Add dataloader for full SourceData (including entity links) (#4)

- Add dataloader for full SourceData (including entity links) (0367281d98d9e4c81a2bb98caf2f621fb4f6bbc0)


Co-authored-by: David Kartchner <[email protected]>

SourceData.py

@@ -19,10 +19,12 @@
 from __future__ import absolute_import, division, print_function
 
 import json
+import os
 import datasets
 
 _BASE_URL = "https://huggingface.co/datasets/EMBO/SourceData/resolve/main/"
 
+
 class SourceData(datasets.GeneratorBasedBuilder):
     """SourceDataNLP provides datasets to train NLP tasks in cell and molecular biology."""
 
@@ -45,19 +47,26 @@ class SourceData(datasets.GeneratorBasedBuilder):
         "B-DISEASE",
         "I-DISEASE",
         "B-CELL_LINE",
-        "I-CELL_LINE"
+        "I-CELL_LINE",
+    ]
+    _SEMANTIC_ROLES = [
+        "O",
+        "B-CONTROLLED_VAR",
+        "I-CONTROLLED_VAR",
+        "B-MEASURED_VAR",
+        "I-MEASURED_VAR",
     ]
-    _SEMANTIC_ROLES =  ["O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "B-MEASURED_VAR", "I-MEASURED_VAR"]
     _PANEL_START_NAMES = ["O", "B-PANEL_START", "I-PANEL_START"]
     _ROLES_MULTI = ["O", "GENEPROD", "SMALL_MOLECULE"]
 
     _CITATION = """\
-    @Unpublished{
-        huggingface: dataset,
-        title = {SourceData NLP},
-        authors={Thomas Lemberger & Jorge Abreu-Vicente, EMBO},
-        year={2023}
-    }
+@article{abreu2023sourcedata,
+  title={The SourceData-NLP dataset: integrating curation into scientific publishing
+  for training large language models},
+  author={Abreu-Vicente, Jorge and Sonntag, Hannah and Eidens, Thomas and Lemberger, Thomas},
+  journal={arXiv preprint arXiv:2310.20440},
+  year={2023}
+}
     """
 
     _DESCRIPTION = """\
@@ -70,32 +79,73 @@ class SourceData(datasets.GeneratorBasedBuilder):
 
     DEFAULT_CONFIG_NAME = "NER"
 
-    _LATEST_VERSION = "1.0.0"
+    _LATEST_VERSION = "2.0.3"  # Should this be updated to 2.0.3
 
     def _info(self):
-        VERSION = self.config.version if self.config.version not in ["0.0.0", "latest"] else self._LATEST_VERSION
+        VERSION = (
+            self.config.version
+            if self.config.version not in ["0.0.0", "latest"]
+            else self._LATEST_VERSION
+        )
         self._URLS = {
             "NER": f"{_BASE_URL}token_classification/v_{VERSION}/ner/",
             "PANELIZATION": f"{_BASE_URL}token_classification/v_{VERSION}/panelization/",
             "ROLES_GP": f"{_BASE_URL}token_classification/v_{VERSION}/roles_gene/",
             "ROLES_SM": f"{_BASE_URL}token_classification/v_{VERSION}/roles_small_mol/",
             "ROLES_MULTI": f"{_BASE_URL}token_classification/v_{VERSION}/roles_multi/",
+            "FULL": os.path.join(
+                _BASE_URL,
+                "bigbio",
+                # f"v_{VERSION}",
+            ),
         }
         self.BUILDER_CONFIGS = [
-            datasets.BuilderConfig(name="NER", version=VERSION, description="Dataset for named-entity recognition."),
-            datasets.BuilderConfig(name="PANELIZATION", version=VERSION, description="Dataset to separate figure captions into panels."),
-            datasets.BuilderConfig(name="ROLES_GP", version=VERSION, description="Dataset for semantic roles of gene products."),
-            datasets.BuilderConfig(name="ROLES_SM", version=VERSION, description="Dataset for semantic roles of small molecules."),
-            datasets.BuilderConfig(name="ROLES_MULTI", version=VERSION, description="Dataset to train roles. ROLES_GP and ROLES_SM at once."),
+            datasets.BuilderConfig(
+                name="NER",
+                version=VERSION,
+                description="Dataset for named-entity recognition.",
+            ),
+            datasets.BuilderConfig(
+                name="PANELIZATION",
+                version=VERSION,
+                description="Dataset to separate figure captions into panels.",
+            ),
+            datasets.BuilderConfig(
+                name="ROLES_GP",
+                version=VERSION,
+                description="Dataset for semantic roles of gene products.",
+            ),
+            datasets.BuilderConfig(
+                name="ROLES_SM",
+                version=VERSION,
+                description="Dataset for semantic roles of small molecules.",
+            ),
+            datasets.BuilderConfig(
+                name="ROLES_MULTI",
+                version=VERSION,
+                description="Dataset to train roles. ROLES_GP and ROLES_SM at once.",
+            ),
+            datasets.BuilderConfig(
+                name="FULL",
+                version=VERSION,
+                description="Full dataset including all NER + entity linking annotations, links to figure images, etc.",
+            ),
+            # datasets.BuilderConfig(
+            #     name="BIGBIO_KB",
+            #     version=VERSION,
+            #     description="Full dataset formatted according to BigBio KB schema (see https://huggingface.co/bigbio).  Includes all NER + entity linking annotations.",
+            # ),
         ]
-        
+
         if self.config.name in ["NER", "default"]:
             features = datasets.Features(
                 {
                     "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,
+                        )
                     ),
                     # "is_category": datasets.Sequence(feature=datasets.Value("int8")),
                     "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
@@ -109,7 +159,7 @@ class SourceData(datasets.GeneratorBasedBuilder):
                     "labels": datasets.Sequence(
                         feature=datasets.ClassLabel(
                             num_classes=len(self._SEMANTIC_ROLES),
-                            names=self._SEMANTIC_ROLES
+                            names=self._SEMANTIC_ROLES,
                         )
                     ),
                     # "is_category": datasets.Sequence(feature=datasets.Value("int8")),
@@ -124,7 +174,7 @@ class SourceData(datasets.GeneratorBasedBuilder):
                     "labels": datasets.Sequence(
                         feature=datasets.ClassLabel(
                             num_classes=len(self._SEMANTIC_ROLES),
-                            names=self._SEMANTIC_ROLES
+                            names=self._SEMANTIC_ROLES,
                         )
                     ),
                     # "is_category": datasets.Sequence(feature=datasets.Value("int8")),
@@ -139,13 +189,12 @@ class SourceData(datasets.GeneratorBasedBuilder):
                     "labels": datasets.Sequence(
                         feature=datasets.ClassLabel(
                             num_classes=len(self._SEMANTIC_ROLES),
-                            names=self._SEMANTIC_ROLES
+                            names=self._SEMANTIC_ROLES,
                         )
                     ),
                     "is_category": datasets.Sequence(
                         feature=datasets.ClassLabel(
-                            num_classes=len(self._ROLES_MULTI),
-                            names=self._ROLES_MULTI
+                            num_classes=len(self._ROLES_MULTI), names=self._ROLES_MULTI
                         )
                     ),
                     "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
@@ -157,13 +206,57 @@ class SourceData(datasets.GeneratorBasedBuilder):
                 {
                     "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")),
                 }
             )
 
+        elif self.config.name == "FULL":
+            features = datasets.Features(
+                {
+                    "doi": datasets.Value("string"),
+                    "abstract": datasets.Value("string"),
+                    # "split": datasets.Value("string"),
+                    "figures": [
+                        {
+                            "fig_id": datasets.Value("string"),
+                            "label": datasets.Value("string"),
+                            "fig_graphic_url": datasets.Value("string"),
+                            "panels": [
+                                {
+                                    "panel_id": datasets.Value("string"),
+                                    "text": datasets.Value("string"),
+                                    "panel_graphic_url": datasets.Value("string"),
+                                    "entities": [
+                                        {
+                                            "annotation_id": datasets.Value("string"),
+                                            "source": datasets.Value("string"),
+                                            "category": datasets.Value("string"),
+                                            "entity_type": datasets.Value("string"),
+                                            "role": datasets.Value("string"),
+                                            "text": datasets.Value("string"),
+                                            "ext_ids": datasets.Value("string"),
+                                            "norm_text": datasets.Value("string"),
+                                            "ext_dbs": datasets.Value("string"),
+                                            "in_caption": datasets.Value("bool"),
+                                            "ext_names": datasets.Value("string"),
+                                            "ext_tax_ids": datasets.Value("string"),
+                                            "ext_tax_names": datasets.Value("string"),
+                                            "ext_urls": datasets.Value("string"),
+                                            "offsets": [datasets.Value("int64")],
+                                        }
+                                    ],
+                                }
+                            ],
+                        }
+                    ],
+                }
+            )
+
         return datasets.DatasetInfo(
             description=self._DESCRIPTION,
             features=features,
@@ -172,38 +265,49 @@ class SourceData(datasets.GeneratorBasedBuilder):
             license=self._LICENSE,
             citation=self._CITATION,
         )
-    
+
     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."""
+        Uses local files if a data_dir is specified. Otherwise downloads the files from their official url.
+        """
 
         try:
             config_name = self.config.name if self.config.name != "default" else "NER"
-            urls = [
-                self._URLS[config_name]+"train.jsonl",
-                self._URLS[config_name]+"test.jsonl",
-                self._URLS[config_name]+"validation.jsonl"
-            ]
-            data_files = dl_manager.download(urls)
+
+            if config_name == "FULL":
+                url = os.path.join(
+                    self._URLS[config_name],
+                    #    "source_data_full.zip"
+                    "source_data_json_splits_2.0.2.zip",
+                )
+                data_dir = dl_manager.download_and_extract(url)
+                data_files = [
+                    os.path.join(data_dir, filename)
+                    for filename in ["train.jsonl", "test.jsonl", "validation.jsonl"]
+                ]
+            else:
+                urls = [
+                    os.path.join(self._URLS[config_name], "train.jsonl"),
+                    os.path.join(self._URLS[config_name], "test.jsonl"),
+                    os.path.join(self._URLS[config_name], "validation.jsonl"),
+                ]
+                data_files = dl_manager.download(urls)
         except:
             raise ValueError(f"unkonwn config name: {self.config.name}")
-        
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_files[0]},
+                gen_kwargs={"filepath": data_files[0]},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": data_files[1]},
+                gen_kwargs={"filepath": data_files[1]},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": data_files[2]},
+                gen_kwargs={"filepath": data_files[2]},
             ),
         ]
 
@@ -212,40 +316,45 @@ class SourceData(datasets.GeneratorBasedBuilder):
         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)"""
 
+        no_panels = 0
+        no_entities = 0
+        has_panels = 0
+        has_entities = 0
+
         with open(filepath, encoding="utf-8") as f:
             # logger.info("⏳ Generating examples from = %s", filepath)
             for id_, row in enumerate(f):
-                data = json.loads(row)
+                data = json.loads(row.strip())
                 if self.config.name in ["NER", "default"]:
                     yield id_, {
                         "words": data["words"],
                         "labels": data["labels"],
                         "tag_mask": data["is_category"],
-                        "text": data["text"]
+                        "text": data["text"],
                     }
                 elif self.config.name == "ROLES_GP":
                     yield id_, {
                         "words": data["words"],
                         "labels": data["labels"],
                         "tag_mask": data["is_category"],
-                        "text": data["text"]
+                        "text": data["text"],
                     }
                 elif self.config.name == "ROLES_MULTI":
                     labels = data["labels"]
-                    tag_mask = [1 if t!=0 else 0 for t in labels]
+                    tag_mask = [1 if t != 0 else 0 for t in labels]
                     yield id_, {
                         "words": data["words"],
                         "labels": data["labels"],
                         "tag_mask": tag_mask,
                         "is_category": data["is_category"],
-                        "text": data["text"]
+                        "text": data["text"],
                     }
                 elif self.config.name == "ROLES_SM":
                     yield id_, {
                         "words": data["words"],
                         "labels": data["labels"],
                         "tag_mask": data["is_category"],
-                        "text": data["text"]
+                        "text": data["text"],
                     }
                 elif self.config.name == "PANELIZATION":
                     labels = data["labels"]
@@ -256,4 +365,66 @@ class SourceData(datasets.GeneratorBasedBuilder):
                         "tag_mask": tag_mask,
                     }
 
+                elif self.config.name == "FULL":
+                    doc_figs = data["figures"]
+                    all_figures = []
+                    for fig in doc_figs:
+                        all_panels = []
+                        figure = {
+                            "fig_id": fig["fig_id"],
+                            "label": fig["label"],
+                            "fig_graphic_url": fig["fig_graphic_url"],
+                        }
+
+                        for p in fig["panels"]:
+                            panel = {
+                                "panel_id": p["panel_id"],
+                                "text": p["text"].strip(),
+                                "panel_graphic_url": p["panel_graphic_url"],
+                                "entities": [
+                                    {
+                                        "annotation_id": t["tag_id"],
+                                        "source": t["source"],
+                                        "category": t["category"],
+                                        "entity_type": t["entity_type"],
+                                        "role": t["role"],
+                                        "text": t["text"],
+                                        "ext_ids": t["ext_ids"],
+                                        "norm_text": t["norm_text"],
+                                        "ext_dbs": t["ext_dbs"],
+                                        "in_caption": bool(t["in_caption"]),
+                                        "ext_names": t["ext_names"],
+                                        "ext_tax_ids": t["ext_tax_ids"],
+                                        "ext_tax_names": t["ext_tax_names"],
+                                        "ext_urls": t["ext_urls"],
+                                        "offsets": t["local_offsets"],
+                                    }
+                                    for t in p["tags"]
+                                ],
+                            }
+                            for e in panel["entities"]:
+                                assert type(e["offsets"]) == list
+                            if len(panel["entities"]) == 0:
+                                no_entities += 1
+                                continue
+                            else:
+                                has_entities += 1
+                            all_panels.append(panel)
+
+                        figure["panels"] = all_panels
+
+                        # Pass on all figures that aren't split into panels
+                        if len(all_panels) == 0:
+                            no_panels += 1
+                            continue
+                        else:
+                            has_panels += 1
+                        all_figures.append(figure)
+
+                    output = {
+                        "doi": data["doi"],
+                        "abstract": data["abstract"],
+                        "figures": all_figures,
+                    }
+                    yield id_, output