import json from copy import copy 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"] RELEVANCE_FILTERS = ["all", "relevant", "non-relevant"] LLM_MODES = ["zeroshot", "fewshot"] 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" def get_label(human_bool, llm_bool, annotation_type): if annotation_type == "human": return human_bool elif annotation_type == "llm": return llm_bool else: return human_bool if human_bool is not None else llm_bool 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, relevance_filter="all", annotation_type=None, llm_mode="fewshot", viewpoint_filter=None, **kwargs, ): super().__init__(*args, **kwargs) self.relevance_filter = relevance_filter assert self.relevance_filter in RELEVANCE_FILTERS self.annotation_type = annotation_type self.llm_mode = llm_mode assert self.llm_mode in LLM_MODES self.viewpoint_filter = viewpoint_filter # Filter for a specific viewpoint 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"), "viewpoint_human": datasets.Value("string"), "viewpoint_llm": datasets.Value("string"), "relevant_human": datasets.Value("bool"), "relevant_llm": 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 _skip_viewpoint(self, viewpoint_human, viewpoint_llm, query_entry): viewpoint = get_label(viewpoint_human, viewpoint_llm, self.annotation_type) if viewpoint is None: return True if self.viewpoint_filter == "Non-controllers": controller = query_entry["Controller"] if controller == "Unknown": return True claimants = copy(query_entry["Claimants"]) claimants.remove(controller) return ( not claimants or viewpoint not in claimants ) # skip if not a non-controller viewpoint # otherwise, handle the case where we want to filter for a specific viewpoint target_viewpoint = ( query_entry["Controller"] if self.viewpoint_filter == "Controller" else self.viewpoint_filter ) return target_viewpoint and viewpoint != target_viewpoint def _skip_relevance(self, relevant_human, relevant_llm): # Filtering logic based on relevance preference relevant = get_label(relevant_human, relevant_llm, self.annotation_type) target_relevant = {"relevant": True, "non-relevant": False}.get(self.relevance_filter, None) return target_relevant is not None and relevant != target_relevant # If "all", do not filter anything 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").set_index("query_id") queries_df["Claimants"] = queries_df["Claimants"].str.split(";").map(set) 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_entry = queries_df.loc[query_id] query_text = query_entry["query_text"] query_lang = query_entry["language"] # Get Human Data human_data = human_map.get((query_id, doc_id), {}) relevant_human = human_data.get("relevant", None) viewpoint_human = human_data.get("territory", None) # Get LLM Data llm_data = llm_map.get((query_id, doc_id), {}) relevant_llm = llm_data[f"relevant_{self.llm_mode}"] viewpoint_llm = llm_data[f"territory_{self.llm_mode}"] # Filtering logic based on viewpoint preference viewpoint_llm = viewpoint_llm.split(") ", 1)[-1] if not pd.isna(viewpoint_llm) else None if self.viewpoint_filter: do_skip = self._skip_viewpoint(viewpoint_human, viewpoint_llm, query_entry) if do_skip: continue if self.relevance_filter != "all": do_skip = self._skip_relevance(relevant_human, relevant_llm) if do_skip: 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, "viewpoint_human": viewpoint_human, "viewpoint_llm": viewpoint_llm, "relevant_human": relevant_human, "relevant_llm": relevant_llm, }, ) counter += 1