Update sd-nlp-non-tokenized.py

sd-nlp-non-tokenized.py

@@ -28,31 +28,31 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
 
     _NER_LABEL_NAMES = [
         "O",
-        "I-SMALL_MOLECULE",
         "B-SMALL_MOLECULE",
-        "I-GENEPROD",
+        "I-SMALL_MOLECULE",
         "B-GENEPROD",
-        "I-SUBCELLULAR",
+        "I-GENEPROD",
         "B-SUBCELLULAR",
-        "I-CELL",
+        "I-SUBCELLULAR",
         "B-CELL",
-        "I-TISSUE",
+        "I-CELL",
         "B-TISSUE",
-        "I-ORGANISM",
+        "I-TISSUE",
         "B-ORGANISM",
-        "I-EXP_ASSAY",
+        "I-ORGANISM",
         "B-EXP_ASSAY",
+        "I-EXP_ASSAY",
     ]
-    _SEMANTIC_GENEPROD_ROLES_LABEL_NAMES =  ["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]
-    _SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES = ["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]
-    _BORING_LABEL_NAMES = ["O", "I-BORING", "B-BORING"]
+    _SEMANTIC_GENEPROD_ROLES_LABEL_NAMES =  ["O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "B-MEASURED_VAR", "I-MEASURED_VAR"]
+    _SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES = ["O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "B-MEASURED_VAR", "I-MEASURED_VAR"]
+    _BORING_LABEL_NAMES = ["O", "B-BORING", "I-BORING"]
     _PANEL_START_NAMES = ["O", "B-PANEL_START"]
 
     _CITATION = """\
     @Unpublished{
         huggingface: dataset,
         title = {SourceData NLP},
-        authors={Thomas Lemberger, EMBO},
+        authors={Thomas Lemberger & Jorge Abreu-Vicente, EMBO},
         year={2021}
     }
     """
@@ -65,20 +65,20 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
 
     _LICENSE = "CC-BY 4.0"
 
-    VERSION = datasets.Version("0.0.1")
+    VERSION = datasets.Version("1.0.0")
 
     _URLS = {
         "NER": f"{_BASE_URL}sd_panels_general_tokenization.zip",
         "PANELIZATION": f"{_BASE_URL}sd_fig_general_tokenization.zip",
     }
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="NER", version="0.0.1", description="Dataset for entity recognition"),
-        datasets.BuilderConfig(name="GENEPROD_ROLES", version="0.0.1", description="Dataset for semantic roles."),
-        datasets.BuilderConfig(name="SMALL_MOL_ROLES", version="0.0.1", description="Dataset for semantic roles."),
-        datasets.BuilderConfig(name="BORING", version="0.0.1", description="Dataset for semantic roles."),
+        datasets.BuilderConfig(name="NER", version=VERSION, description="Dataset for entity recognition"),
+        datasets.BuilderConfig(name="GENEPROD_ROLES", version=VERSION, description="Dataset for semantic roles."),
+        datasets.BuilderConfig(name="SMALL_MOL_ROLES", version=VERSION, description="Dataset for semantic roles."),
+        datasets.BuilderConfig(name="BORING", version=VERSION, description="Dataset for semantic roles."),
         datasets.BuilderConfig(
             name="PANELIZATION",
-            version="0.0.1",
+            version=VERSION,
             description="Dataset for figure legend segmentation into panel-specific legends.",
         ),
     ]
@@ -192,7 +192,6 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
         """Yields examples. This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
         It is in charge of opening the given file and yielding (key, example) tuples from the dataset
         The key is not important, it's more here for legacy reason (legacy from tfds)"""
-        print(" This line is taking place")
 
         with open(filepath, encoding="utf-8") as f:
             # logger.info("⏳ Generating examples from = %s", filepath)
@@ -235,3 +234,4 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
                         "labels": data["label_ids"]["panel_start"],
                         "tag_mask": tag_mask,
                     }
+