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1
  ---
2
- dataset_info:
3
- features:
4
- - name: prompt
5
- dtype: string
6
- id: field
7
- - name: input
8
- dtype: string
9
- id: field
10
- - name: input2
11
- dtype: string
12
- id: field
13
- - name: prompt-ents
14
- list:
15
- - name: user_id
16
- dtype: string
17
- id: question
18
- - name: value
19
- sequence:
20
- - name: start
21
- dtype: int32
22
- - name: end
23
- dtype: int32
24
- - name: label
25
- dtype: string
26
- - name: text
27
- dtype: string
28
- id: question
29
- - name: status
30
- dtype: string
31
- id: question
32
- - name: prompt-ents-suggestion
33
- sequence:
34
- - name: start
35
- dtype: int32
36
- - name: end
37
- dtype: int32
38
- - name: label
39
- dtype: string
40
- - name: text
41
- dtype: string
42
- - name: score
43
- dtype: float32
44
- id: suggestion
45
- - name: prompt-ents-suggestion-metadata
46
- struct:
47
- - name: type
48
- dtype: string
49
- id: suggestion-metadata
50
- - name: score
51
- dtype: float32
52
- id: suggestion-metadata
53
- - name: agent
54
- dtype: string
55
- id: suggestion-metadata
56
- - name: input-ents
57
- list:
58
- - name: user_id
59
- dtype: string
60
- id: question
61
- - name: value
62
- sequence:
63
- - name: start
64
- dtype: int32
65
- - name: end
66
- dtype: int32
67
- - name: label
68
- dtype: string
69
- - name: text
70
- dtype: string
71
- id: question
72
- - name: status
73
- dtype: string
74
- id: question
75
- - name: input-ents-suggestion
76
- sequence:
77
- - name: start
78
- dtype: int32
79
- - name: end
80
- dtype: int32
81
- - name: label
82
- dtype: string
83
- - name: text
84
- dtype: string
85
- - name: score
86
- dtype: float32
87
- id: suggestion
88
- - name: input-ents-suggestion-metadata
89
- struct:
90
- - name: type
91
- dtype: string
92
- id: suggestion-metadata
93
- - name: score
94
- dtype: float32
95
- id: suggestion-metadata
96
- - name: agent
97
- dtype: string
98
- id: suggestion-metadata
99
- - name: info-extraction
100
- list:
101
- - name: user_id
102
- dtype: string
103
- id: question
104
- - name: value
105
- sequence:
106
- - name: start
107
- dtype: int32
108
- - name: end
109
- dtype: int32
110
- - name: label
111
- dtype: string
112
- - name: text
113
- dtype: string
114
- id: question
115
- - name: status
116
- dtype: string
117
- id: question
118
- - name: info-extraction-suggestion
119
- sequence:
120
- - name: start
121
- dtype: int32
122
- - name: end
123
- dtype: int32
124
- - name: label
125
- dtype: string
126
- - name: text
127
- dtype: string
128
- - name: score
129
- dtype: float32
130
- id: suggestion
131
- - name: info-extraction-suggestion-metadata
132
- struct:
133
- - name: type
134
- dtype: string
135
- id: suggestion-metadata
136
- - name: score
137
- dtype: float32
138
- id: suggestion-metadata
139
- - name: agent
140
- dtype: string
141
- id: suggestion-metadata
142
- - name: final-response
143
- list:
144
- - name: user_id
145
- dtype: string
146
- id: question
147
- - name: value
148
- dtype: string
149
- id: question
150
- - name: status
151
- dtype: string
152
- id: question
153
- - name: final-response-suggestion
154
- dtype: string
155
- id: suggestion
156
- - name: final-response-suggestion-metadata
157
- struct:
158
- - name: type
159
- dtype: string
160
- id: suggestion-metadata
161
- - name: score
162
- dtype: float32
163
- id: suggestion-metadata
164
- - name: agent
165
- dtype: string
166
- id: suggestion-metadata
167
- - name: external_id
168
- dtype: string
169
- id: external_id
170
- - name: metadata
171
- dtype: string
172
- id: metadata
173
- splits:
174
- - name: train
175
- num_bytes: 690344
176
- num_examples: 212
177
- download_size: 466005
178
- dataset_size: 690344
179
  ---
180
- # Dataset Card for "test_spans_dataset"
181
 
182
- [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ size_categories: n<1K
3
+ tags:
4
+ - rlfh
5
+ - argilla
6
+ - human-feedback
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7
  ---
 
8
 
9
+ # Dataset Card for test_spans_dataset
10
+
11
+ This dataset has been created with [Argilla](https://docs.argilla.io).
12
+
13
+ As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
14
+
15
+ ## Dataset Description
16
+
17
+ - **Homepage:** https://argilla.io
18
+ - **Repository:** https://github.com/argilla-io/argilla
19
+ - **Paper:**
20
+ - **Leaderboard:**
21
+ - **Point of Contact:**
22
+
23
+ ### Dataset Summary
24
+
25
+ This dataset contains:
26
+
27
+ * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
28
+
29
+ * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
30
+
31
+ * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
32
+
33
+ ### Load with Argilla
34
+
35
+ To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
36
+
37
+ ```python
38
+ import argilla as rg
39
+
40
+ ds = rg.FeedbackDataset.from_huggingface("nataliaElv/test_spans_dataset")
41
+ ```
42
+
43
+ ### Load with `datasets`
44
+
45
+ To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
46
+
47
+ ```python
48
+ from datasets import load_dataset
49
+
50
+ ds = load_dataset("nataliaElv/test_spans_dataset")
51
+ ```
52
+
53
+ ### Supported Tasks and Leaderboards
54
+
55
+ This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
56
+
57
+ There are no leaderboards associated with this dataset.
58
+
59
+ ### Languages
60
+
61
+ [More Information Needed]
62
+
63
+ ## Dataset Structure
64
+
65
+ ### Data in Argilla
66
+
67
+ The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
68
+
69
+ The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
70
+
71
+ | Field Name | Title | Type | Required | Markdown |
72
+ | ---------- | ----- | ---- | -------- | -------- |
73
+ | prompt | Prompt-(Ents) | text | True | False |
74
+ | input | Input-(Ents) | text | True | False |
75
+ | input2 | Input-(Info Extraction) | text | True | False |
76
+
77
+
78
+ The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
79
+
80
+ | Question Name | Title | Type | Required | Description | Values/Labels |
81
+ | ------------- | ----- | ---- | -------- | ----------- | ------------- |
82
+ | prompt-ents | Highlight the entities inside Prompt-(Ents): | span | True | N/A | N/A |
83
+ | input-ents | Highlight the entities inside Input-(Ents): | span | True | N/A | N/A |
84
+ | info-extraction | Highlight the information inside Input-(Info Extraction) that is relevant to the prompt | span | True | N/A | N/A |
85
+ | final-response | Provide a correct response given the prompt and the input: | text | True | Only make the necessary corrections. You can submit the text as it is, if it's correct. | N/A |
86
+
87
+
88
+ The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
89
+
90
+ The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
91
+
92
+
93
+
94
+ | Metadata Name | Title | Type | Values | Visible for Annotators |
95
+ | ------------- | ----- | ---- | ------ | ---------------------- |
96
+
97
+
98
+ The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
99
+
100
+ ### Data Instances
101
+
102
+ An example of a dataset instance in Argilla looks as follows:
103
+
104
+ ```json
105
+ {
106
+ "external_id": null,
107
+ "fields": {
108
+ "input": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.",
109
+ "input2": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.",
110
+ "prompt": "When did Virgin Australia start operating?"
111
+ },
112
+ "metadata": {},
113
+ "responses": [],
114
+ "suggestions": [
115
+ {
116
+ "agent": null,
117
+ "question_name": "prompt-ents",
118
+ "score": null,
119
+ "type": null,
120
+ "value": [
121
+ {
122
+ "end": 25,
123
+ "label": "ORG",
124
+ "score": 0.9999854564666748,
125
+ "start": 9
126
+ }
127
+ ]
128
+ },
129
+ {
130
+ "agent": null,
131
+ "question_name": "input-ents",
132
+ "score": null,
133
+ "type": null,
134
+ "value": [
135
+ {
136
+ "end": 16,
137
+ "label": "ORG",
138
+ "score": 0.9998990297317505,
139
+ "start": 0
140
+ },
141
+ {
142
+ "end": 71,
143
+ "label": "ORG",
144
+ "score": 0.9999301433563232,
145
+ "start": 38
146
+ },
147
+ {
148
+ "end": 162,
149
+ "label": "ORG",
150
+ "score": 0.9961417317390442,
151
+ "start": 156
152
+ },
153
+ {
154
+ "end": 224,
155
+ "label": "ORG",
156
+ "score": 0.9999250173568726,
157
+ "start": 213
158
+ },
159
+ {
160
+ "end": 319,
161
+ "label": "LOC",
162
+ "score": 0.9998377561569214,
163
+ "start": 310
164
+ },
165
+ {
166
+ "end": 376,
167
+ "label": "ORG",
168
+ "score": 0.9999576807022095,
169
+ "start": 360
170
+ },
171
+ {
172
+ "end": 464,
173
+ "label": "LOC",
174
+ "score": 0.9998786449432373,
175
+ "start": 455
176
+ },
177
+ {
178
+ "end": 487,
179
+ "label": "LOC",
180
+ "score": 0.9998598098754883,
181
+ "start": 479
182
+ },
183
+ {
184
+ "end": 498,
185
+ "label": "LOC",
186
+ "score": 0.9997498393058777,
187
+ "start": 489
188
+ },
189
+ {
190
+ "end": 509,
191
+ "label": "LOC",
192
+ "score": 0.9998868703842163,
193
+ "start": 503
194
+ }
195
+ ]
196
+ },
197
+ {
198
+ "agent": null,
199
+ "question_name": "final-response",
200
+ "score": null,
201
+ "type": null,
202
+ "value": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route."
203
+ }
204
+ ],
205
+ "vectors": {}
206
+ }
207
+ ```
208
+
209
+ While the same record in HuggingFace `datasets` looks as follows:
210
+
211
+ ```json
212
+ {
213
+ "external_id": null,
214
+ "final-response": [],
215
+ "final-response-suggestion": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route.",
216
+ "final-response-suggestion-metadata": {
217
+ "agent": null,
218
+ "score": null,
219
+ "type": null
220
+ },
221
+ "info-extraction": [],
222
+ "info-extraction-suggestion": null,
223
+ "info-extraction-suggestion-metadata": {
224
+ "agent": null,
225
+ "score": null,
226
+ "type": null
227
+ },
228
+ "input": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.",
229
+ "input-ents": [],
230
+ "input-ents-suggestion": {
231
+ "end": [
232
+ 16,
233
+ 71,
234
+ 162,
235
+ 224,
236
+ 319,
237
+ 376,
238
+ 464,
239
+ 487,
240
+ 498,
241
+ 509
242
+ ],
243
+ "label": [
244
+ "ORG",
245
+ "ORG",
246
+ "ORG",
247
+ "ORG",
248
+ "LOC",
249
+ "ORG",
250
+ "LOC",
251
+ "LOC",
252
+ "LOC",
253
+ "LOC"
254
+ ],
255
+ "score": [
256
+ 0.9998990297317505,
257
+ 0.9999301433563232,
258
+ 0.9961417317390442,
259
+ 0.9999250173568726,
260
+ 0.9998377561569214,
261
+ 0.9999576807022095,
262
+ 0.9998786449432373,
263
+ 0.9998598098754883,
264
+ 0.9997498393058777,
265
+ 0.9998868703842163
266
+ ],
267
+ "start": [
268
+ 0,
269
+ 38,
270
+ 156,
271
+ 213,
272
+ 310,
273
+ 360,
274
+ 455,
275
+ 479,
276
+ 489,
277
+ 503
278
+ ],
279
+ "text": [
280
+ "Virgin Australia",
281
+ "Virgin Australia Airlines Pty Ltd",
282
+ "Virgin",
283
+ "Virgin Blue",
284
+ "Australia",
285
+ "Ansett Australia",
286
+ "Australia",
287
+ "Brisbane",
288
+ "Melbourne",
289
+ "Sydney"
290
+ ]
291
+ },
292
+ "input-ents-suggestion-metadata": {
293
+ "agent": null,
294
+ "score": null,
295
+ "type": null
296
+ },
297
+ "input2": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.",
298
+ "metadata": "{}",
299
+ "prompt": "When did Virgin Australia start operating?",
300
+ "prompt-ents": [],
301
+ "prompt-ents-suggestion": {
302
+ "end": [
303
+ 25
304
+ ],
305
+ "label": [
306
+ "ORG"
307
+ ],
308
+ "score": [
309
+ 0.9999854564666748
310
+ ],
311
+ "start": [
312
+ 9
313
+ ],
314
+ "text": [
315
+ "Virgin Australia"
316
+ ]
317
+ },
318
+ "prompt-ents-suggestion-metadata": {
319
+ "agent": null,
320
+ "score": null,
321
+ "type": null
322
+ }
323
+ }
324
+ ```
325
+
326
+ ### Data Fields
327
+
328
+ Among the dataset fields, we differentiate between the following:
329
+
330
+ * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
331
+
332
+ * **prompt** is of type `text`.
333
+ * **input** is of type `text`.
334
+ * **input2** is of type `text`.
335
+
336
+ * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
337
+
338
+ * **prompt-ents** is of type `span`.
339
+ * **input-ents** is of type `span`.
340
+ * **info-extraction** is of type `span`.
341
+ * **final-response** is of type `text`, and description "Only make the necessary corrections. You can submit the text as it is, if it's correct.".
342
+
343
+ * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
344
+
345
+ * (optional) **prompt-ents-suggestion** is of type `span`.
346
+ * (optional) **input-ents-suggestion** is of type `span`.
347
+ * (optional) **info-extraction-suggestion** is of type `span`.
348
+ * (optional) **final-response-suggestion** is of type `text`.
349
+
350
+
351
+
352
+ Additionally, we also have two more fields that are optional and are the following:
353
+
354
+ * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
355
+ * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
356
+
357
+ ### Data Splits
358
+
359
+ The dataset contains a single split, which is `train`.
360
+
361
+ ## Dataset Creation
362
+
363
+ ### Curation Rationale
364
+
365
+ [More Information Needed]
366
+
367
+ ### Source Data
368
+
369
+ #### Initial Data Collection and Normalization
370
+
371
+ [More Information Needed]
372
+
373
+ #### Who are the source language producers?
374
+
375
+ [More Information Needed]
376
+
377
+ ### Annotations
378
+
379
+ #### Annotation guidelines
380
+
381
+
382
+ This is a subset of the Dolly dataset with prompts classified as being Closed QA or Information Extractions tasks.
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+ In the record, you will find the prompt and the input of the task. In the first two fields, you will need to highlight and classify all entities found in the prompt and the input. These are marked as (Ents) for easier recognition.
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+ The input field is then repeated as "Input-(Info Extraction)". Using the "Relevant Info" tag, highlight all pieces of information in the input that are relevant to answer the prompt.
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+ Finally, you will be asked to provide a correct response following the prompt and the given input. You may submit the text as it is, if it's correct, or make any necessary amendments.
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+ #### Annotation process
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+ [More Information Needed]
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+ #### Who are the annotators?
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+ [More Information Needed]
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+ ### Personal and Sensitive Information
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+ [More Information Needed]
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+ ## Considerations for Using the Data
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+ ### Social Impact of Dataset
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+ [More Information Needed]
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+ ### Discussion of Biases
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+ [More Information Needed]
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+ ### Other Known Limitations
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+ [More Information Needed]
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+ ## Additional Information
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+ ### Dataset Curators
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+ [More Information Needed]
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+ ### Licensing Information
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+ [More Information Needed]
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+ ### Citation Information
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+ [More Information Needed]
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+ ### Contributions
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+ [More Information Needed]