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Update SemEval2018_Task7.py

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  1. SemEval2018_Task7.py +2 -27
SemEval2018_Task7.py CHANGED
@@ -1,7 +1,3 @@
1
- # I am trying to understand to the following code. Do not use this for any purpose as I do not support this.
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- # Use the original source from https://huggingface.co/datasets/DFKI-SLT/SemEval2018_Task7/blob/main/SemEval2018_Task7.py
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-
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-
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  # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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  #
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  # Licensed under the Apache License, Version 2.0 (the "License");
@@ -24,7 +20,6 @@ import datasets
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  import xml.dom.minidom
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  import xml.etree.ElementTree as ET
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- # Find for instance the citation on arxiv or on the dataset repo/website
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  _CITATION = """\
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  @inproceedings{gabor-etal-2018-semeval,
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  title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
@@ -52,7 +47,6 @@ _CITATION = """\
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  }
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  """
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- # You can copy an official description
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  _DESCRIPTION = """\
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  This paper describes the first task on semantic relation extraction and classification in scientific paper
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  abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three
@@ -63,10 +57,8 @@ limited to scientific or bio-medical information extraction. The task attracted
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  with 158 submissions across different scenarios.
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  """
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- # Add a link to an official homepage for the dataset here
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  _HOMEPAGE = "https://github.com/gkata/SemEval2018Task7/tree/testing"
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- # Add the licence for the dataset here if you can find it
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  _LICENSE = ""
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  # Add link to the official dataset URLs here
@@ -193,18 +185,10 @@ class Semeval2018Task7(datasets.GeneratorBasedBuilder):
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  )
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  return datasets.DatasetInfo(
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- # This is the description that will appear on the datasets page.
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  description=_DESCRIPTION,
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- # This defines the different columns of the dataset and their types
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- features=features, # Here we define them above because they are different between the two configurations
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- # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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- # specify them. They'll be used if as_supervised=True in builder.as_dataset.
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- # supervised_keys=("sentence", "label"),
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- # Homepage of the dataset for documentation
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  homepage=_HOMEPAGE,
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- # License for the dataset if available
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  license=_LICENSE,
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- # Citation for the dataset
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  citation=_CITATION,
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  )
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@@ -267,15 +251,7 @@ class Semeval2018Task7(datasets.GeneratorBasedBuilder):
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  if arg_id not in text_id_to_relations_map:
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  text_id_to_relations_map[arg_id] = [relation]
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  else:
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- text_id_to_relations_map[arg_id].append(relation)
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- #print("result", text_id_to_relations_map)
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-
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- #for arg_id, values in text_id_to_relations_map.items():
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- #print(f"ID: {arg_id}")
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- # for value in values:
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- # (value)
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-
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-
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  doc2 = ET.parse(text_filepath)
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  root = doc2.getroot()
@@ -284,7 +260,6 @@ class Semeval2018Task7(datasets.GeneratorBasedBuilder):
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  if child.find("title")==None:
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  continue
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  text_id = child.attrib
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- #print("text_id", text_id)
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  if child.find("abstract")==None:
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  continue
 
 
 
 
 
1
  # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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  #
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  # Licensed under the Apache License, Version 2.0 (the "License");
 
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  import xml.dom.minidom
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  import xml.etree.ElementTree as ET
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  _CITATION = """\
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  @inproceedings{gabor-etal-2018-semeval,
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  title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
 
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  }
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  """
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  _DESCRIPTION = """\
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  This paper describes the first task on semantic relation extraction and classification in scientific paper
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  abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three
 
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  with 158 submissions across different scenarios.
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  """
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  _HOMEPAGE = "https://github.com/gkata/SemEval2018Task7/tree/testing"
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  _LICENSE = ""
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  # Add link to the official dataset URLs here
 
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  )
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  return datasets.DatasetInfo(
 
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  description=_DESCRIPTION,
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+ features=features,
 
 
 
 
 
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  homepage=_HOMEPAGE,
 
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  license=_LICENSE,
 
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  citation=_CITATION,
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  )
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  if arg_id not in text_id_to_relations_map:
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  text_id_to_relations_map[arg_id] = [relation]
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  else:
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+ text_id_to_relations_map[arg_id].append(relation)
 
 
 
 
 
 
 
 
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  doc2 = ET.parse(text_filepath)
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  root = doc2.getroot()
 
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  if child.find("title")==None:
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  continue
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  text_id = child.attrib
 
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  if child.find("abstract")==None:
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  continue