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lewtun HF staff commited on
Commit
5ffc72f
1 Parent(s): d465d0f

Remove Medical Subdomain of Clinical Notes

Browse files
data/medical_subdomain_of_clinical_notes/task.json DELETED
@@ -1 +0,0 @@
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- {"name": "medical_subdomain_of_clinical_notes", "description": "", "data_columns": ["Note", "ID"], "label_columns": {"Label": ["cardiology", "gastroenterology", "nephrology", "neurology", "psychiatry", "pulmonary disease"]}}
 
 
data/medical_subdomain_of_clinical_notes/test_unlabeled.csv DELETED
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data/medical_subdomain_of_clinical_notes/train.csv DELETED
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raft.py CHANGED
@@ -21,7 +21,6 @@ from pathlib import Path
21
  import datasets
22
 
23
  # TODO: Add BibTeX citation
24
- # Find for instance the citation on arxiv or on the dataset repo/website
25
  _CITATION = """\
26
  @InProceedings{huggingface:dataset,
27
  title = {A great new dataset},
@@ -31,44 +30,32 @@ year={2020}
31
  }
32
  """
33
 
34
- # You can copy an official description
35
- _DESCRIPTION = """
 
 
 
 
 
36
  """
37
 
38
- # TODO: Add a link to an official homepage for the dataset here
39
- _HOMEPAGE = ""
40
 
41
  # TODO: Add the licence for the dataset here if you can find it
42
  _LICENSE = ""
43
 
44
- # TODO: Add link to the official dataset URLs here
45
- # The HuggingFace dataset library don't host the datasets but only point to the original files
46
- # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
47
- # This gets all folders within the directory named `data`
48
- DATA_DIR_URL = "data/" # "https://huggingface.co/datasets/ought/raft/resolve/main/data/"
49
- # print([p for p in DATA_DIR_PATH.iterdir() if p.is_dir()])
50
  TASKS = {
51
  "ade_corpus_v2": {
52
  "name": "ade_corpus_v2",
53
  "description": "",
54
- "data_columns": [
55
- "Sentence",
56
- "ID"
57
- ],
58
- "label_columns": {
59
- "Label": [
60
- "ADE-related",
61
- "not ADE-related"
62
- ]
63
- }
64
  },
65
  "banking_77": {
66
  "name": "banking_77",
67
  "description": "",
68
- "data_columns": [
69
- "Query",
70
- "ID"
71
- ],
72
  "label_columns": {
73
  "Label": [
74
  "Refund_not_showing_up",
@@ -147,23 +134,15 @@ TASKS = {
147
  "visa_or_mastercard",
148
  "why_verify_identity",
149
  "wrong_amount_of_cash_received",
150
- "wrong_exchange_rate_for_cash_withdrawal"
151
  ]
152
- }
153
  },
154
  "terms_of_service": {
155
  "name": "terms_of_service",
156
  "description": "",
157
- "data_columns": [
158
- "Sentence",
159
- "ID"
160
- ],
161
- "label_columns": {
162
- "Label": [
163
- "not potentially unfair",
164
- "potentially unfair"
165
- ]
166
- }
167
  },
168
  "tai_safety_research": {
169
  "name": "tai_safety_research",
@@ -176,138 +155,51 @@ TASKS = {
176
  "Item Type",
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  "Author",
178
  "Publication Title",
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- "ID"
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  ],
181
- "label_columns": {
182
- "Label": [
183
- "TAI safety research",
184
- "not TAI safety research"
185
- ]
186
- }
187
  },
188
  "neurips_impact_statement_risks": {
189
  "name": "neurips_impact_statement_risks",
190
  "description": "",
191
- "data_columns": [
192
- "Paper title",
193
- "Paper link",
194
- "Impact statement",
195
- "ID"
196
- ],
197
- "label_columns": {
198
- "Label": [
199
- "doesn't mention a harmful application",
200
- "mentions a harmful application"
201
- ]
202
- }
203
- },
204
- "medical_subdomain_of_clinical_notes": {
205
- "name": "medical_subdomain_of_clinical_notes",
206
- "description": "",
207
- "data_columns": [
208
- "Note",
209
- "ID"
210
- ],
211
- "label_columns": {
212
- "Label": [
213
- "cardiology",
214
- "gastroenterology",
215
- "nephrology",
216
- "neurology",
217
- "psychiatry",
218
- "pulmonary disease"
219
- ]
220
- }
221
  },
222
  "overruling": {
223
  "name": "overruling",
224
  "description": "",
225
- "data_columns": [
226
- "Sentence",
227
- "ID"
228
- ],
229
- "label_columns": {
230
- "Label": [
231
- "not overruling",
232
- "overruling"
233
- ]
234
- }
235
  },
236
  "systematic_review_inclusion": {
237
  "name": "systematic_review_inclusion",
238
  "description": "",
239
- "data_columns": [
240
- "Title",
241
- "Abstract",
242
- "Authors",
243
- "Journal",
244
- "ID"
245
- ],
246
- "label_columns": {
247
- "Label": [
248
- "included",
249
- "not included"
250
- ]
251
- }
252
  },
253
  "one_stop_english": {
254
  "name": "one_stop_english",
255
  "description": "",
256
- "data_columns": [
257
- "Article",
258
- "ID"
259
- ],
260
- "label_columns": {
261
- "Label": [
262
- "advanced",
263
- "elementary",
264
- "intermediate"
265
- ]
266
- }
267
  },
268
  "tweet_eval_hate": {
269
  "name": "tweet_eval_hate",
270
  "description": "",
271
- "data_columns": [
272
- "Tweet",
273
- "ID"
274
- ],
275
- "label_columns": {
276
- "Label": [
277
- "hate speech",
278
- "not hate speech"
279
- ]
280
- }
281
  },
282
  "twitter_complaints": {
283
  "name": "twitter_complaints",
284
  "description": "",
285
- "data_columns": [
286
- "Tweet text",
287
- "ID"
288
- ],
289
- "label_columns": {
290
- "Label": [
291
- "complaint",
292
- "no complaint"
293
- ]
294
- }
295
  },
296
  "semiconductor_org_types": {
297
  "name": "semiconductor_org_types",
298
  "description": "",
299
- "data_columns": [
300
- "Paper title",
301
- "Organization name",
302
- "ID"
303
- ],
304
- "label_columns": {
305
- "Label": [
306
- "company",
307
- "research institute",
308
- "university"
309
- ]
310
- }
311
  },
312
  }
313
 
 
21
  import datasets
22
 
23
  # TODO: Add BibTeX citation
 
24
  _CITATION = """\
25
  @InProceedings{huggingface:dataset,
26
  title = {A great new dataset},
 
30
  }
31
  """
32
 
33
+ _DESCRIPTION = """Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants?
34
+
35
+ [RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:
36
+
37
+ - across multiple domains (lit review, tweets, customer interaction, etc.)
38
+ - on economically valuable classification tasks (someone inherently cares about the task)
39
+ - in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)
40
  """
41
 
42
+ _HOMEPAGE = "https://raft.elicit.org"
 
43
 
44
  # TODO: Add the licence for the dataset here if you can find it
45
  _LICENSE = ""
46
 
47
+ DATA_DIR_URL = "data/"
 
 
 
 
 
48
  TASKS = {
49
  "ade_corpus_v2": {
50
  "name": "ade_corpus_v2",
51
  "description": "",
52
+ "data_columns": ["Sentence", "ID"],
53
+ "label_columns": {"Label": ["ADE-related", "not ADE-related"]},
 
 
 
 
 
 
 
 
54
  },
55
  "banking_77": {
56
  "name": "banking_77",
57
  "description": "",
58
+ "data_columns": ["Query", "ID"],
 
 
 
59
  "label_columns": {
60
  "Label": [
61
  "Refund_not_showing_up",
 
134
  "visa_or_mastercard",
135
  "why_verify_identity",
136
  "wrong_amount_of_cash_received",
137
+ "wrong_exchange_rate_for_cash_withdrawal",
138
  ]
139
+ },
140
  },
141
  "terms_of_service": {
142
  "name": "terms_of_service",
143
  "description": "",
144
+ "data_columns": ["Sentence", "ID"],
145
+ "label_columns": {"Label": ["not potentially unfair", "potentially unfair"]},
 
 
 
 
 
 
 
 
146
  },
147
  "tai_safety_research": {
148
  "name": "tai_safety_research",
 
155
  "Item Type",
156
  "Author",
157
  "Publication Title",
158
+ "ID",
159
  ],
160
+ "label_columns": {"Label": ["TAI safety research", "not TAI safety research"]},
 
 
 
 
 
161
  },
162
  "neurips_impact_statement_risks": {
163
  "name": "neurips_impact_statement_risks",
164
  "description": "",
165
+ "data_columns": ["Paper title", "Paper link", "Impact statement", "ID"],
166
+ "label_columns": {"Label": ["doesn't mention a harmful application", "mentions a harmful application"]},
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
167
  },
168
  "overruling": {
169
  "name": "overruling",
170
  "description": "",
171
+ "data_columns": ["Sentence", "ID"],
172
+ "label_columns": {"Label": ["not overruling", "overruling"]},
 
 
 
 
 
 
 
 
173
  },
174
  "systematic_review_inclusion": {
175
  "name": "systematic_review_inclusion",
176
  "description": "",
177
+ "data_columns": ["Title", "Abstract", "Authors", "Journal", "ID"],
178
+ "label_columns": {"Label": ["included", "not included"]},
 
 
 
 
 
 
 
 
 
 
 
179
  },
180
  "one_stop_english": {
181
  "name": "one_stop_english",
182
  "description": "",
183
+ "data_columns": ["Article", "ID"],
184
+ "label_columns": {"Label": ["advanced", "elementary", "intermediate"]},
 
 
 
 
 
 
 
 
 
185
  },
186
  "tweet_eval_hate": {
187
  "name": "tweet_eval_hate",
188
  "description": "",
189
+ "data_columns": ["Tweet", "ID"],
190
+ "label_columns": {"Label": ["hate speech", "not hate speech"]},
 
 
 
 
 
 
 
 
191
  },
192
  "twitter_complaints": {
193
  "name": "twitter_complaints",
194
  "description": "",
195
+ "data_columns": ["Tweet text", "ID"],
196
+ "label_columns": {"Label": ["complaint", "no complaint"]},
 
 
 
 
 
 
 
 
197
  },
198
  "semiconductor_org_types": {
199
  "name": "semiconductor_org_types",
200
  "description": "",
201
+ "data_columns": ["Paper title", "Organization name", "ID"],
202
+ "label_columns": {"Label": ["company", "research institute", "university"]},
 
 
 
 
 
 
 
 
 
 
203
  },
204
  }
205