import json from functools import lru_cache import datasets import pandas as pd SUPPORTED_LANGUAGES = [ "sl", "ur", "sw", "uz", "vi", "sq", "ms", "km", "hy", "da", "ky", "mg", "mn", "ja", "el", "it", "is", "ru", "tl", "so", "pt", "uk", "sr", "sn", "ht", "bs", "my", "ar", "hr", "nl", "bn", "ne", "hi", "ka", "az", "ko", "id", "fr", "es", "en", "fa", "lo", "iw", "th", "tr", "zht", "zhs", "ti", "tg", "control", ] SYSTEMS = ["openai", "m3"] MODES = ["qlang", "qlang_en", "en", "rel_langs"] # # get combination of systems and supported modes # SUPPORTED_SOURCES = [f"{system}.{mode}" for system in SYSTEMS for mode in MODES] ROOT_DIR = "data" class BordIRlinesConfig(datasets.BuilderConfig): def __init__(self, language, n_hits=10, **kwargs): super(BordIRlinesConfig, self).__init__(**kwargs) self.language = language self.n_hits = n_hits self.data_root_dir = ROOT_DIR def load_json(path): with open(path, "r", encoding="utf-8") as f: return json.load(f) @lru_cache def replace_lang_str(path, lang): parent = path.rsplit("/", 2)[0] return f"{parent}/{lang}/{lang}_docs.json" class BordIRLinesDataset(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ BordIRlinesConfig( name=lang, language=lang, description=f"{lang.upper()} dataset", ) for lang in SUPPORTED_LANGUAGES ] def __init__(self, *args, relevant_only=False, annotation_type=None, llm_mode="fewshot", **kwargs): super().__init__(*args, **kwargs) self.relevant_only = relevant_only self.annotation_type = annotation_type self.llm_mode = llm_mode # Choose between "zeroshot" and "fewshot". Default: "fewshot". def _info(self): return datasets.DatasetInfo( description="IR Dataset for BordIRLines paper.", features=datasets.Features( { "query_id": datasets.Value("string"), "query": datasets.Value("string"), "query_lang": datasets.Value("string"), "territory": datasets.Value("string"), "rank": datasets.Value("int32"), "score": datasets.Value("float32"), "doc_id": datasets.Value("string"), "doc_text": datasets.Value("string"), "doc_lang": datasets.Value("string"), "relevant_human": datasets.Value("bool"), "territory_human": datasets.Sequence(datasets.Value("string")), "relevant_llm_zeroshot": datasets.Value("bool"), "relevant_llm_fewshot": datasets.Value("bool"), } ), ) def _split_generators(self, dl_manager): base_url = self.config.data_root_dir queries_path = f"{base_url}/queries.tsv" docs_path = dl_manager.download_and_extract(f"{base_url}/all_docs.json") human_annotations_path = dl_manager.download_and_extract(f"{base_url}/human_annotations.tsv") llm_annotations_path = dl_manager.download_and_extract(f"{base_url}/llm_annotations.tsv") lang = self.config.language splits = [] downloaded_data = {} for system in SYSTEMS: for mode in MODES: source = f"{system}.{mode}" downloaded_data[source] = dl_manager.download_and_extract( { "hits": f"{base_url}/{lang}/{system}/{mode}/{lang}_query_hits.tsv", "docs": docs_path, "queries": queries_path, "human_annotations": human_annotations_path, "llm_annotations": llm_annotations_path, } ) split = datasets.SplitGenerator( name=f"{system}.{mode}", gen_kwargs={ "hits_path": downloaded_data[source]["hits"], "docs_path": downloaded_data[source]["docs"], "queries_path": downloaded_data[source]["queries"], "human_annotations_path": downloaded_data[source]["human_annotations"], "llm_annotations_path": downloaded_data[source]["llm_annotations"], }, ) splits.append(split) return splits def _generate_examples(self, hits_path, docs_path, queries_path, human_annotations_path, llm_annotations_path): n_hits = self.config.n_hits queries_df = pd.read_csv(queries_path, sep="\t") query_map = dict(zip(queries_df["query_id"], queries_df["query_text"])) query_to_lang_map = dict(zip(queries_df["query_id"], queries_df["language"])) counter = 0 docs = load_json(docs_path) hits = pd.read_csv(hits_path, sep="\t") human_annotations = pd.read_csv(human_annotations_path, sep="\t") llm_annotations = pd.read_csv(llm_annotations_path, sep="\t") if n_hits: hits = hits.groupby("query_id").head(n_hits) # sort hits by query_id and rank hits["query_id_int"] = hits["query_id"].str[1:].astype(int) hits = hits.sort_values(by=["query_id_int", "rank"]) hits = hits.drop(columns=["query_id_int"]) human_map = human_annotations.set_index(["query_id", "doc_id"]).to_dict(orient="index") llm_map = llm_annotations.set_index(["query_id", "doc_id"]).to_dict(orient="index") for _, row in hits.iterrows(): doc_id = row["doc_id"] doc_lang = row["doc_lang"] query_id = row["query_id"] query_text = query_map[query_id] query_lang = query_to_lang_map[query_id] # Get Human Data human_data = human_map.get((query_id, doc_id), {}) # Parse relevant_human_votes manually raw_votes = human_data.get("relevant_human", "[]") relevant_human_votes = [ True if v.strip() == "True" else False if v.strip() == "False" else False for v in raw_votes.strip("[]").split(",") if v.strip() ] # Parse territory_human manually raw_territories = human_data.get("territory_human", "[]") territory_human = [ v.strip().strip("'").strip('"') # Remove extra quotes and whitespace for v in raw_territories.strip("[]").split(",") if v.strip() ] # Calculate majority relevance majority_relevant_human = ( sum(relevant_human_votes) > len(relevant_human_votes) / 2 if relevant_human_votes else False ) # Get LLM Data llm_data = llm_map.get((query_id, doc_id), {}) relevant_llm = ( llm_data.get("relevant_fewshot", None) if self.llm_mode == "fewshot" else llm_data.get("relevant_zeroshot", None) ) # Filtering logic if self.relevant_only: if self.annotation_type == "human" and not majority_relevant_human: continue elif self.annotation_type == "llm" and not (relevant_llm is True): continue elif not majority_relevant_human and not (relevant_llm is True): continue yield ( counter, { "query_id": query_id, "query": query_text, "query_lang": query_lang, "territory": row["territory"], "rank": row["rank"], "score": row["score"], "doc_id": doc_id, "doc_text": docs[doc_lang][doc_id], "doc_lang": doc_lang, "relevant_human": majority_relevant_human, "territory_human": territory_human, "relevant_llm_zeroshot": llm_data.get("relevant_zeroshot", None), "relevant_llm_fewshot": llm_data.get("relevant_fewshot", None), }, ) counter += 1