updating data files and loader script

sd-nlp.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and Thomas Lemberger.
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,69 +12,74 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""SourceDataNLP dataset."""
+
+
+# template from : https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py
+
+"""Loading script for the biolang dataset for language modeling in biology."""
 
 from __future__ import absolute_import, division, print_function
 
 import json
-from pathlib import Path
-
+import pdb
 import datasets
 
 
-_NER_LABEL_NAMES = [
-    "O",
-    "I-SMALL_MOLECULE",
-    "B-SMALL_MOLECULE",
-    "I-GENEPROD",
-    "B-GENEPROD",
-    "I-SUBCELLULAR",
-    "B-SUBCELLULAR",
-    "I-CELL",
-    "B-CELL",
-    "I-TISSUE",
-    "B-TISSUE",
-    "I-ORGANISM",
-    "B-ORGANISM",
-    "I-EXP_ASSAY",
-    "B-EXP_ASSAY",
-]
-_SEMANTIC_ROLES_LABEL_NAMES = ["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]
-_BORING_LABEL_NAMES = ["O", "I-BORING", "B-BORING"]
-_PANEL_START_NAMES = ["O", "B-PANEL_START"]
-
-_CITATION = """\
-@Unpublished{
-    huggingface: dataset,
-    title = {SourceData NLP},
-    authors={Thomas Lemberger, EMBO},
-    year={2021}
-}
-"""
-
-_DESCRIPTION = """\
-This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/EMBO/sd-nlp"
-
-_LICENSE = "CC-BY 4.0"
-
-_URLS = {
-    "NER": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
-    "ROLES": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
-    "BORING": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
-    "PANELIZATION": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panelization.zip",
-}
-
 class SourceDataNLP(datasets.GeneratorBasedBuilder):
     """SourceDataNLP provides datasets to train NLP tasks in cell and molecular biology."""
 
+    _NER_LABEL_NAMES = [
+        "O",
+        "I-SMALL_MOLECULE",
+        "B-SMALL_MOLECULE",
+        "I-GENEPROD",
+        "B-GENEPROD",
+        "I-SUBCELLULAR",
+        "B-SUBCELLULAR",
+        "I-CELL",
+        "B-CELL",
+        "I-TISSUE",
+        "B-TISSUE",
+        "I-ORGANISM",
+        "B-ORGANISM",
+        "I-EXP_ASSAY",
+        "B-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"]
+    _PANEL_START_NAMES = ["O", "B-PANEL_START"]
+
+    _CITATION = """\
+    @Unpublished{
+        huggingface: dataset,
+        title = {SourceData NLP},
+        authors={Thomas Lemberger, EMBO},
+        year={2021}
+    }
+    """
+
+    _DESCRIPTION = """\
+    This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain.
+    """
+
+    _HOMEPAGE = "https://huggingface.co/datasets/EMBO/sd-nlp"
+
+    _LICENSE = "CC-BY 4.0"
+
+    _URLS = {
+        "NER": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
+        "ROLES": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
+        "BORING": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
+        "PANELIZATION": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_figs.zip",
+    }
+
     VERSION = datasets.Version("0.0.1")
 
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name="NER", version="0.0.1", description="Dataset for entity recognition"),
-        datasets.BuilderConfig(name="ROLES", version="0.0.1", description="Dataset for semantic roles."),
+        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="PANELIZATION",
@@ -91,18 +96,30 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
                 {
                     "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
                     "labels": datasets.Sequence(
-                        feature=datasets.ClassLabel(num_classes=len(_NER_LABEL_NAMES), names=_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 == "ROLES":
+        elif self.config.name == "GENEPROD_ROLES":
             features = datasets.Features(
                 {
                     "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
                     "labels": datasets.Sequence(
                         feature=datasets.ClassLabel(
-                            num_classes=len(_SEMANTIC_ROLES_LABEL_NAMES), names=_SEMANTIC_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("int32")),
+                    "labels": datasets.Sequence(
+                        feature=datasets.ClassLabel(
+                            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")),
@@ -113,7 +130,7 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
                 {
                     "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
                     "labels": datasets.Sequence(
-                        feature=datasets.ClassLabel(num_classes=len(_BORING_LABEL_NAMES), names=_BORING_LABEL_NAMES)
+                        feature=datasets.ClassLabel(num_classes=len(self._BORING_LABEL_NAMES), names=self._BORING_LABEL_NAMES)
                     ),
                 }
             )
@@ -122,18 +139,19 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
                 {
                     "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
                     "labels": datasets.Sequence(
-                        feature=datasets.ClassLabel(num_classes=len(_PANEL_START_NAMES), names=_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")),
                 }
             )
 
         return datasets.DatasetInfo(
-            description=_DESCRIPTION,
+            description=self._DESCRIPTION,
             features=features,
             supervised_keys=("input_ids", "labels"),
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
+            homepage=self._HOMEPAGE,
+            license=self._LICENSE,
+            citation=self._CITATION,
         )
 
     def _split_generators(self, dl_manager: datasets.DownloadManager):
@@ -142,12 +160,12 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
         if self.config.data_dir:
             data_dir = self.config.data_dir
         else:
-            url = _URLS[self.config.name]
+            url = self._URLS[self.config.name]
             data_dir = dl_manager.download_and_extract(url)
-            if self.config.name in ["NER", "ROLES", "BORING"]:
+            if self.config.name in ["NER", "GENEPROD_ROLES", "SMALL_MOL_ROLES", "BORING"]:
                 data_dir += "/sd_panels"
             elif self.config.name == "PANELIZATION":
-                data_dir += "/sd_panelization"
+                data_dir += "/sd_figs"
             else:
                 raise ValueError(f"unkonwn config name: {self.config.name}")
         return [
@@ -183,22 +201,38 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
             for id_, row in enumerate(f):
                 data = json.loads(row)
                 if self.config.name == "NER":
-                    labels_type = data["label_ids"]["entity_types"]
-                    tag_mask = [0 if tag == "O" else 1 for tag in labels_type]
-                    yield id_, {"input_ids": data["input_ids"], "labels": labels_type, "tag_mask": tag_mask}
-                elif self.config.name == "ROLES":
-                    labels_type = data["label_ids"]["entity_types"]
+                    labels = data["label_ids"]["entity_types"]
+                    tag_mask = [0 if tag == "O" else 1 for tag in labels]
+                    yield id_, {
+                        "input_ids": data["input_ids"],
+                        "labels": labels,
+                        "tag_mask": tag_mask,
+                    }
+                elif self.config.name == "GENEPROD_ROLES":
+                    labels = data["label_ids"]["entity_types"]
                     geneprod = ["B-GENEPROD", "I-GENEPROD", "B-PROTEIN", "I-PROTEIN", "B-GENE", "I-GENE"]
-                    tag_mask = [1 if t in geneprod else 0 for t in labels_type]
+                    tag_mask = [1 if t in geneprod else 0 for t in labels]
                     yield id_, {
                         "input_ids": data["input_ids"],
                         "labels": data["label_ids"]["geneprod_roles"],
                         "tag_mask": tag_mask,
                     }
+                elif self.config.name == "SMALL_MOL_ROLES":
+                    labels = data["label_ids"]["entity_types"]
+                    small_mol = ["B-SMALL_MOLECULE", "I-SMALL_MOLECULE"]
+                    tag_mask = [1 if t in small_mol else 0 for t in labels]
+                    yield id_, {
+                        "input_ids": data["input_ids"],
+                        "labels": data["label_ids"]["small_mol_roles"],
+                        "tag_mask": tag_mask,
+                    }
                 elif self.config.name == "BORING":
                     yield id_, {"input_ids": data["input_ids"], "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["input_ids"],
                         "labels": data["label_ids"]["panel_start"],
+                        "tag_mask": tag_mask,
                     }

sd_figs.zip

@@ -1,3 +1,3 @@
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-oid sha256:a2b41ac167b5a8f1408f2af577ccd86049dce0211c1f742fd61d6233bddb5631
-size 16286918
+oid sha256:fbf923d27a98176e57949103906f5bef4fd829bfc9526ecd0e1d1bc24f1cea72
+size 21662607

sd_panels.zip

@@ -1,3 +1,3 @@
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-oid sha256:45e9b09acd2238d71b07fcf0f33428d8b51a2dcfd476aaa760acf41d6d21cf4b
-size 22501489
+oid sha256:d88d2267474583a71010c80015bb1659c3d6f547c3323a072a5c62617eca3316
+size 36651093