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011bd7a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | """See clarin-knext/arguana-pl, clarin-knext/arguana-pl-qrels and
beir.datasets.data_loader_hf.HFDataLoader for BEIR format.
"""
from __future__ import annotations
from datasets import Dataset, DatasetDict, Features, Value, load_dataset
dataset = load_dataset("deepset/germanquad")
dataset.pop("train")
# Deduplicate contexts and map them uniquely one-to-one to ids
context_to_id = {}
corpus_data = {"_id": [], "text": []}
queries_data = {"_id": [], "text": []}
qrels_data = {"query-id": [], "corpus-id": [], "score": []}
for item in dataset["test"]:
# Check if the context is already in the dictionary
if item["context"] not in context_to_id:
context_to_id[item["context"]] = "c" + str(item["id"])
entry = {"_id": context_to_id[item["context"]], "text": item["context"]}
corpus_data["_id"].append(entry["_id"])
corpus_data["text"].append(entry["text"])
for item in dataset["test"]:
entry = {"_id": "q" + str(item["id"]), "text": item["question"]}
queries_data["_id"].append(entry["_id"])
queries_data["text"].append(entry["text"])
# this maps queries to relevant documents
for item in dataset["test"]:
corpus_id = context_to_id[item["context"]]
entry = {"query-id": "q" + str(item["id"]), "corpus-id": corpus_id, "score": 1}
qrels_data["query-id"].append(entry["query-id"])
qrels_data["corpus-id"].append(entry["corpus-id"])
qrels_data["score"].append(entry["score"])
corpus_features = Features({"_id": Value("string"), "text": Value("string")})
qrels_features = Features(
{"query-id": Value("string"), "corpus-id": Value("string"), "score": Value("int32")}
)
corpus_dataset = Dataset.from_dict(corpus_data, features=corpus_features)
queries_dataset = Dataset.from_dict(queries_data, features=corpus_features)
qrels_dataset = Dataset.from_dict(qrels_data, features=qrels_features)
corpus_datadict = DatasetDict({"corpus": corpus_dataset, "queries": queries_dataset})
qrels_datadict = DatasetDict({"test": qrels_dataset})
corpus_datadict.save_to_disk("scripts/data/germanquad/corpus")
qrels_datadict.save_to_disk("scripts/data/germanquad/qrels")
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