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Datasets:
sbx
/
superlim-2

Tasks:
Multiple Choice
Text Classification
Question Answering
Modalities:
Text
Sub-tasks:
sentiment-analysis
acceptability-classification
closed-domain-qa
Languages:
Swedish
Size:
100K - 1M
Libraries:
Datasets
Dataset card Data Studio Files
xet
Community
4
Felix commited on
Commit
fa5311d
·
1 Parent(s): 59a5da9

format more compactly

Browse files
Files changed (1) hide show
  1. superlim-2.py +84 -110
superlim-2.py CHANGED
@@ -186,101 +186,81 @@ class SuperLim(datasets.GeneratorBasedBuilder):
186
  def _info(self):
187
  # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
188
  if self.config.name == 'absabank-imm': # This is the name of the configuration selected in BUILDER_CONFIGS above
189
- features = datasets.Features(
190
- {
191
- "id": datasets.Value("string"),
192
- "text": datasets.Value("string"),
193
- "label": datasets.Value(dtype='float32')
194
- }
195
- )
196
  elif self.config.name == 'argumentation_sent':
197
- features = datasets.Features(
198
- {
199
- "sentence_id": datasets.Value("string"),
200
- "topic": datasets.Value("string"),
201
- "label": datasets.Value("string"),
202
- "sentence": datasets.Value("string")
203
- }
204
- )
205
  elif self.config.name == "dalaj-ged":
206
- features = datasets.Features(
207
- {
208
- "sentence": datasets.Value("string"),
209
- "label": datasets.Value("string"),
210
- "meta": datasets.Features(
211
- {
212
- 'error_span': datasets.Value("string"),
213
- 'confusion_pair': datasets.Value("string"),
214
- 'error_label': datasets.Value("string"),
215
- 'education_level': datasets.Value("string"),
216
- 'l1': datasets.Value("string"),
217
- 'data_source': datasets.Value("string")
218
- }
219
- )
220
- }
221
- )
222
  elif self.config.name == "sweana":
223
- features = datasets.Features(
224
- {
225
- "pair1_element1": datasets.Value("string"),
226
- "pair1_element2": datasets.Value("string"),
227
- "pair2_element1": datasets.Value("string"),
228
- "pair2_element2": datasets.Value("string"),
229
- "category": datasets.Value("string"),
230
- }
231
- )
232
  elif self.config.name == 'swediagnostics':
233
- features = datasets.Features(
234
- {
235
- 'lexical_semantics': datasets.Value("string"),
236
- 'predicate_argument_structure': datasets.Value("string"),
237
- 'logic': datasets.Value("string"),
238
- 'knowledge': datasets.Value("string"),
239
- 'domain': datasets.Value("string"),
240
- 'premise': datasets.Value("string"),
241
- 'hypothesis': datasets.Value("string"),
242
- 'label':datasets.Value("string")
243
- }
244
- )
245
  elif self.config.name == 'swedn':
246
- features = datasets.Features(
247
- {
248
- "id": datasets.Value("string"),
249
- "headline": datasets.Value("string"),
250
- "summary": datasets.Value("string"),
251
- "article": datasets.Value("string"),
252
- "article_category": datasets.Value("string")
253
- }
254
- )
255
  elif self.config.name == "swefaq":
256
- features = datasets.Features(
257
- {
258
- "category_id": datasets.Value("string"),
259
- "candidate_answers": datasets.Value("string"),
260
- "question": datasets.Value("string"),
261
- "label": datasets.Value("string"),
262
- "meta": datasets.Value("string"),
263
- }
264
- )
265
  elif self.config.name == 'swemnli':
266
- features = datasets.Features(
267
- {
268
- "id": datasets.Value("string"),
269
- "premise": datasets.Value("string"),
270
- "hypothesis": datasets.Value("string"),
271
- "label": datasets.Value("string")
272
- }
273
- )
274
  elif self.config.name == "swepar":
275
- features = datasets.Features(
276
- {
277
- "genre": datasets.Value("string"),
278
- "file": datasets.Value("string"),
279
- "sentence_1": datasets.Value("string"),
280
- "sentence_2": datasets.Value("string"),
281
- "label": datasets.Value("string"),
282
- }
283
- )
284
  elif self.config.name == "swesat":
285
  features = datasets.Features({
286
  "id": datasets.Value("string"),
@@ -295,31 +275,25 @@ class SuperLim(datasets.GeneratorBasedBuilder):
295
  })
296
  })
297
  elif self.config.name == "swesim_relatedness":
298
- features = datasets.Features(
299
- {
300
- "word_1": datasets.Value("string"),
301
- "word_2": datasets.Value("string"),
302
- "label": datasets.Value("string"),
303
- }
304
- )
305
  elif self.config.name == "swesim_similarity":
306
- features = datasets.Features(
307
- {
308
- "word_1": datasets.Value("string"),
309
- "word_2": datasets.Value("string"),
310
- "label": datasets.Value("string"),
311
- }
312
- )
313
  elif self.config.name == "swewic":
314
- features = datasets.Features(
315
- {
316
- "idx": datasets.Value("string"),
317
- "first": datasets.Value("string"),
318
- "second": datasets.Value("string"),
319
- "label": datasets.Value("string"),
320
- "meta": datasets.Value("string"),
321
- }
322
- )
323
  else:
324
  raise ValueError(f"Subset {self.config.name} does not exist.")
325
  return datasets.DatasetInfo(
 
186
  def _info(self):
187
  # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
188
  if self.config.name == 'absabank-imm': # This is the name of the configuration selected in BUILDER_CONFIGS above
189
+ features = datasets.Features({
190
+ "id": datasets.Value("string"),
191
+ "text": datasets.Value("string"),
192
+ "label": datasets.Value(dtype='float32')
193
+ })
 
 
194
  elif self.config.name == 'argumentation_sent':
195
+ features = datasets.Features({
196
+ "sentence_id": datasets.Value("string"),
197
+ "topic": datasets.Value("string"),
198
+ "label": datasets.Value("string"),
199
+ "sentence": datasets.Value("string")
200
+ })
 
 
201
  elif self.config.name == "dalaj-ged":
202
+ features = datasets.Features({
203
+ "sentence": datasets.Value("string"),
204
+ "label": datasets.Value("string"),
205
+ "meta": datasets.Features({
206
+ 'error_span': datasets.Value("string"),
207
+ 'confusion_pair': datasets.Value("string"),
208
+ 'error_label': datasets.Value("string"),
209
+ 'education_level': datasets.Value("string"),
210
+ 'l1': datasets.Value("string"),
211
+ 'data_source': datasets.Value("string")
212
+ })
213
+ })
 
 
 
 
214
  elif self.config.name == "sweana":
215
+ features = datasets.Features({
216
+ "pair1_element1": datasets.Value("string"),
217
+ "pair1_element2": datasets.Value("string"),
218
+ "pair2_element1": datasets.Value("string"),
219
+ "pair2_element2": datasets.Value("string"),
220
+ "category": datasets.Value("string"),
221
+ })
 
 
222
  elif self.config.name == 'swediagnostics':
223
+ features = datasets.Features({
224
+ 'lexical_semantics': datasets.Value("string"),
225
+ 'predicate_argument_structure': datasets.Value("string"),
226
+ 'logic': datasets.Value("string"),
227
+ 'knowledge': datasets.Value("string"),
228
+ 'domain': datasets.Value("string"),
229
+ 'premise': datasets.Value("string"),
230
+ 'hypothesis': datasets.Value("string"),
231
+ 'label':datasets.Value("string")
232
+ })
 
 
233
  elif self.config.name == 'swedn':
234
+ features = datasets.Features({
235
+ "id": datasets.Value("string"),
236
+ "headline": datasets.Value("string"),
237
+ "summary": datasets.Value("string"),
238
+ "article": datasets.Value("string"),
239
+ "article_category": datasets.Value("string")
240
+ })
 
 
241
  elif self.config.name == "swefaq":
242
+ features = datasets.Features({
243
+ "category_id": datasets.Value("string"),
244
+ "candidate_answers": datasets.Value("string"),
245
+ "question": datasets.Value("string"),
246
+ "label": datasets.Value("string"),
247
+ "meta": datasets.Value("string"),
248
+ })
 
 
249
  elif self.config.name == 'swemnli':
250
+ features = datasets.Features({
251
+ "id": datasets.Value("string"),
252
+ "premise": datasets.Value("string"),
253
+ "hypothesis": datasets.Value("string"),
254
+ "label": datasets.Value("string")
255
+ })
 
 
256
  elif self.config.name == "swepar":
257
+ features = datasets.Features({
258
+ "genre": datasets.Value("string"),
259
+ "file": datasets.Value("string"),
260
+ "sentence_1": datasets.Value("string"),
261
+ "sentence_2": datasets.Value("string"),
262
+ "label": datasets.Value("string"),
263
+ })
 
 
264
  elif self.config.name == "swesat":
265
  features = datasets.Features({
266
  "id": datasets.Value("string"),
 
275
  })
276
  })
277
  elif self.config.name == "swesim_relatedness":
278
+ features = datasets.Features({
279
+ "word_1": datasets.Value("string"),
280
+ "word_2": datasets.Value("string"),
281
+ "label": datasets.Value("string"),
282
+ })
 
 
283
  elif self.config.name == "swesim_similarity":
284
+ features = datasets.Features({
285
+ "word_1": datasets.Value("string"),
286
+ "word_2": datasets.Value("string"),
287
+ "label": datasets.Value("string"),
288
+ })
 
 
289
  elif self.config.name == "swewic":
290
+ features = datasets.Features({
291
+ "idx": datasets.Value("string"),
292
+ "first": datasets.Value("string"),
293
+ "second": datasets.Value("string"),
294
+ "label": datasets.Value("string"),
295
+ "meta": datasets.Value("string"),
296
+ })
 
 
297
  else:
298
  raise ValueError(f"Subset {self.config.name} does not exist.")
299
  return datasets.DatasetInfo(