Update pico-breast-cancer.py

pico-breast-cancer.py

@@ -2,6 +2,9 @@ import os
 import datasets
 
 
+logger = datasets.logging.get_logger(__name__)
+
+
 _CITATION = """\
 @InProceedings{mutinda2022pico,
   title = {PICO Corpus: A Publicly Available Corpus to Support Automatic Data Extraction from Biomedical Literature},
@@ -22,63 +25,10 @@ converted to CoNLL-2003.
 
 _HOMEPAGE = "https://github.com/Martin-Masson/pico-corpus"
 
-_URL = "https://github.com/Martin-Masson/pico-breast-cancer/raw/main/pico-breast-cancer.zip"
-
-_TAGS = [
-    "O",
-    "B-total-participants",
-    "I-total-participants",
-    "B-intervention-participants",
-    "I-intervention-participants",
-    "B-control-participants",
-    "I-control-participants",
-    "B-age",
-    "I-age",
-    "B-eligibility",
-    "I-eligibility",
-    "B-ethinicity",
-    "I-ethinicity",
-    "B-condition",
-    "I-condition",
-    "B-location",
-    "I-location",
-    "B-intervention",
-    "I-intervention",
-    "B-control",
-    "I-control",
-    "B-outcome",
-    "I-outcome",
-    "B-outcome-measure",
-    "I-outcome-measure",
-    "B-iv-bin-abs",
-    "I-iv-bin-abs",
-    "B-cv-bin-abs",
-    "I-cv-bin-abs",
-    "B-iv-bin-percent",
-    "I-iv-bin-percent",
-    "B-cv-bin-percent",
-    "I-cv-bin-percent",
-    "B-iv-cont-mean",
-    "I-iv-cont-mean",
-    "B-cv-cont-mean",
-    "I-cv-cont-mean",
-    "B-iv-cont-median",
-    "I-iv-cont-median",
-    "B-cv-cont-median",
-    "I-cv-cont-median",
-    "B-iv-cont-sd",
-    "I-iv-cont-sd",
-    "B-cv-cont-sd",
-    "I-cv-cont-sd",
-    "B-iv-cont-q1",
-    "I-iv-cont-q1",
-    "B-cv-cont-q1",
-    "I-cv-cont-q1",
-    "B-iv-cont-q3",
-    "I-iv-cont-q3",
-    "B-cv-cont-q3",
-    "I-cv-cont-q3",
-]
+_URL = "https://raw.githubusercontent.com/Martin-Masson/pico-breast-cancer/main/pico_conll/"
+_TRAINING_FILE = "train.txt"
+_DEV_FILE = "dev.txt"
+_TEST_FILE = "test.txt"
 
 
 class PicoBreastCancer(datasets.GeneratorBasedBuilder):
@@ -93,7 +43,65 @@ class PicoBreastCancer(datasets.GeneratorBasedBuilder):
                 {
                     "id": datasets.Value("string"), 
                     "tokens": datasets.Sequence(datasets.Value("string")), 
-                    "ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=_TAGS)),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names= [
+                                "O",
+                                "B-total-participants",
+                                "I-total-participants",
+                                "B-intervention-participants",
+                                "I-intervention-participants",
+                                "B-control-participants",
+                                "I-control-participants",
+                                "B-age",
+                                "I-age",
+                                "B-eligibility",
+                                "I-eligibility",
+                                "B-ethinicity",
+                                "I-ethinicity",
+                                "B-condition",
+                                "I-condition",
+                                "B-location",
+                                "I-location",
+                                "B-intervention",
+                                "I-intervention",
+                                "B-control",
+                                "I-control",
+                                "B-outcome",
+                                "I-outcome",
+                                "B-outcome-measure",
+                                "I-outcome-measure",
+                                "B-iv-bin-abs",
+                                "I-iv-bin-abs",
+                                "B-cv-bin-abs",
+                                "I-cv-bin-abs",
+                                "B-iv-bin-percent",
+                                "I-iv-bin-percent",
+                                "B-cv-bin-percent",
+                                "I-cv-bin-percent",
+                                "B-iv-cont-mean",
+                                "I-iv-cont-mean",
+                                "B-cv-cont-mean",
+                                "I-cv-cont-mean",
+                                "B-iv-cont-median",
+                                "I-iv-cont-median",
+                                "B-cv-cont-median",
+                                "I-cv-cont-median",
+                                "B-iv-cont-sd",
+                                "I-iv-cont-sd",
+                                "B-cv-cont-sd",
+                                "I-cv-cont-sd",
+                                "B-iv-cont-q1",
+                                "I-iv-cont-q1",
+                                "B-cv-cont-q1",
+                                "I-cv-cont-q1",
+                                "B-iv-cont-q3",
+                                "I-iv-cont-q3",
+                                "B-cv-cont-q3",
+                                "I-cv-cont-q3",
+                            ]
+                        )
+                    ),
                 }
             ),
             supervised_keys=None,
@@ -102,25 +110,25 @@ class PicoBreastCancer(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URL)
-        data_files = {
-            "train": os.path.join(data_dir, "train.txt"),
-            "dev": os.path.join(data_dir, "dev.txt"),
-            "test": os.path.join(data_dir, "test.txt"),
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
         }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
 
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
         ]
 
     def _generate_examples(self, filepath):
-        idx = 0
-        tokens = []
-        ner_tags = []
-
+        logger.info("⏳ Generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as f:
+            idx = 0
+            tokens = []
+            ner_tags = []
             for line in f:
                 if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                     if tokens: