Add dataloader for full SourceData (including entity links)

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- @Unpublished{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