entity_linking_data_loader

.gitattributes

@@ -139,27 +139,3 @@ token_classification/roles_multi/validation.jsonl filter=lfs diff=lfs merge=lfs
 token_classification/roles_small_mol/test.jsonl filter=lfs diff=lfs merge=lfs -text
 token_classification/roles_small_mol/train.jsonl filter=lfs diff=lfs merge=lfs -text
 token_classification/roles_small_mol/validation.jsonl filter=lfs diff=lfs merge=lfs -text
-image_segmentation/train/labels.cache filter=lfs diff=lfs merge=lfs -text
-image_segmentation/clip_panel_image_captions/ filter=lfs diff=lfs merge=lfs -text
-. filter=lfs diff=lfs merge=lfs -text
-information_extraction/ filter=lfs diff=lfs merge=lfs -text
-information_extraction/experimental_assay.json filter=lfs diff=lfs merge=lfs -text
-information_extraction/hypothesis_tested.json filter=lfs diff=lfs merge=lfs -text
-information_extraction/is_experiment.json filter=lfs diff=lfs merge=lfs -text
-information_extraction/ncbi_gene_linking.csv filter=lfs diff=lfs merge=lfs -text
-information_extraction/where_was_tested.csv filter=lfs diff=lfs merge=lfs -text
-information_extraction/where_was_tested.json filter=lfs diff=lfs merge=lfs -text
-information_extraction/controlled_entities.json filter=lfs diff=lfs merge=lfs -text
-information_extraction/diseases.json filter=lfs diff=lfs merge=lfs -text
-information_extraction/chemicals.csv filter=lfs diff=lfs merge=lfs -text
-information_extraction/is_experiment.csv filter=lfs diff=lfs merge=lfs -text
-information_extraction/diseases.csv filter=lfs diff=lfs merge=lfs -text
-information_extraction/experimental_assay.csv filter=lfs diff=lfs merge=lfs -text
-information_extraction/assayed_entities.csv filter=lfs diff=lfs merge=lfs -text
-information_extraction/controlled_entities.csv filter=lfs diff=lfs merge=lfs -text
-information_extraction/hypothesis_tested.csv filter=lfs diff=lfs merge=lfs -text
-information_extraction/ncbi_gene_linking.json filter=lfs diff=lfs merge=lfs -text
-information_extraction/assayed_entities.json filter=lfs diff=lfs merge=lfs -text
-information_extraction/chemicals.json filter=lfs diff=lfs merge=lfs -text
-compound_image_segmentation/segmented_images.zip filter=lfs diff=lfs merge=lfs -text
-compound_image_segmentation/soda_panelization_figures.zip filter=lfs diff=lfs merge=lfs -text

README.md

@@ -172,39 +172,6 @@ The text in the dataset is English.
   })
 ```
 
-### Information Extraction
-
-This folder contains `caption`-`answer` pairs intended to be used for information extraction. Each of the files contains answers to given questions about the captions.
-Each file is provided in `csv` and `json` format for convinience for different cases.
-The list of files and questions they answer are shown below:
-
-* `assayed_entities`: What is the assayed/measured entity?
-
-* `chemicals`: Are there any chemical compounds or small molecules mentioned?
-
-* `diseases`: Is there any disease term mentioned, or can be infered, in the figure legend?
-
-* `experimental_assay`: What kind of experimental assay was used for this experiment?
-
-* `hypothesis_tested`: Can you formulate the hypothesis that this experiment has tested.
-
-* `is_experiment`: Does the legend describe an experiment or not?
-
-* `ncbi_gene_linking`: Can you link the identified genes to their NCBI gene identifiers?
-
-* `where_was_tested`: In what kind of cell/tissue/organism/subcellular component was the experiment performed?
-
-We refer the interested reader to the [cypher queries](https://github.com/source-data/soda-data/blob/master/src/soda_data/sdneo/info_extraction_queries.py) used to generate this data for further information.
-
-
-### Compound Image Segmentation
-
-This folder contain the data for the compound image segmentation task. The data is provided in format compatible to train `YOLOv10` models.
-
-The file `soda_panelization_figures.zip` contains 13039 figures extracted from scientific manuscripts, that are labeled to use object detection algorithms to separate the figure into its panels. The dataset is divided into train, validation and test sets. 
-
-The file ``segmented_images.zip`` contains `panel`-`caption` pairs. These have been used, together with multimodal LLMs to assign the correct panel label and caption to each panel in the figure. 
-
 ## Dataset Creation
 
 ### Curation Rationale

SourceData.py

@@ -19,12 +19,10 @@
 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."""
 
@@ -47,26 +45,19 @@ class SourceData(datasets.GeneratorBasedBuilder):
         "B-DISEASE",
         "I-DISEASE",
         "B-CELL_LINE",
-        "I-CELL_LINE",
-    ]
-    _SEMANTIC_ROLES = [
-        "O",
-        "B-CONTROLLED_VAR",
-        "I-CONTROLLED_VAR",
-        "B-MEASURED_VAR",
-        "I-MEASURED_VAR",
+        "I-CELL_LINE"
     ]
+    _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 = """\
-@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}
-}
+    @Unpublished{
+        huggingface: dataset,
+        title = {SourceData NLP},
+        authors={Thomas Lemberger & Jorge Abreu-Vicente, EMBO},
+        year={2023}
+    }
     """
 
     _DESCRIPTION = """\
@@ -79,73 +70,32 @@ class SourceData(datasets.GeneratorBasedBuilder):
 
     DEFAULT_CONFIG_NAME = "NER"
 
-    _LATEST_VERSION = "2.0.3"  # Should this be updated to 2.0.3
+    _LATEST_VERSION = "1.0.0"
 
     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="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.",
-            # ),
+            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."),
         ]
-
+        
         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")),
@@ -159,7 +109,7 @@ 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")),
@@ -174,7 +124,7 @@ 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")),
@@ -189,12 +139,13 @@ 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")),
@@ -206,57 +157,13 @@ 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,
@@ -265,49 +172,38 @@ 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"
-
-            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)
+            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)
         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]},
             ),
         ]
 
@@ -316,45 +212,40 @@ 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.strip())
+                data = json.loads(row)
                 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"]
@@ -365,66 +256,4 @@ 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
 

compound_image_segmentation/segmented_images.zip

@@ -1,3 +0,0 @@
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-size 14901995938

compound_image_segmentation/soda_panelization_figures.zip

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-size 2169668963

information_extraction/assayed_entities.csv

@@ -1,3 +0,0 @@
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-size 24151715

information_extraction/assayed_entities.json

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-size 26060444

information_extraction/chemicals.csv

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information_extraction/controlled_entities.csv

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-size 20885680

information_extraction/diseases.csv

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information_extraction/experimental_assay.csv

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information_extraction/hypothesis_tested.csv

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information_extraction/hypothesis_tested.json

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information_extraction/is_experiment.csv

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information_extraction/is_experiment.json

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information_extraction/ncbi_gene_linking.csv

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