Create dl_dataset.py

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"
+      "--------------------------------------------------------------------------------------------------------------")