Thomas Lemberger commited on
Commit
dd092a0
1 Parent(s): 16031ca

updating data files and loader script

Browse files
Files changed (3) hide show
  1. sd-nlp.py +102 -68
  2. sd_figs.zip +2 -2
  3. sd_panels.zip +2 -2
sd-nlp.py CHANGED
@@ -1,5 +1,5 @@
1
  # coding=utf-8
2
- # Copyright 2020 The HuggingFace Datasets Authors and Thomas Lemberger.
3
  #
4
  # Licensed under the Apache License, Version 2.0 (the "License");
5
  # you may not use this file except in compliance with the License.
@@ -12,69 +12,74 @@
12
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
  # See the License for the specific language governing permissions and
14
  # limitations under the License.
15
- """SourceDataNLP dataset."""
 
 
 
 
16
 
17
  from __future__ import absolute_import, division, print_function
18
 
19
  import json
20
- from pathlib import Path
21
-
22
  import datasets
23
 
24
 
25
- _NER_LABEL_NAMES = [
26
- "O",
27
- "I-SMALL_MOLECULE",
28
- "B-SMALL_MOLECULE",
29
- "I-GENEPROD",
30
- "B-GENEPROD",
31
- "I-SUBCELLULAR",
32
- "B-SUBCELLULAR",
33
- "I-CELL",
34
- "B-CELL",
35
- "I-TISSUE",
36
- "B-TISSUE",
37
- "I-ORGANISM",
38
- "B-ORGANISM",
39
- "I-EXP_ASSAY",
40
- "B-EXP_ASSAY",
41
- ]
42
- _SEMANTIC_ROLES_LABEL_NAMES = ["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]
43
- _BORING_LABEL_NAMES = ["O", "I-BORING", "B-BORING"]
44
- _PANEL_START_NAMES = ["O", "B-PANEL_START"]
45
-
46
- _CITATION = """\
47
- @Unpublished{
48
- huggingface: dataset,
49
- title = {SourceData NLP},
50
- authors={Thomas Lemberger, EMBO},
51
- year={2021}
52
- }
53
- """
54
-
55
- _DESCRIPTION = """\
56
- This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain.
57
- """
58
-
59
- _HOMEPAGE = "https://huggingface.co/datasets/EMBO/sd-nlp"
60
-
61
- _LICENSE = "CC-BY 4.0"
62
-
63
- _URLS = {
64
- "NER": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
65
- "ROLES": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
66
- "BORING": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
67
- "PANELIZATION": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panelization.zip",
68
- }
69
-
70
  class SourceDataNLP(datasets.GeneratorBasedBuilder):
71
  """SourceDataNLP provides datasets to train NLP tasks in cell and molecular biology."""
72
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
73
  VERSION = datasets.Version("0.0.1")
74
 
75
  BUILDER_CONFIGS = [
76
  datasets.BuilderConfig(name="NER", version="0.0.1", description="Dataset for entity recognition"),
77
- datasets.BuilderConfig(name="ROLES", version="0.0.1", description="Dataset for semantic roles."),
 
78
  datasets.BuilderConfig(name="BORING", version="0.0.1", description="Dataset for semantic roles."),
79
  datasets.BuilderConfig(
80
  name="PANELIZATION",
@@ -91,18 +96,30 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
91
  {
92
  "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
93
  "labels": datasets.Sequence(
94
- feature=datasets.ClassLabel(num_classes=len(_NER_LABEL_NAMES), names=_NER_LABEL_NAMES)
95
  ),
96
  "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
97
  }
98
  )
99
- elif self.config.name == "ROLES":
100
  features = datasets.Features(
101
  {
102
  "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
103
  "labels": datasets.Sequence(
104
  feature=datasets.ClassLabel(
105
- num_classes=len(_SEMANTIC_ROLES_LABEL_NAMES), names=_SEMANTIC_ROLES_LABEL_NAMES
 
 
 
 
 
 
 
 
 
 
 
 
106
  )
107
  ),
108
  "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
@@ -113,7 +130,7 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
113
  {
114
  "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
115
  "labels": datasets.Sequence(
116
- feature=datasets.ClassLabel(num_classes=len(_BORING_LABEL_NAMES), names=_BORING_LABEL_NAMES)
117
  ),
118
  }
119
  )
@@ -122,18 +139,19 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
122
  {
123
  "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
124
  "labels": datasets.Sequence(
125
- feature=datasets.ClassLabel(num_classes=len(_PANEL_START_NAMES), names=_PANEL_START_NAMES)
126
  ),
 
127
  }
128
  )
129
 
130
  return datasets.DatasetInfo(
131
- description=_DESCRIPTION,
132
  features=features,
133
  supervised_keys=("input_ids", "labels"),
134
- homepage=_HOMEPAGE,
135
- license=_LICENSE,
136
- citation=_CITATION,
137
  )
138
 
139
  def _split_generators(self, dl_manager: datasets.DownloadManager):
@@ -142,12 +160,12 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
142
  if self.config.data_dir:
143
  data_dir = self.config.data_dir
144
  else:
145
- url = _URLS[self.config.name]
146
  data_dir = dl_manager.download_and_extract(url)
147
- if self.config.name in ["NER", "ROLES", "BORING"]:
148
  data_dir += "/sd_panels"
149
  elif self.config.name == "PANELIZATION":
150
- data_dir += "/sd_panelization"
151
  else:
152
  raise ValueError(f"unkonwn config name: {self.config.name}")
153
  return [
@@ -183,22 +201,38 @@ class SourceDataNLP(datasets.GeneratorBasedBuilder):
183
  for id_, row in enumerate(f):
184
  data = json.loads(row)
185
  if self.config.name == "NER":
186
- labels_type = data["label_ids"]["entity_types"]
187
- tag_mask = [0 if tag == "O" else 1 for tag in labels_type]
188
- yield id_, {"input_ids": data["input_ids"], "labels": labels_type, "tag_mask": tag_mask}
189
- elif self.config.name == "ROLES":
190
- labels_type = data["label_ids"]["entity_types"]
 
 
 
 
191
  geneprod = ["B-GENEPROD", "I-GENEPROD", "B-PROTEIN", "I-PROTEIN", "B-GENE", "I-GENE"]
192
- tag_mask = [1 if t in geneprod else 0 for t in labels_type]
193
  yield id_, {
194
  "input_ids": data["input_ids"],
195
  "labels": data["label_ids"]["geneprod_roles"],
196
  "tag_mask": tag_mask,
197
  }
 
 
 
 
 
 
 
 
 
198
  elif self.config.name == "BORING":
199
  yield id_, {"input_ids": data["input_ids"], "labels": data["label_ids"]["boring"]}
200
  elif self.config.name == "PANELIZATION":
 
 
201
  yield id_, {
202
  "input_ids": data["input_ids"],
203
  "labels": data["label_ids"]["panel_start"],
 
204
  }
 
1
  # coding=utf-8
2
+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
3
  #
4
  # Licensed under the Apache License, Version 2.0 (the "License");
5
  # you may not use this file except in compliance with the License.
 
12
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
  # See the License for the specific language governing permissions and
14
  # limitations under the License.
15
+
16
+
17
+ # template from : https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py
18
+
19
+ """Loading script for the biolang dataset for language modeling in biology."""
20
 
21
  from __future__ import absolute_import, division, print_function
22
 
23
  import json
24
+ import pdb
 
25
  import datasets
26
 
27
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
28
  class SourceDataNLP(datasets.GeneratorBasedBuilder):
29
  """SourceDataNLP provides datasets to train NLP tasks in cell and molecular biology."""
30
 
31
+ _NER_LABEL_NAMES = [
32
+ "O",
33
+ "I-SMALL_MOLECULE",
34
+ "B-SMALL_MOLECULE",
35
+ "I-GENEPROD",
36
+ "B-GENEPROD",
37
+ "I-SUBCELLULAR",
38
+ "B-SUBCELLULAR",
39
+ "I-CELL",
40
+ "B-CELL",
41
+ "I-TISSUE",
42
+ "B-TISSUE",
43
+ "I-ORGANISM",
44
+ "B-ORGANISM",
45
+ "I-EXP_ASSAY",
46
+ "B-EXP_ASSAY",
47
+ ]
48
+ _SEMANTIC_GENEPROD_ROLES_LABEL_NAMES = ["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]
49
+ _SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES = ["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]
50
+ _BORING_LABEL_NAMES = ["O", "I-BORING", "B-BORING"]
51
+ _PANEL_START_NAMES = ["O", "B-PANEL_START"]
52
+
53
+ _CITATION = """\
54
+ @Unpublished{
55
+ huggingface: dataset,
56
+ title = {SourceData NLP},
57
+ authors={Thomas Lemberger, EMBO},
58
+ year={2021}
59
+ }
60
+ """
61
+
62
+ _DESCRIPTION = """\
63
+ This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain.
64
+ """
65
+
66
+ _HOMEPAGE = "https://huggingface.co/datasets/EMBO/sd-nlp"
67
+
68
+ _LICENSE = "CC-BY 4.0"
69
+
70
+ _URLS = {
71
+ "NER": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
72
+ "ROLES": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
73
+ "BORING": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
74
+ "PANELIZATION": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_figs.zip",
75
+ }
76
+
77
  VERSION = datasets.Version("0.0.1")
78
 
79
  BUILDER_CONFIGS = [
80
  datasets.BuilderConfig(name="NER", version="0.0.1", description="Dataset for entity recognition"),
81
+ datasets.BuilderConfig(name="GENEPROD_ROLES", version="0.0.1", description="Dataset for semantic roles."),
82
+ datasets.BuilderConfig(name="SMALL_MOL_ROLES", version="0.0.1", description="Dataset for semantic roles."),
83
  datasets.BuilderConfig(name="BORING", version="0.0.1", description="Dataset for semantic roles."),
84
  datasets.BuilderConfig(
85
  name="PANELIZATION",
 
96
  {
97
  "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
98
  "labels": datasets.Sequence(
99
+ feature=datasets.ClassLabel(num_classes=len(self._NER_LABEL_NAMES), names=self._NER_LABEL_NAMES)
100
  ),
101
  "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
102
  }
103
  )
104
+ elif self.config.name == "GENEPROD_ROLES":
105
  features = datasets.Features(
106
  {
107
  "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
108
  "labels": datasets.Sequence(
109
  feature=datasets.ClassLabel(
110
+ num_classes=len(self._SEMANTIC_GENEPROD_ROLES_LABEL_NAMES), names=self._SEMANTIC_GENEPROD_ROLES_LABEL_NAMES
111
+ )
112
+ ),
113
+ "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
114
+ }
115
+ )
116
+ elif self.config.name == "SMALL_MOL_ROLES":
117
+ features = datasets.Features(
118
+ {
119
+ "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
120
+ "labels": datasets.Sequence(
121
+ feature=datasets.ClassLabel(
122
+ num_classes=len(self._SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES), names=self._SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES
123
  )
124
  ),
125
  "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
 
130
  {
131
  "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
132
  "labels": datasets.Sequence(
133
+ feature=datasets.ClassLabel(num_classes=len(self._BORING_LABEL_NAMES), names=self._BORING_LABEL_NAMES)
134
  ),
135
  }
136
  )
 
139
  {
140
  "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
141
  "labels": datasets.Sequence(
142
+ feature=datasets.ClassLabel(num_classes=len(self._PANEL_START_NAMES), names=self._PANEL_START_NAMES)
143
  ),
144
+ "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
145
  }
146
  )
147
 
148
  return datasets.DatasetInfo(
149
+ description=self._DESCRIPTION,
150
  features=features,
151
  supervised_keys=("input_ids", "labels"),
152
+ homepage=self._HOMEPAGE,
153
+ license=self._LICENSE,
154
+ citation=self._CITATION,
155
  )
156
 
157
  def _split_generators(self, dl_manager: datasets.DownloadManager):
 
160
  if self.config.data_dir:
161
  data_dir = self.config.data_dir
162
  else:
163
+ url = self._URLS[self.config.name]
164
  data_dir = dl_manager.download_and_extract(url)
165
+ if self.config.name in ["NER", "GENEPROD_ROLES", "SMALL_MOL_ROLES", "BORING"]:
166
  data_dir += "/sd_panels"
167
  elif self.config.name == "PANELIZATION":
168
+ data_dir += "/sd_figs"
169
  else:
170
  raise ValueError(f"unkonwn config name: {self.config.name}")
171
  return [
 
201
  for id_, row in enumerate(f):
202
  data = json.loads(row)
203
  if self.config.name == "NER":
204
+ labels = data["label_ids"]["entity_types"]
205
+ tag_mask = [0 if tag == "O" else 1 for tag in labels]
206
+ yield id_, {
207
+ "input_ids": data["input_ids"],
208
+ "labels": labels,
209
+ "tag_mask": tag_mask,
210
+ }
211
+ elif self.config.name == "GENEPROD_ROLES":
212
+ labels = data["label_ids"]["entity_types"]
213
  geneprod = ["B-GENEPROD", "I-GENEPROD", "B-PROTEIN", "I-PROTEIN", "B-GENE", "I-GENE"]
214
+ tag_mask = [1 if t in geneprod else 0 for t in labels]
215
  yield id_, {
216
  "input_ids": data["input_ids"],
217
  "labels": data["label_ids"]["geneprod_roles"],
218
  "tag_mask": tag_mask,
219
  }
220
+ elif self.config.name == "SMALL_MOL_ROLES":
221
+ labels = data["label_ids"]["entity_types"]
222
+ small_mol = ["B-SMALL_MOLECULE", "I-SMALL_MOLECULE"]
223
+ tag_mask = [1 if t in small_mol else 0 for t in labels]
224
+ yield id_, {
225
+ "input_ids": data["input_ids"],
226
+ "labels": data["label_ids"]["small_mol_roles"],
227
+ "tag_mask": tag_mask,
228
+ }
229
  elif self.config.name == "BORING":
230
  yield id_, {"input_ids": data["input_ids"], "labels": data["label_ids"]["boring"]}
231
  elif self.config.name == "PANELIZATION":
232
+ labels = data["label_ids"]["panel_start"]
233
+ tag_mask = [1 if t == "B-PANEL_START" else 0 for t in labels]
234
  yield id_, {
235
  "input_ids": data["input_ids"],
236
  "labels": data["label_ids"]["panel_start"],
237
+ "tag_mask": tag_mask,
238
  }
sd_figs.zip CHANGED
@@ -1,3 +1,3 @@
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3
- size 16286918
 
1
  version https://git-lfs.github.com/spec/v1
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+ oid sha256:fbf923d27a98176e57949103906f5bef4fd829bfc9526ecd0e1d1bc24f1cea72
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sd_panels.zip CHANGED
@@ -1,3 +1,3 @@
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