cahngeS

PLOD-filtered.py

@@ -14,14 +14,6 @@ This is the dataset repository for PLOD Dataset accepted to be published at LREC
 The dataset can help build sequence labelling models for the task Abbreviation Detection.
 """
 
-_TRAINING_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-train70-filtered-pos_bio.json"
-_DEV_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-val15-filtered-pos_bio.json"
-_TEST_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-test15-filtered-pos_bio.json"
-
-_TRAINING_FILE = "PLOS-train70-filtered-pos_bio.json"
-_DEV_FILE = "PLOS-val15-filtered-pos_bio.json"
-_TEST_FILE = "PLOS-test15-filtered-pos_bio.json"
-
 class PLODfilteredConfig(datasets.BuilderConfig):
     """BuilderConfig for Conll2003"""
 
@@ -89,25 +81,49 @@ class PLODfilteredConfig(datasets.GeneratorBasedBuilder):
             homepage="https://github.com/surrey-nlp/PLOD-AbbreviationDetection",
             citation=_CITATION,
         )
+    # _TRAINING_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-train70-filtered-pos_bio.json"
+    # _DEV_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-val15-filtered-pos_bio.json"
+    # _TEST_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-test15-filtered-pos_bio.json"
 
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        downloaded_train = dl_manager.download_and_extract(_TRAINING_FILE_URL)
-        downloaded_val = dl_manager.download_and_extract(_DEV_FILE_URL)
-        downloaded_test = dl_manager.download_and_extract(_TEST_FILE_URL)
+    # _TRAINING_FILE = "PLOS-train70-filtered-pos_bio.json"
+    # _DEV_FILE = "PLOS-val15-filtered-pos_bio.json"
+    # _TEST_FILE = "PLOS-test15-filtered-pos_bio.json"
 
-        data_files = {
-            "train": _TRAINING_FILE,
-            "dev": _DEV_FILE,
-            "test": _TEST_FILE,
-        }
+    _URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/"
+    _URLS = {
+        "train": _URL + "PLOS-train70-filtered-pos_bio.json",
+        "dev": _URL + "PLOS-val15-filtered-pos_bio.json",
+        "test": _URL + "PLOS-test15-filtered-pos_bio.json"
+    }
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls_to_download = self._URLS
+        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 _split_generators(self, dl_manager):
+    #     """Returns SplitGenerators."""
+    #     downloaded_train = dl_manager.download_and_extract(_TRAINING_FILE_URL)
+    #     downloaded_val = dl_manager.download_and_extract(_DEV_FILE_URL)
+    #     downloaded_test = dl_manager.download_and_extract(_TEST_FILE_URL)
+
+    #     data_files = {
+    #         "train": _TRAINING_FILE,
+    #         "dev": _DEV_FILE,
+    #         "test": _TEST_FILE,
+    #     }
+
+    #     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"]}),
+    #     ]
+
     def _generate_examples(self, filepath):
         logger.info("⏳ Generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as f: