system HF staff commited on
Commit
d6e0beb
0 Parent(s):

Update files from the datasets library (from 1.2.0)

Browse files

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

Files changed (5) hide show
  1. .gitattributes +27 -0
  2. README.md +192 -0
  3. dataset_infos.json +1 -0
  4. dummy/1.1.0/dummy_data.zip +3 -0
  5. hippocorpus.py +153 -0
.gitattributes ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ *.7z filter=lfs diff=lfs merge=lfs -text
2
+ *.arrow filter=lfs diff=lfs merge=lfs -text
3
+ *.bin filter=lfs diff=lfs merge=lfs -text
4
+ *.bin.* filter=lfs diff=lfs merge=lfs -text
5
+ *.bz2 filter=lfs diff=lfs merge=lfs -text
6
+ *.ftz filter=lfs diff=lfs merge=lfs -text
7
+ *.gz filter=lfs diff=lfs merge=lfs -text
8
+ *.h5 filter=lfs diff=lfs merge=lfs -text
9
+ *.joblib filter=lfs diff=lfs merge=lfs -text
10
+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
11
+ *.model filter=lfs diff=lfs merge=lfs -text
12
+ *.msgpack filter=lfs diff=lfs merge=lfs -text
13
+ *.onnx filter=lfs diff=lfs merge=lfs -text
14
+ *.ot filter=lfs diff=lfs merge=lfs -text
15
+ *.parquet filter=lfs diff=lfs merge=lfs -text
16
+ *.pb filter=lfs diff=lfs merge=lfs -text
17
+ *.pt filter=lfs diff=lfs merge=lfs -text
18
+ *.pth filter=lfs diff=lfs merge=lfs -text
19
+ *.rar filter=lfs diff=lfs merge=lfs -text
20
+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
21
+ *.tar.* filter=lfs diff=lfs merge=lfs -text
22
+ *.tflite filter=lfs diff=lfs merge=lfs -text
23
+ *.tgz filter=lfs diff=lfs merge=lfs -text
24
+ *.xz filter=lfs diff=lfs merge=lfs -text
25
+ *.zip filter=lfs diff=lfs merge=lfs -text
26
+ *.zstandard filter=lfs diff=lfs merge=lfs -text
27
+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,192 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ annotations_creators:
3
+ - expert-generated
4
+ language_creators:
5
+ - expert-generated
6
+ languages:
7
+ - en
8
+ licenses:
9
+ - other-my-license
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 1K<n<10K
14
+ source_datasets:
15
+ - original
16
+ task_categories:
17
+ - text-scoring
18
+ task_ids:
19
+ - text-scoring-other-narrative-flow
20
+ ---
21
+
22
+ # Dataset Card for [Dataset Name]
23
+
24
+ ## Table of Contents
25
+ - [Dataset Description](#dataset-description)
26
+ - [Dataset Summary](#dataset-summary)
27
+ - [Supported Tasks](#supported-tasks-and-leaderboards)
28
+ - [Languages](#languages)
29
+ - [Dataset Structure](#dataset-structure)
30
+ - [Data Instances](#data-instances)
31
+ - [Data Fields](#data-instances)
32
+ - [Data Splits](#data-instances)
33
+ - [Dataset Creation](#dataset-creation)
34
+ - [Curation Rationale](#curation-rationale)
35
+ - [Source Data](#source-data)
36
+ - [Annotations](#annotations)
37
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
38
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
39
+ - [Social Impact of Dataset](#social-impact-of-dataset)
40
+ - [Discussion of Biases](#discussion-of-biases)
41
+ - [Other Known Limitations](#other-known-limitations)
42
+ - [Additional Information](#additional-information)
43
+ - [Dataset Curators](#dataset-curators)
44
+ - [Licensing Information](#licensing-information)
45
+ - [Citation Information](#citation-information)
46
+
47
+ ## Dataset Description
48
+
49
+ - **Homepage:** [Hippocorpus](https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318)
50
+ - **Repository:** [Hippocorpus](https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318)
51
+ - **Paper:** [Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models](http://erichorvitz.com/cognitive_studies_narrative.pdf)
52
+ - **Point of Contact:** [Eric Horvitz](mailto:horvitz@microsoft.com)
53
+
54
+
55
+ ### Dataset Summary
56
+
57
+ To examine the cognitive processes of remembering and imagining and their traces in language, we introduce Hippocorpus, a dataset of 6,854 English diary-like short stories about recalled and imagined events. Using a crowdsourcing framework, we first collect recalled stories and summaries from workers, then provide these summaries to other workers who write imagined stories. Finally, months later, we collect a retold version of the recalled stories from a subset of recalled authors. Our dataset comes paired with author demographics (age, gender, race), their openness to experience, as well as some variables regarding the author's relationship to the event (e.g., how personal the event is, how often they tell its story, etc.).
58
+
59
+ ### Supported Tasks and Leaderboards
60
+
61
+ [More Information Needed]
62
+
63
+ ### Languages
64
+
65
+ The dataset can be found in English
66
+
67
+ ## Dataset Structure
68
+
69
+ [More Information Needed]
70
+
71
+ ### Data Instances
72
+
73
+ [More Information Needed]
74
+
75
+ ### Data Fields
76
+
77
+ This CSV file contains all the stories in Hippcorpus v2 (6854 stories)
78
+
79
+ These are the columns in the file:
80
+ - `AssignmentId`: Unique ID of this story
81
+ - `WorkTimeInSeconds`: Time in seconds that it took the worker to do the entire HIT (reading instructions, storywriting, questions)
82
+ - `WorkerId`: Unique ID of the worker (random string, not MTurk worker ID)
83
+ - `annotatorAge`: Lower limit of the age bucket of the worker. Buckets are: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55+
84
+ - `annotatorGender`: Gender of the worker
85
+ - `annotatorRace`: Race/ethnicity of the worker
86
+ - `distracted`: How distracted were you while writing your story? (5-point Likert)
87
+ - `draining`: How taxing/draining was writing for you emotionally? (5-point Likert)
88
+ - `frequency`: How often do you think about or talk about this event? (5-point Likert)
89
+ - `importance`: How impactful, important, or personal is this story/this event to you? (5-point Likert)
90
+ - `logTimeSinceEvent`: Log of time (days) since the recalled event happened
91
+ - `mainEvent`: Short phrase describing the main event described
92
+ - `memType`: Type of story (recalled, imagined, retold)
93
+ - `mostSurprising`: Short phrase describing what the most surpring aspect of the story was
94
+ - `openness`: Continuous variable representing the openness to experience of the worker
95
+ - `recAgnPairId`: ID of the recalled story that corresponds to this retold story (null for imagined stories). Group on this variable to get the recalled-retold pairs.
96
+ - `recImgPairId`: ID of the recalled story that corresponds to this imagined story (null for retold stories). Group on this variable to get the recalled-imagined pairs.
97
+ - `similarity`: How similar to your life does this event/story feel to you? (5-point Likert)
98
+ - `similarityReason`: Free text annotation of similarity
99
+ - `story`: Story about the imagined or recalled event (15-25 sentences)
100
+ - `stressful`: How stressful was this writing task? (5-point Likert)
101
+ - `summary`: Summary of the events in the story (1-3 sentences)
102
+ - `timeSinceEvent`: Time (num. days) since the recalled event happened
103
+
104
+ ### Data Splits
105
+
106
+ [More Information Needed]
107
+
108
+ ## Dataset Creation
109
+
110
+ [More Information Needed]
111
+
112
+ ### Curation Rationale
113
+
114
+ [More Information Needed]
115
+
116
+ ### Source Data
117
+
118
+ [More Information Needed]
119
+
120
+ #### Initial Data Collection and Normalization
121
+
122
+ [More Information Needed]
123
+
124
+ #### Who are the source language producers?
125
+
126
+ [More Information Needed]
127
+
128
+ ### Annotations
129
+
130
+ [More Information Needed]
131
+
132
+ #### Annotation process
133
+
134
+ [More Information Needed]
135
+
136
+ #### Who are the annotators?
137
+
138
+ [More Information Needed]
139
+
140
+ ### Personal and Sensitive Information
141
+
142
+ [More Information Needed]
143
+
144
+ ## Considerations for Using the Data
145
+
146
+ [More Information Needed]
147
+
148
+ ### Social Impact of Dataset
149
+
150
+ [More Information Needed]
151
+
152
+ ### Discussion of Biases
153
+
154
+ [More Information Needed]
155
+
156
+ ### Other Known Limitations
157
+
158
+ [More Information Needed]
159
+
160
+ ## Additional Information
161
+
162
+ [More Information Needed]
163
+
164
+ ### Dataset Curators
165
+
166
+ The dataset was initially created by Maarten Sap, Eric Horvitz, Yejin Choi, Noah A. Smith, James W. Pennebaker, during work done at Microsoft Research.
167
+
168
+ ### Licensing Information
169
+
170
+ Hippocorpus is distributed under the [Open Use of Data Agreement v1.0](https://msropendata-web-api.azurewebsites.net/licenses/f1f352a6-243f-4905-8e00-389edbca9e83/view).
171
+
172
+ ### Citation Information
173
+
174
+ ```
175
+ @inproceedings{sap-etal-2020-recollection,
176
+ title = "Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models",
177
+ author = "Sap, Maarten and
178
+ Horvitz, Eric and
179
+ Choi, Yejin and
180
+ Smith, Noah A. and
181
+ Pennebaker, James",
182
+ booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
183
+ month = jul,
184
+ year = "2020",
185
+ address = "Online",
186
+ publisher = "Association for Computational Linguistics",
187
+ url = "https://www.aclweb.org/anthology/2020.acl-main.178",
188
+ doi = "10.18653/v1/2020.acl-main.178",
189
+ pages = "1970--1978",
190
+ abstract = "We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.",
191
+ }
192
+ ```
dataset_infos.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"default": {"description": "To examine the cognitive processes of remembering and imagining and their traces in language, we introduce Hippocorpus, a dataset of 6,854 English diary-like short stories about recalled and imagined events. Using a crowdsourcing framework, we first collect recalled stories and summaries from workers, then provide these summaries to other workers who write imagined stories. Finally, months later, we collect a retold version of the recalled stories from a subset of recalled authors. Our dataset comes paired with author demographics (age, gender, race), their openness to experience, as well as some variables regarding the author's relationship to the event (e.g., how personal the event is, how often they tell its story, etc.).\n", "citation": "@inproceedings{sap-etal-2020-recollection,\n title = \"Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models\",\n author = \"Sap, Maarten and\n Horvitz, Eric and\n Choi, Yejin and\n Smith, Noah A. and\n Pennebaker, James\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.acl-main.178\",\n doi = \"10.18653/v1/2020.acl-main.178\",\n pages = \"1970--1978\",\n abstract = \"We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.\",\n}\n", "homepage": "https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318", "license": "", "features": {"AssignmentId": {"dtype": "string", "id": null, "_type": "Value"}, "WorkTimeInSeconds": {"dtype": "string", "id": null, "_type": "Value"}, "WorkerId": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorAge": {"dtype": "float32", "id": null, "_type": "Value"}, "annotatorGender": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorRace": {"dtype": "string", "id": null, "_type": "Value"}, "distracted": {"dtype": "float32", "id": null, "_type": "Value"}, "draining": {"dtype": "float32", "id": null, "_type": "Value"}, "frequency": {"dtype": "float32", "id": null, "_type": "Value"}, "importance": {"dtype": "float32", "id": null, "_type": "Value"}, "logTimeSinceEvent": {"dtype": "string", "id": null, "_type": "Value"}, "mainEvent": {"dtype": "string", "id": null, "_type": "Value"}, "memType": {"dtype": "string", "id": null, "_type": "Value"}, "mostSurprising": {"dtype": "string", "id": null, "_type": "Value"}, "openness": {"dtype": "string", "id": null, "_type": "Value"}, "recAgnPairId": {"dtype": "string", "id": null, "_type": "Value"}, "recImgPairId": {"dtype": "string", "id": null, "_type": "Value"}, "similarity": {"dtype": "string", "id": null, "_type": "Value"}, "similarityReason": {"dtype": "string", "id": null, "_type": "Value"}, "story": {"dtype": "string", "id": null, "_type": "Value"}, "stressful": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}, "timeSinceEvent": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "hippocorpus", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7229795, "num_examples": 6854, "dataset_name": "hippocorpus"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 7229795, "size_in_bytes": 7229795}}
dummy/1.1.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0dc3cab86041adc8cad8f51023631aaea95b06f214ad23325007170d96eadace
3
+ size 10623
hippocorpus.py ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
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
+ from __future__ import absolute_import, division, print_function
17
+
18
+ import csv
19
+ import os
20
+
21
+ import datasets
22
+
23
+
24
+ # Find for instance the citation on arxiv or on the dataset repo/website
25
+ _CITATION = """\
26
+ @inproceedings{sap-etal-2020-recollection,
27
+ title = "Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models",
28
+ author = "Sap, Maarten and
29
+ Horvitz, Eric and
30
+ Choi, Yejin and
31
+ Smith, Noah A. and
32
+ Pennebaker, James",
33
+ booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
34
+ month = jul,
35
+ year = "2020",
36
+ address = "Online",
37
+ publisher = "Association for Computational Linguistics",
38
+ url = "https://www.aclweb.org/anthology/2020.acl-main.178",
39
+ doi = "10.18653/v1/2020.acl-main.178",
40
+ pages = "1970--1978",
41
+ abstract = "We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.",
42
+ }
43
+ """
44
+
45
+ _DESCRIPTION = """\
46
+ To examine the cognitive processes of remembering and imagining and their traces in language, we introduce Hippocorpus, a dataset of 6,854 English diary-like short stories about recalled and imagined events. Using a crowdsourcing framework, we first collect recalled stories and summaries from workers, then provide these summaries to other workers who write imagined stories. Finally, months later, we collect a retold version of the recalled stories from a subset of recalled authors. Our dataset comes paired with author demographics (age, gender, race), their openness to experience, as well as some variables regarding the author's relationship to the event (e.g., how personal the event is, how often they tell its story, etc.).
47
+ """
48
+
49
+ _HOMEPAGE = "https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318"
50
+
51
+
52
+ class Hippocorpus(datasets.GeneratorBasedBuilder):
53
+
54
+ VERSION = datasets.Version("1.1.0")
55
+
56
+ @property
57
+ def manual_download_instructions(self):
58
+ return """\
59
+ To use hippocorpus you need to download it manually. Please go to its homepage (https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318)and login. Extract all files in one folder and use the path folder in datasets.load_dataset('hippocorpus', data_dir='path/to/folder/folder_name')
60
+ """
61
+
62
+ def _info(self):
63
+ return datasets.DatasetInfo(
64
+ # This is the description that will appear on the datasets page.
65
+ description=_DESCRIPTION,
66
+ # datasets.features.FeatureConnectors
67
+ features=datasets.Features(
68
+ {
69
+ # These are the features of your dataset like images, labels ...
70
+ "AssignmentId": datasets.Value("string"),
71
+ "WorkTimeInSeconds": datasets.Value("string"),
72
+ "WorkerId": datasets.Value("string"),
73
+ "annotatorAge": datasets.Value("float32"),
74
+ "annotatorGender": datasets.Value("string"),
75
+ "annotatorRace": datasets.Value("string"),
76
+ "distracted": datasets.Value("float32"),
77
+ "draining": datasets.Value("float32"),
78
+ "frequency": datasets.Value("float32"),
79
+ "importance": datasets.Value("float32"),
80
+ "logTimeSinceEvent": datasets.Value("string"),
81
+ "mainEvent": datasets.Value("string"),
82
+ "memType": datasets.Value("string"),
83
+ "mostSurprising": datasets.Value("string"),
84
+ "openness": datasets.Value("string"),
85
+ "recAgnPairId": datasets.Value("string"),
86
+ "recImgPairId": datasets.Value("string"),
87
+ "similarity": datasets.Value("string"),
88
+ "similarityReason": datasets.Value("string"),
89
+ "story": datasets.Value("string"),
90
+ "stressful": datasets.Value("string"),
91
+ "summary": datasets.Value("string"),
92
+ "timeSinceEvent": datasets.Value("string"),
93
+ }
94
+ ),
95
+ # If there's a common (input, target) tuple from the features,
96
+ # specify them here. They'll be used if as_supervised=True in
97
+ # builder.as_dataset.
98
+ supervised_keys=None,
99
+ # Homepage of the dataset for documentation
100
+ homepage=_HOMEPAGE,
101
+ citation=_CITATION,
102
+ )
103
+
104
+ def _split_generators(self, dl_manager):
105
+ """Returns SplitGenerators."""
106
+ # dl_manager is a datasets.download.DownloadManager that can be used to
107
+ # download and extract URLs
108
+ data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
109
+
110
+ if not os.path.exists(data_dir):
111
+ raise FileNotFoundError(
112
+ "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('hippocorpus', data_dir=...)` that includes files unzipped from the hippocorpus zip. Manual download instructions: {}".format(
113
+ data_dir, self.manual_download_instructions
114
+ )
115
+ )
116
+ return [
117
+ datasets.SplitGenerator(
118
+ name=datasets.Split.TRAIN,
119
+ # These kwargs will be passed to _generate_examples
120
+ gen_kwargs={"filepath": os.path.join(data_dir, "hippoCorpusV2.csv")},
121
+ ),
122
+ ]
123
+
124
+ def _generate_examples(self, filepath):
125
+ """Yields examples."""
126
+ with open(filepath, encoding="utf-8") as f:
127
+ data = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_NONE)
128
+ for id_, row in enumerate(data):
129
+ yield id_, {
130
+ "AssignmentId": row["AssignmentId"],
131
+ "WorkTimeInSeconds": row["WorkTimeInSeconds"],
132
+ "WorkerId": row["WorkerId"],
133
+ "annotatorAge": row["annotatorAge"],
134
+ "annotatorGender": row["annotatorGender"],
135
+ "annotatorRace": row["annotatorRace"],
136
+ "distracted": row["distracted"],
137
+ "draining": row["draining"],
138
+ "frequency": row["frequency"],
139
+ "importance": row["importance"],
140
+ "logTimeSinceEvent": row["logTimeSinceEvent"],
141
+ "mainEvent": row["mainEvent"],
142
+ "memType": row["memType"],
143
+ "mostSurprising": row["mostSurprising"],
144
+ "openness": row["openness"],
145
+ "recAgnPairId": row["recAgnPairId"],
146
+ "recImgPairId": row["recImgPairId"],
147
+ "similarity": row["similarity"],
148
+ "similarityReason": row["similarityReason"],
149
+ "story": row["story"],
150
+ "stressful": row["stressful"],
151
+ "summary": row["summary"],
152
+ "timeSinceEvent": row["timeSinceEvent"],
153
+ }