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PM-AI
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germandpr-beir

Tasks:

Sentence Similarity

Feature Extraction

Sub-tasks:

document-retrieval

closed-domain-qa

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information retrieval

documents retrieval

passage retrieval

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germandpr-beir / dl_dataset.py

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Create dl_dataset.py

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	import json
	import os

	import datasets
	from beir.datasets.data_loader import GenericDataLoader

	# ----------------------------------------
	# This scripts downloads the BEIR compatible deepsetDPR dataset from "Huggingface Datasets" to your local machine.
	# Please see dataset's description/readme to learn more about how the dataset was created.
	# If you want to use deepset/germandpr without any changes, use TYPE "original"
	# If you want to reproduce PM-AI/bi-encoder_msmarco_bert-base_german, use TYPE "processed"
	# ----------------------------------------


	TYPE = "processed" # or "original"
	SPLIT = "train" # or "train"
	DOWNLOAD_DIR = "germandpr-beir-dataset"
	DOWNLOAD_DIR = os.path.join(DOWNLOAD_DIR, f'{TYPE}/{SPLIT}')
	DOWNLOAD_QREL_DIR = os.path.join(DOWNLOAD_DIR, f'qrels/')

	os.makedirs(DOWNLOAD_QREL_DIR, exist_ok=True)

	# for BEIR compatibility we need queries, corpus and qrels all together
	# ensure to always load these three based on the same type (all "processed" or all "original")
	for subset_name in ["queries", "corpus", "qrels"]:
	subset = datasets.load_dataset("PM-AI/germandpr-beir", f'{TYPE}-{subset_name}', split=SPLIT)
	if subset_name == "qrels":
	out_path = os.path.join(DOWNLOAD_QREL_DIR, f'{SPLIT}.tsv')
	subset.to_csv(out_path, sep="\t", index=False)
	else:
	if subset_name == "queries":
	_row_to_json = lambda row: json.dumps({"_id": row["_id"], "text": row["text"]}, ensure_ascii=False)
	else:
	_row_to_json = lambda row: json.dumps({"_id": row["_id"], "title": row["title"], "text": row["text"]}, ensure_ascii=False)

	with open(os.path.join(DOWNLOAD_DIR, f'{subset_name}.jsonl'), "w", encoding="utf-8") as out_file:
	for row in subset:
	out_file.write(_row_to_json(row) + "\n")


	# GenericDataLoader is part of BEIR. If everything is working correctly we can now load the dataset
	corpus, queries, qrels = GenericDataLoader(data_folder=DOWNLOAD_DIR).load(SPLIT)
	print(f'{SPLIT} corpus size: {len(corpus)}\n'
	f'{SPLIT} queries size: {len(queries)}\n'
	f'{SPLIT} qrels: {len(qrels)}\n')

	print("--------------------------------------------------------------------------------------------------------------\n"
	"Now you can use the downloaded files in BEIR framework\n"
	"Example: https://github.com/beir-cellar/beir/blob/v1.0.1/examples/retrieval/evaluation/dense/evaluate_sbert.py\n"
	"--------------------------------------------------------------------------------------------------------------")