Update README.md

README.md

@@ -31,4 +31,161 @@ task_ids:
 - open-domain-qa
 - closed-domain-qa
 viewer: true
----
+---
+
+# Dataset Card for germanDPR-beir
+
+## Dataset Summary
+
+This database has been used to evaluate a newly trained [bi-encoder model](https://huggingface.co/PM-AI/bi-encoder_msmarco_bert-base_german) via [BEIR framework](https://github.com/beir-cellar/beir).
+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 "[deepset/germanDPR](https://www.deepset.ai/germanquad)" 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](https://huggingface.co/datasets/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 from `queries.json` (via `q_id`) with a relevant text/answer from `corpus.jsonl` (via `c_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](https://huggingface.co/datasets/PM-AI/germandpr-beir/resolve/main/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](https://github.com/beir-cellar/beir/blob/c3334fd5b336dba03c5e3e605a82fcfb1bdf667d/beir/retrieval/evaluation.py#L49) 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](https://huggingface.co/datasets/PM-AI/germandpr-beir/tree/main/dl_dataset.py) script is provided to download the dataset and to ensure the correct file and folder structure.
+
+```python
+# 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](https://github.com/beir-cellar/beir/blob/v1.0.1/examples/retrieval/evaluation/dense/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 and `qrels` size: `24009`, `9275` and `9275`
+- Original **test** `corpus` size, `queries` size and `qrels` size: `2876`, `1025` and `1025`
+
+- Processed **train** `corpus` size, `queries` size and `qrels` size: `23993`, `9275` and `9275`
+- Processed **test** `corpus` size, `queries` size and `qrels` size: `2875` and `1025` and `1025`
+
+### Languages
+
+This dataset only supports german (aka. de, DE).
+
+### Acknowledgment
+
+The dataset was initially created as "[deepset/germanDPR](https://www.deepset.ai/germanquad)" by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at [deepset.ai](https://www.deepset.ai/).
+
+This work is a collaboration between [Technical University of Applied Sciences Wildau (TH Wildau)](https://en.th-wildau.de/) and [sense.ai.tion GmbH](https://senseaition.com/).
+You can contact us via:
+* [Philipp Müller (M.Eng.)](www.linkedin.com/in/herrphilipps); Author
+* [Prof. Dr. Janett Mohnke](mailto:icampus@th-wildau.de); TH Wildau
+* [Dr. Matthias Boldt, Jörg Oehmichen](mailto:info@senseaition.com); 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".
+
+<div style="display:flex">
+     <div style="padding-left:20px;">
+          <a href="https://efre.brandenburg.de/efre/de/"><img src="https://huggingface.co/datasets/PM-AI/germandpr-beir/resolve/main/res/EFRE-Logo_rechts_oweb_en_rgb.jpeg" alt="Logo of European Regional Development Fund (EFRE)" width="200"/></a>
+     </div>
+     <div style="padding-left:20px;">
+          <a href="https://www.senseaition.com"><img src="https://senseaition.com/wp-content/uploads/thegem-logos/logo_c847aaa8f42141c4055d4a8665eb208d_3x.png" alt="Logo of senseaition GmbH" width="200"/></a>
+     </div>
+     <div style="padding-left:20px;">
+          <a href="https://www.th-wildau.de"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f6/TH_Wildau_Logo.png/640px-TH_Wildau_Logo.png" alt="Logo of TH Wildau" width="180"/></a>
+     </div>
+</div>