Use hosted data files in JSON Lines format

biomrc.py

@@ -21,7 +21,6 @@ import json
 
 import datasets
 
-
 logger = datasets.logging.get_logger(__name__)
 
 
@@ -47,6 +46,22 @@ _DESCRIPTION = """\
 We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.
 """
 
+_URLS = {
+    "large": {
+        "train": "data/biomrc_large/dataset_train{setting}.jsonl.gz",
+        "val": "data/biomrc_large/dataset_val{setting}.jsonl.gz",
+        "test": "data/biomrc_large/dataset_test{setting}.jsonl.gz",
+    },
+    "small": {
+        "train": "data/biomrc_small/dataset_train_small{setting}.jsonl.gz",
+        "val": "data/biomrc_small/dataset_val_small{setting}.jsonl.gz",
+        "test": "data/biomrc_small/dataset_test_small{setting}.jsonl.gz",
+    },
+    "tiny": {
+        "test": "data/biomrc_tiny/dataset_tiny{setting}.jsonl.gz",
+    },
+}
+
 
 class BiomrcConfig(datasets.BuilderConfig):
     """BuilderConfig for BioMRC."""
@@ -143,23 +158,9 @@ class Biomrc(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         setting = "" if self.config.biomrc_setting == "A" else "_B"
-        if self.config.biomrc_version == "large":
-            urls_to_download = {
-                "train": f"https://archive.org/download/biomrc_dataset/biomrc_large/dataset_train{setting}.json.gz",
-                "val": f"https://archive.org/download/biomrc_dataset/biomrc_large/dataset_val{setting}.json.gz",
-                "test": f"https://archive.org/download/biomrc_dataset/biomrc_large/dataset_test{setting}.json.gz",
-            }
-        elif self.config.biomrc_version == "small":
-            urls_to_download = {
-                "train": f"https://archive.org/download/biomrc_dataset/biomrc_small/dataset_train_small{setting}.json.gz",
-                "val": f"https://archive.org/download/biomrc_dataset/biomrc_small/dataset_val_small{setting}.json.gz",
-                "test": f"https://archive.org/download/biomrc_dataset/biomrc_small/dataset_test_small{setting}.json.gz",
-            }
-        else:
-            urls_to_download = {
-                "test": f"https://archive.org/download/biomrc_dataset/biomrc_tiny/dataset_tiny{setting}.json.gz"
-            }
-
+        urls_to_download = {
+            split: url.format(setting=setting) for split, url in _URLS[self.config.biomrc_version].items()
+        }
         downloaded_files = dl_manager.download_and_extract(urls_to_download)
 
         if self.config.biomrc_version == "tiny":
@@ -178,10 +179,6 @@ class Biomrc(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, filepath):
         """This function returns the examples in the raw (text) form."""
         logger.info("generating examples from = %s", filepath)
-        # Id for the biomrc dataset
         with open(filepath, encoding="utf-8") as fp:
-            biomrc = json.load(fp)
-            for _id, (ab, ti, el, an) in enumerate(
-                zip(biomrc["abstracts"], biomrc["titles"], biomrc["entities_list"], biomrc["answers"])
-            ):
-                yield _id, {"abstract": ab, "title": ti, "entities_list": el, "answer": an}
+            for idx, example in enumerate(fp):
+                yield idx, json.loads(example)