All tasks working

sd-nlp-non-tokenized.py

@@ -19,9 +19,7 @@
 from __future__ import absolute_import, division, print_function
 
 import json
-import pdb
 import datasets
-import os
 
 _BASE_URL = "https://huggingface.co/datasets/EMBO/sd-nlp-non-tokenized/resolve/main/"
 
@@ -71,8 +69,6 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
 
     _URLS = {
         "NER": f"{_BASE_URL}sd_panels_general_tokenization.zip",
-        "ROLES": f"{_BASE_URL}sd_panels_general_tokenization.zip",
-        "BORING": f"{_BASE_URL}sd_panels_general_tokenization.zip",
         "PANELIZATION": f"{_BASE_URL}sd_fig_general_tokenization.zip",
     }
     BUILDER_CONFIGS = [
@@ -95,49 +91,57 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
                 {
                     "words": datasets.Sequence(feature=datasets.Value("string")),
                     "labels": datasets.Sequence(
-                        feature=datasets.ClassLabel(num_classes=len(self._NER_LABEL_NAMES), names=self._NER_LABEL_NAMES)
+                        feature=datasets.ClassLabel(num_classes=len(self._NER_LABEL_NAMES),
+                                                    names=self._NER_LABEL_NAMES)
                     ),
+                    "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
                 }
             )
         elif self.config.name == "GENEPROD_ROLES":
             features = datasets.Features(
                 {
-                    "input_ids": datasets.Sequence(feature=datasets.Value("string")),
+                    "words": datasets.Sequence(feature=datasets.Value("string")),
                     "labels": datasets.Sequence(
                         feature=datasets.ClassLabel(
-                            num_classes=len(self._SEMANTIC_GENEPROD_ROLES_LABEL_NAMES), names=self._SEMANTIC_GENEPROD_ROLES_LABEL_NAMES
+                            num_classes=len(self._SEMANTIC_GENEPROD_ROLES_LABEL_NAMES),
+                            names=self._SEMANTIC_GENEPROD_ROLES_LABEL_NAMES
                         )
                     ),
-
+                    "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
                 }
             )
         elif self.config.name == "SMALL_MOL_ROLES":
             features = datasets.Features(
                 {
-                    "input_ids": datasets.Sequence(feature=datasets.Value("string")),
+                    "words": datasets.Sequence(feature=datasets.Value("string")),
                     "labels": datasets.Sequence(
                         feature=datasets.ClassLabel(
-                            num_classes=len(self._SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES), names=self._SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES
+                            num_classes=len(self._SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES),
+                            names=self._SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES
                         )
                     ),
+                    "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
                 }
             )
         elif self.config.name == "BORING":
             features = datasets.Features(
                 {
-                    "input_ids": datasets.Sequence(feature=datasets.Value("string")),
+                    "words": datasets.Sequence(feature=datasets.Value("string")),
                     "labels": datasets.Sequence(
-                        feature=datasets.ClassLabel(num_classes=len(self._BORING_LABEL_NAMES), names=self._BORING_LABEL_NAMES)
+                        feature=datasets.ClassLabel(num_classes=len(self._BORING_LABEL_NAMES),
+                                                    names=self._BORING_LABEL_NAMES)
                     ),
                 }
             )
         elif self.config.name == "PANELIZATION":
             features = datasets.Features(
                 {
-                    "input_ids": datasets.Sequence(feature=datasets.Value("string")),
+                    "words": datasets.Sequence(feature=datasets.Value("string")),
                     "labels": datasets.Sequence(
-                        feature=datasets.ClassLabel(num_classes=len(self._PANEL_START_NAMES), names=self._PANEL_START_NAMES)
+                        feature=datasets.ClassLabel(num_classes=len(self._PANEL_START_NAMES),
+                                                    names=self._PANEL_START_NAMES)
                     ),
+                    "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
                 }
             )
 
@@ -153,11 +157,14 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager: datasets.DownloadManager):
         """Returns SplitGenerators.
         Uses local files if a data_dir is specified. Otherwise downloads the files from their official url."""
-        url = self._URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(url)
+
         if self.config.name in ["NER", "GENEPROD_ROLES", "SMALL_MOL_ROLES", "BORING"]:
+            url = self._URLS["NER"]
+            data_dir = dl_manager.download_and_extract(url)
             data_dir += "/sd_panels_general_tokenization"
         elif self.config.name == "PANELIZATION":
+            url = self._URLS[self.config.name]
+            data_dir = dl_manager.download_and_extract(url)
             data_dir += "/sd_fig_general_tokenization"
         else:
             raise ValueError(f"unkonwn config name: {self.config.name}")
@@ -185,6 +192,7 @@ 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)
@@ -208,7 +216,7 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
                         "tag_mask": tag_mask,
                     }
                 elif self.config.name == "SMALL_MOL_ROLES":
-                    labels = data["words"]["small_mol_roles"]
+                    labels = data["label_ids"]["small_mol_roles"]
                     small_mol = ["B-SMALL_MOLECULE", "I-SMALL_MOLECULE"]
                     tag_mask = [1 if t in small_mol else 0 for t in labels]
                     yield id_, {
@@ -217,12 +225,13 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
                         "tag_mask": tag_mask,
                     }
                 elif self.config.name == "BORING":
-                    yield id_, {"words": data["words"], "labels": data["label_ids"]["boring"]}
+                    yield id_, {"words": data["words"],
+                                "labels": data["label_ids"]["boring"]}
                 elif self.config.name == "PANELIZATION":
                     labels = data["label_ids"]["panel_start"]
                     tag_mask = [1 if t == "B-PANEL_START" else 0 for t in labels]
                     yield id_, {
-                        "input_ids": data["words"],
+                        "words": data["words"],
                         "labels": data["label_ids"]["panel_start"],
                         "tag_mask": tag_mask,
                     }