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Dataset Card for germanDPR-beir
Dataset Summary
This dataset can be used for BEIR evaluation based on deepset/germanDPR. It already has been used to evaluate a newly trained bi-encoder model. The benchmark framework requires a particular dataset structure by default which has been created locally and uploaded here.
Acknowledgement: The dataset was initially created as "germanDPR" by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at deepset.ai.
Dataset Creation
First, the original dataset deepset/germanDPR was converted into three files for BEIR compatibility:
- The first file is
queries.jsonl
and contains an ID and a question in each line. - The second file,
corpus.jsonl
, contains in each line an ID, a title, a text and some metadata. - In the
qrel
folder is the third file. It connects every question fromqueries.json
(viaq_id
) with a relevant text/answer fromcorpus.jsonl
(viac_id
)
This process has been done for train
and test
split separately based on the original germanDPR dataset.
Approaching the dataset creation like that is necessary because queries AND corpus both differ in deepset's germanDPR dataset
and it might be confusion changing this specific split.
In conclusion, queries and corpus differ between train and test split and not only qrels data!
Note: If you want one big corpus use datasets.concatenate_datasets()
.
In the original dataset, there is one passage containing the answer and three "wrong" passages for each question. During the creation of this customized dataset, all four passages are added, but only if they are not already present (... meaning they have been deduplicated).
It should be noted, that BEIR is combining title
+ text
in corpus.jsonl
to a new string which may produce odd results:
The original germanDPR dataset does not always contain "classical" titles (i.e. short), but sometimes consists of whole sentences, which are also present in the "text" field.
This results in very long passages as well as duplications.
In addition, both title and text contain specially formatted content.
For example, the words used in titles are often connected with underscores:
Apple_Magic_Mouse
And texts begin with special characters to distinguish headings and subheadings:
Wirtschaft_der_Vereinigten_Staaten\n\n== Verschuldung ==\nEin durchschnittlicher Haushalt (...)
Line breaks are also frequently found, as you can see.
Of course, it depends on the application whether these things become a problem or not. However, it was decided to release two variants of the original dataset:
- The
original
variant leaves the titles and texts as they are. There are no modifications. - The
processed
variant removes the title completely and simplifies the texts by removing the special formatting.
The creation of both variants can be viewed in create_dataset.py. In particular, the following parameters were used:
original
:SPLIT=test/train, TEXT_PREPROCESSING=False, KEEP_TITLE=True
processed
:SPLIT=test/Train, TEXT_PREPROCESSING=True, KEEP_TITLE=False
One final thing to mention: The IDs for queries and the corpus should not match!!! During the evaluation using BEIR, it was found that if these IDs match, the result for that entry is completely removed. This means some of the results are missing. A correct calculation of the overall result is no longer possible. Have a look into BEIR's evaluation.py for further understanding.
Dataset Usage
As earlier mentioned, this dataset is intended to be used with the BEIR benchmark framework. The file and data structure required for BEIR can only be used to a limited extent with Huggingface Datasets or it is necessary to define multiple dataset repositories at once. To make it easier, the dl_dataset.py script is provided to download the dataset and to ensure the correct file and folder structure.
# dl_dataset.py
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"
"--------------------------------------------------------------------------------------------------------------")
Alternatively, the data sets can be downloaded directly:
- https://huggingface.co/datasets/PM-AI/germandpr-beir/resolve/main/data/original.tar.gz
- https://huggingface.co/datasets/PM-AI/germandpr-beir/resolve/main/data/processed.tar.gz
Now you can use the downloaded files in BEIR framework:
- For Example: evaluate_sbert.py
- Just set variable
"dataset"
to"germandpr-beir-dataset/processed/test"
or"germandpr-beir-dataset/original/test"
. - Same goes for
"train"
.
Dataset Sizes
Original train
corpus
size,queries
size andqrels
size:24009
,9275
and9275
Original test
corpus
size,queries
size andqrels
size:2876
,1025
and1025
Processed train
corpus
size,queries
size andqrels
size:23993
,9275
and9275
Processed test
corpus
size,queries
size andqrels
size:2875
and1025
and1025
Languages
This dataset only supports german (aka. de, DE).
Acknowledgment
The dataset was initially created as "deepset/germanDPR" by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at deepset.ai.
This work is a collaboration between Technical University of Applied Sciences Wildau (TH Wildau) and sense.ai.tion GmbH. You can contact us via:
- Philipp Müller (M.Eng.); Author
- Prof. Dr. Janett Mohnke; TH Wildau
- Dr. Matthias Boldt, Jörg Oehmichen; sense.AI.tion GmbH
This work was funded by the European Regional Development Fund (EFRE) and the State of Brandenburg. Project/Vorhaben: "ProFIT: Natürlichsprachliche Dialogassistenten in der Pflege".
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