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import json |
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from functools import lru_cache |
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import datasets |
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import pandas as pd |
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SUPPORTED_LANGUAGES = [ |
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"sl", |
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"ur", |
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"sw", |
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"uz", |
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"vi", |
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"sq", |
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"ms", |
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"km", |
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"hy", |
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"da", |
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"ky", |
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"mg", |
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"mn", |
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"ja", |
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"el", |
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"it", |
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"is", |
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"ru", |
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"tl", |
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"so", |
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"pt", |
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"uk", |
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"sr", |
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"sn", |
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"ht", |
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"bs", |
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"my", |
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"ar", |
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"hr", |
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"nl", |
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"bn", |
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"ne", |
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"hi", |
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"ka", |
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"az", |
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"ko", |
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"id", |
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"fr", |
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"es", |
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"en", |
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"fa", |
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"lo", |
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"iw", |
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"th", |
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"tr", |
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"zht", |
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"zhs", |
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"ti", |
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"tg", |
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"control", |
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] |
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SYSTEMS = ["openai", "m3"] |
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MODES = ["qlang", "qlang_en", "en", "rel_langs"] |
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ROOT_DIR = "data" |
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class BordIRlinesConfig(datasets.BuilderConfig): |
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def __init__(self, language, n_hits=10, **kwargs): |
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super(BordIRlinesConfig, self).__init__(**kwargs) |
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self.language = language |
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self.n_hits = n_hits |
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self.data_root_dir = ROOT_DIR |
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def load_json(path): |
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with open(path, "r", encoding="utf-8") as f: |
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return json.load(f) |
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@lru_cache |
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def replace_lang_str(path, lang): |
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parent = path.rsplit("/", 2)[0] |
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return f"{parent}/{lang}/{lang}_docs.json" |
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class BordIRLinesDataset(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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BordIRlinesConfig( |
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name=lang, |
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language=lang, |
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description=f"{lang.upper()} dataset", |
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) |
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for lang in SUPPORTED_LANGUAGES |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description="IR Dataset for BordIRLines paper.", |
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features=datasets.Features( |
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{ |
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"query_id": datasets.Value("string"), |
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"query": datasets.Value("string"), |
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"territory": datasets.Value("string"), |
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"rank": datasets.Value("int32"), |
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"score": datasets.Value("float32"), |
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"doc_id": datasets.Value("string"), |
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"doc_text": datasets.Value("string"), |
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"doc_lang": datasets.Value("string"), |
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} |
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), |
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) |
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def _split_generators(self, dl_manager): |
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base_url = self.config.data_root_dir |
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queries_path = f"{base_url}/queries.tsv" |
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docs_path = dl_manager.download_and_extract(f"{base_url}/all_docs.json") |
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lang = self.config.language |
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splits = [] |
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downloaded_data = {} |
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for system in SYSTEMS: |
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for mode in MODES: |
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source = f"{system}.{mode}" |
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downloaded_data[source] = dl_manager.download_and_extract( |
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{ |
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"hits": f"{base_url}/{lang}/{system}/{mode}/{lang}_query_hits.tsv", |
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"docs": docs_path, |
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"queries": queries_path, |
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} |
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) |
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split = datasets.SplitGenerator( |
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name=f"{system}.{mode}", |
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gen_kwargs={ |
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"hits_path": downloaded_data[source]["hits"], |
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"docs_path": downloaded_data[source]["docs"], |
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"queries_path": downloaded_data[source]["queries"], |
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}, |
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) |
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splits.append(split) |
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return splits |
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def _generate_examples(self, hits_path, docs_path, queries_path): |
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n_hits = self.config.n_hits |
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queries_df = pd.read_csv(queries_path, sep="\t") |
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query_map = dict(zip(queries_df["query_id"], queries_df["query_text"])) |
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counter = 0 |
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docs = load_json(docs_path) |
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hits = pd.read_csv(hits_path, sep="\t") |
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if n_hits: |
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hits = hits.groupby("query_id").head(n_hits) |
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hits["query_id_int"] = hits["query_id"].str[1:].astype(int) |
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hits = hits.sort_values(by=["query_id_int", "rank"]) |
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hits = hits.drop(columns=["query_id_int"]) |
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for _, row in hits.iterrows(): |
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doc_id = row["doc_id"] |
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doc_lang = row["doc_lang"] |
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query_id = row["query_id"] |
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query_text = query_map[query_id] |
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yield ( |
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counter, |
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{ |
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"query_id": query_id, |
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"query": query_text, |
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"territory": row["territory"], |
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"rank": row["rank"], |
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"score": row["score"], |
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"doc_id": doc_id, |
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"doc_text": docs[doc_lang][doc_id], |
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"doc_lang": doc_lang, |
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}, |
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) |
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counter += 1 |
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