Update pico-breast-cancer.py

pico-breast-cancer.py

@@ -22,11 +22,12 @@ The abstracts were annotated using BRAT (https://brat.nlplab.org/) and later
 converted to IOB format.
 """
 
-_URL = "https://raw.githubusercontent.com/Martin-Masson/pico-breast-cancer/main/pico_conll/"
+_URL = "https://raw.githubusercontent.com/Martin-Masson/pico-breast-cancer/main/pico_iob/"
 _TRAINING_FILE = "train.txt"
 _DEV_FILE = "dev.txt"
 _TEST_FILE = "test.txt"
 _ALL_FILE = "all.txt"
+_SHORT_FILE = "all_short.txt"
 
 
 class PicoBreastCancerConfig(datasets.BuilderConfig):
@@ -46,6 +47,7 @@ class PicoBreastCancer(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
         PicoBreastCancerConfig(name="split", version=datasets.Version("1.0.0"), description="Train/Validation/Test splits containing respectively 80%/10%/10% of all the abstracts."),
         PicoBreastCancerConfig(name="all", version=datasets.Version("1.0.0"), description="Single dataset containing all the absracts for custom splitting."),
+        PicoBreastCancerConfig(name="short", version=datasets.Version("1.0.0"), description="Similar to 'all' but with shortened examples to account for embeddings size."),
     ]
 
     DEFAULT_CONFIG_NAME = "split"
@@ -130,6 +132,7 @@ class PicoBreastCancer(datasets.GeneratorBasedBuilder):
             "dev": f"{_URL}{_DEV_FILE}",
             "test": f"{_URL}{_TEST_FILE}",
             "all": f"{_URL}{_ALL_FILE}",
+            "short": f"{_URL}{_SHORT_FILE_FILE}",
         }
         downloaded_files = dl_manager.download_and_extract(urls_to_download)
         if self.config.name == "split":
@@ -139,9 +142,11 @@ class PicoBreastCancer(datasets.GeneratorBasedBuilder):
                 datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
             ]
         else:
+            file = "all" if self.config.name == "all" else "short"
             return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["all"]}),
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files[file]}),
             ]
+            
 
     def _generate_examples(self, filepath):
         logger.info("⏳ Generating examples from = %s", filepath)