Datasets:

Modalities:
Text
Formats:
parquet
Languages:
English
Libraries:
Datasets
pandas
License:
phucdev commited on
Commit
2b23b17
·
1 Parent(s): da8cf68

Update fabner.py

Browse files
Files changed (1) hide show
  1. fabner.py +27 -1
fabner.py CHANGED
@@ -61,6 +61,26 @@ _URLS = {
61
  "test": "https://figshare.com/ndownloader/files/28405851/S1-test.txt",
62
  }
63
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
64
  class FabNER(datasets.GeneratorBasedBuilder):
65
  """FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition."""
66
 
@@ -84,6 +104,8 @@ class FabNER(datasets.GeneratorBasedBuilder):
84
  description="The FabNER dataset with BIO tagging format"),
85
  datasets.BuilderConfig(name="fabner_simple", version=VERSION,
86
  description="The FabNER dataset with no tagging format"),
 
 
87
  ]
88
  DEFAULT_CONFIG_NAME = "fabner"
89
 
@@ -102,7 +124,9 @@ class FabNER(datasets.GeneratorBasedBuilder):
102
  "MANS", # Manufacturing Standards
103
  "BIOP", # BioMedical
104
  ]
105
- if self.config.name == "fabner":
 
 
106
  class_labels = ["O"]
107
  for entity_type in entity_types:
108
  class_labels.extend(
@@ -195,6 +219,8 @@ class FabNER(datasets.GeneratorBasedBuilder):
195
  ner_tag = "O"
196
  else:
197
  ner_tag = ner_tag.replace("S-", "B-").replace("E-", "I-")
 
 
198
  ner_tags.append(ner_tag)
199
  # last example
200
  if tokens:
 
61
  "test": "https://figshare.com/ndownloader/files/28405851/S1-test.txt",
62
  }
63
 
64
+
65
+ def map_fabner_labels(string_tag):
66
+ tag = string_tag[2:]
67
+ # MATERIAL (FABNER)
68
+ if tag == "MATE":
69
+ return "Material"
70
+ # MANUFACTURING PROCESS (FABNER)
71
+ elif tag == "MANP":
72
+ return "Method"
73
+ # MACHINE/EQUIPMENT, MECHANICAL PROPERTIES, CHARACTERIZATION, ENABLING TECHNOLOGY (FABNER)
74
+ elif tag in ["MACEQ", "PRO", "CHAR", "ENAT"]:
75
+ return "Technological System"
76
+ # APPLICATION (FABNER)
77
+ elif tag == "APPL":
78
+ return "Technical Field"
79
+ # FEATURES, PARAMETERS, CONCEPT/PRINCIPLES, MANUFACTURING STANDARDS, BIOMEDICAL, O (FABNER)
80
+ else:
81
+ return "O"
82
+
83
+
84
  class FabNER(datasets.GeneratorBasedBuilder):
85
  """FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition."""
86
 
 
104
  description="The FabNER dataset with BIO tagging format"),
105
  datasets.BuilderConfig(name="fabner_simple", version=VERSION,
106
  description="The FabNER dataset with no tagging format"),
107
+ datasets.BuilderConfig(name="text2tech", version=VERSION,
108
+ description="The FabNER dataset mapped to the Text2Tech tag set"),
109
  ]
110
  DEFAULT_CONFIG_NAME = "fabner"
111
 
 
124
  "MANS", # Manufacturing Standards
125
  "BIOP", # BioMedical
126
  ]
127
+ if self.config.name == "text2tech":
128
+ class_labels = ["O", "Technological System", "Method", "Material", "Technical Field"]
129
+ elif self.config.name == "fabner":
130
  class_labels = ["O"]
131
  for entity_type in entity_types:
132
  class_labels.extend(
 
219
  ner_tag = "O"
220
  else:
221
  ner_tag = ner_tag.replace("S-", "B-").replace("E-", "I-")
222
+ elif self.config.name == "text2tech":
223
+ ner_tag = map_fabner_labels(ner_tag)
224
  ner_tags.append(ner_tag)
225
  # last example
226
  if tokens: