged" | |
| ] | |
| }, | |
| "law": { | |
| "en": [ | |
| "lex_files_300k-400k", | |
| "lex_files_400k-500k", | |
| "lex_files_500k-600k", | |
| "lex_files_600k-700k" | |
| ] | |
| } | |
| } | |
| } | |
| metric_list = [ | |
| "ndcg_at_1", | |
| "ndcg_at_3", | |
| "ndcg_at_5", | |
| "ndcg_at_10", | |
| "ndcg_at_100", | |
| "ndcg_at_1000", | |
| "map_at_1", | |
| "map_at_3", | |
| "map_at_5", | |
| "map_at_10", | |
| "map_at_100", | |
| "map_at_1000", | |
| "recall_at_1", | |
| "recall_at_3", | |
| "recall_at_5", | |
| "recall_at_10" | |
| "recall_at_100", | |
| "recall_at_1000", | |
| "precision_at_1", | |
| "precision_at_3", | |
| "precision_at_5", | |
| "precision_at_10", | |
| "precision_at_100", | |
| "precision_at_1000", | |
| "mrr_at_1", | |
| "mrr_at_3", | |
| "mrr_at_5", | |
| "mrr_at_10", | |
| "mrr_at_100", | |
| "mrr_at_1000" | |
| ] | |
| class Benchmark: | |
| name: str # [domain]_[language]_[metric], task_key in the json file, | |
| metric: str # ndcg_at_1 ,metric_key in the json file | |
| col_name: str # [domain]_[language], name to display in the leaderboard | |
| domain: str | |
| lang: str | |
| task: str | |
| qa_benchmark_dict = {} | |
| long_doc_benchmark_dict = {} | |
| for task, domain_dict in dataset_dict.items(): | |
| for domain, lang_dict in domain_dict.items(): | |
| for lang, dataset_list in lang_dict.items(): | |
| if task == "qa": | |
| benchmark_name = f"{domain}_{lang}" | |
| benchmark_name = get_safe_name(benchmark_name) | |
| col_name = benchmark_name | |
| for metric in dataset_list: | |
| qa_benchmark_dict[benchmark_name] = Benchmark(benchmark_name, metric, col_name, domain, lang, task) | |
| elif task == "long_doc": | |
| for dataset in dataset_list: | |
| benchmark_name = f"{domain}_{lang}_{dataset}" | |
| benchmark_name = get_safe_name(benchmark_name) | |
| col_name = benchmark_name | |
| for metric in metric_list: | |
| long_doc_benchmark_dict[benchmark_name] = Benchmark(benchmark_name, metric, col_name, domain, lang, task) | |
| BenchmarksQA = Enum('BenchmarksQA', qa_benchmark_dict) | |
| BenchmarksLongDoc = Enum('BenchmarksLongDoc', long_doc_benchmark_dict) | |
| BENCHMARK_COLS_QA = [c.col_name for c in qa_benchmark_dict.values()] | |
| BENCHMARK_COLS_LONG_DOC = [c.col_name for c in long_doc_benchmark_dict.values()] | |
| DOMAIN_COLS_QA = list(frozenset([c.domain for c in qa_benchmark_dict.values()])) | |
| LANG_COLS_QA = list(frozenset([c.lang for c in qa_benchmark_dict.values()])) | |
| DOMAIN_COLS_LONG_DOC = list(frozenset([c.domain for c in long_doc_benchmark_dict.values()])) | |
| LANG_COLS_LONG_DOC = list(frozenset([c.lang for c in long_doc_benchmark_dict.values()])) | |