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Update files from the datasets library (from 1.12.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.12.0

Files changed (3) hide show
  1. README.md +40 -14
  2. dataset_infos.json +1 -1
  3. ttc4900.py +32 -24
README.md CHANGED
@@ -18,11 +18,13 @@ task_categories:
18
  task_ids:
19
  - text-classification-other-news-category-classification
20
  paperswithcode_id: null
 
21
  ---
22
 
23
  # Dataset Card for TTC4900: A Benchmark Data for Turkish Text Categorization
24
 
25
  ## Table of Contents
 
26
  - [Dataset Description](#dataset-description)
27
  - [Dataset Summary](#dataset-summary)
28
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
@@ -48,15 +50,26 @@ paperswithcode_id: null
48
 
49
  ## Dataset Description
50
 
51
- - **Homepage:** [https://www.kaggle.com/savasy/ttc4900](https://www.kaggle.com/savasy/ttc4900)
52
- - **Point of Contact:** [ Avatar
53
- Savaş Yıldırım](mailto:savasy@gmail.com)
 
54
 
55
  ### Dataset Summary
56
 
57
  The data set is taken from [kemik group](http://www.kemik.yildiz.edu.tr/)
 
 
58
 
59
- The data are pre-processed (noun phrase chunking etc.) for the text categorization problem by the study ["A Knowledge-poor Approach to Turkish Text Categorization with a Comparative Analysis, Proceedings of CICLING 2014, Springer LNCS, Nepal, 2014"](https://link.springer.com/chapter/10.1007/978-3-642-54903-8_36)
 
 
 
 
 
 
 
 
60
 
61
  ### Languages
62
 
@@ -77,7 +90,6 @@ Here is an example from the dataset:
77
  }
78
  ```
79
 
80
-
81
  ### Data Fields
82
 
83
  - **category** : Indicates to which category the news text belongs.
@@ -96,21 +108,16 @@ It is not divided into Train set and Test set.
96
 
97
  ### Source Data
98
 
99
- [More Information Needed]
100
-
101
  #### Initial Data Collection and Normalization
102
 
103
  The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth.
104
 
105
-
106
  #### Who are the source language producers?
107
 
108
  Turkish online news sites.
109
 
110
  ### Annotations
111
 
112
- The dataset does not contain any additional annotations.
113
-
114
  #### Annotation process
115
 
116
  [More Information Needed]
@@ -125,7 +132,11 @@ The dataset does not contain any additional annotations.
125
 
126
  ## Considerations for Using the Data
127
 
128
- ### Discussion of Social Impact and Biases
 
 
 
 
129
 
130
  [More Information Needed]
131
 
@@ -137,7 +148,7 @@ The dataset does not contain any additional annotations.
137
 
138
  ### Dataset Curators
139
 
140
- [More Information Needed]
141
 
142
  ### Licensing Information
143
 
@@ -145,8 +156,23 @@ The dataset does not contain any additional annotations.
145
 
146
  ### Citation Information
147
 
148
- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
149
 
150
  ### Contributions
151
 
152
- Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset.
 
18
  task_ids:
19
  - text-classification-other-news-category-classification
20
  paperswithcode_id: null
21
+ pretty_name: TTC4900 - A Benchmark Data for Turkish Text Categorization
22
  ---
23
 
24
  # Dataset Card for TTC4900: A Benchmark Data for Turkish Text Categorization
25
 
26
  ## Table of Contents
27
+ - [Table of Contents](#table-of-contents)
28
  - [Dataset Description](#dataset-description)
29
  - [Dataset Summary](#dataset-summary)
30
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
 
50
 
51
  ## Dataset Description
52
 
53
+ - **Homepage:** [TTC4900 Homepage](https://www.kaggle.com/savasy/ttc4900)
54
+ - **Repository:** [TTC4900 Repository](https://github.com/savasy/TurkishTextClassification)
55
+ - **Paper:** [A Comparison of Different Approaches to Document Representation in Turkish Language](https://dergipark.org.tr/en/pub/sdufenbed/issue/38975/456349)
56
+ - **Point of Contact:** [Savaş Yıldırım](mailto:savasy@gmail.com)
57
 
58
  ### Dataset Summary
59
 
60
  The data set is taken from [kemik group](http://www.kemik.yildiz.edu.tr/)
61
+ The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth.
62
+ We named TTC4900 by mimicking the name convention of TTC 3600 dataset shared by the study ["A Knowledge-poor Approach to Turkish Text Categorization with a Comparative Analysis, Proceedings of CICLING 2014, Springer LNCS, Nepal, 2014"](https://link.springer.com/chapter/10.1007/978-3-642-54903-8_36)
63
 
64
+ If you use the dataset in a paper, please refer https://www.kaggle.com/savasy/ttc4900 as footnote and cite one of the papers as follows:
65
+
66
+ - A Comparison of Different Approaches to Document Representation in Turkish Language, SDU Journal of Natural and Applied Science, Vol 22, Issue 2, 2018
67
+ - A comparative analysis of text classification for Turkish language, Pamukkale University Journal of Engineering Science Volume 25 Issue 5, 2018
68
+ - A Knowledge-poor Approach to Turkish Text Categorization with a Comparative Analysis, Proceedings of CICLING 2014, Springer LNCS, Nepal, 2014.
69
+
70
+ ### Supported Tasks and Leaderboards
71
+
72
+ [More Information Needed]
73
 
74
  ### Languages
75
 
 
90
  }
91
  ```
92
 
 
93
  ### Data Fields
94
 
95
  - **category** : Indicates to which category the news text belongs.
 
108
 
109
  ### Source Data
110
 
 
 
111
  #### Initial Data Collection and Normalization
112
 
113
  The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth.
114
 
 
115
  #### Who are the source language producers?
116
 
117
  Turkish online news sites.
118
 
119
  ### Annotations
120
 
 
 
121
  #### Annotation process
122
 
123
  [More Information Needed]
 
132
 
133
  ## Considerations for Using the Data
134
 
135
+ ### Social Impact of Dataset
136
+
137
+ [More Information Needed]
138
+
139
+ ### Discussion of Biases
140
 
141
  [More Information Needed]
142
 
 
148
 
149
  ### Dataset Curators
150
 
151
+ The dataset was created by [Savaş Yıldırım](https://github.com/savasy)
152
 
153
  ### Licensing Information
154
 
 
156
 
157
  ### Citation Information
158
 
159
+ ```
160
+ @article{doi:10.5505/pajes.2018.15931,
161
+ author = {Yıldırım, Savaş and Yıldız, Tuğba},
162
+ title = {A comparative analysis of text classification for Turkish language},
163
+ journal = {Pamukkale Univ Muh Bilim Derg},
164
+ volume = {24},
165
+ number = {5},
166
+ pages = {879-886},
167
+ year = {2018},
168
+ doi = {10.5505/pajes.2018.15931},
169
+ note ={doi: 10.5505/pajes.2018.15931},
170
+
171
+ URL = {https://dx.doi.org/10.5505/pajes.2018.15931},
172
+ eprint = {https://dx.doi.org/10.5505/pajes.2018.15931}
173
+ }
174
+ ```
175
 
176
  ### Contributions
177
 
178
+ Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset.
dataset_infos.json CHANGED
@@ -1 +1 @@
1
- {"default": {"description": "The data set is taken from kemik group\nhttp://www.kemik.yildiz.edu.tr/\nThe data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth.\nWe named TTC4900 by mimicking the name convention of TTC 3600 dataset shared by the study http://journals.sagepub.com/doi/abs/10.1177/0165551515620551\n", "citation": "", "homepage": "https://www.kaggle.com/savasy/ttc4900", "license": "CC0: Public Domain", "features": {"category": {"num_classes": 7, "names": ["siyaset", "dunya", "ekonomi", "kultur", "saglik", "spor", "teknoloji"], "names_file": null, "id": null, "_type": "ClassLabel"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tt_c4900", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10640831, "num_examples": 4900, "dataset_name": "tt_c4900"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 10640831, "size_in_bytes": 10640831}, "ttc4900": {"description": "The data set is taken from kemik group\nhttp://www.kemik.yildiz.edu.tr/\nThe data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth.\nWe named TTC4900 by mimicking the name convention of TTC 3600 dataset shared by the study http://journals.sagepub.com/doi/abs/10.1177/0165551515620551\n", "citation": "", "homepage": "https://www.kaggle.com/savasy/ttc4900", "license": "CC0: Public Domain", "features": {"category": {"num_classes": 7, "names": ["siyaset", "dunya", "ekonomi", "kultur", "saglik", "spor", "teknoloji"], "names_file": null, "id": null, "_type": "ClassLabel"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tt_c4900", "config_name": "ttc4900", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10640831, "num_examples": 4900, "dataset_name": "tt_c4900"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 10640831, "size_in_bytes": 10640831}}
 
1
+ {"ttc4900": {"description": "The data set is taken from kemik group\nhttp://www.kemik.yildiz.edu.tr/\nThe data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth.\nWe named TTC4900 by mimicking the name convention of TTC 3600 dataset shared by the study http://journals.sagepub.com/doi/abs/10.1177/0165551515620551\n\nIf you use the dataset in a paper, please refer https://www.kaggle.com/savasy/ttc4900 as footnote and cite one of the papers as follows:\n\n- A Comparison of Different Approaches to Document Representation in Turkish Language, SDU Journal of Natural and Applied Science, Vol 22, Issue 2, 2018\n- A comparative analysis of text classification for Turkish language, Pamukkale University Journal of Engineering Science Volume 25 Issue 5, 2018\n- A Knowledge-poor Approach to Turkish Text Categorization with a Comparative Analysis, Proceedings of CICLING 2014, Springer LNCS, Nepal, 2014.\n", "citation": "@article{doi:10.5505/pajes.2018.15931,\nauthor = {Y\u0131ld\u0131r\u0131m, Sava\u015f and Y\u0131ld\u0131z, Tu\u011fba},\ntitle = {A comparative analysis of text classification for Turkish language},\njournal = {Pamukkale Univ Muh Bilim Derg},\nvolume = {24},\nnumber = {5},\npages = {879-886},\nyear = {2018},\ndoi = {10.5505/pajes.2018.15931},\nnote ={doi: 10.5505/pajes.2018.15931},\n\nURL = {https://dx.doi.org/10.5505/pajes.2018.15931},\neprint = {https://dx.doi.org/10.5505/pajes.2018.15931}\n}\n", "homepage": "https://www.kaggle.com/savasy/ttc4900", "license": "CC0: Public Domain", "features": {"category": {"num_classes": 7, "names": ["siyaset", "dunya", "ekonomi", "kultur", "saglik", "spor", "teknoloji"], "names_file": null, "id": null, "_type": "ClassLabel"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "text-classification", "text_column": "text", "label_column": "category", "labels": ["dunya", "ekonomi", "kultur", "saglik", "siyaset", "spor", "teknoloji"]}], "builder_name": "ttc4900", "config_name": "ttc4900", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10640831, "num_examples": 4900, "dataset_name": "ttc4900"}}, "download_checksums": {"https://raw.githubusercontent.com/savasy/TurkishTextClassification/master/7allV03.csv": {"num_bytes": 10627541, "checksum": "e17b79e89a3679ed77b3d5fd6d855fca43e9986a714cd4927c646c2be692c23e"}}, "download_size": 10627541, "post_processing_size": null, "dataset_size": 10640831, "size_in_bytes": 21268372}}
ttc4900.py CHANGED
@@ -17,9 +17,9 @@
17
 
18
 
19
  import csv
20
- import os
21
 
22
  import datasets
 
23
 
24
 
25
  logger = datasets.logging.get_logger(__name__)
@@ -30,11 +30,34 @@ The data set is taken from kemik group
30
  http://www.kemik.yildiz.edu.tr/
31
  The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth.
32
  We named TTC4900 by mimicking the name convention of TTC 3600 dataset shared by the study http://journals.sagepub.com/doi/abs/10.1177/0165551515620551
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
33
  """
34
 
35
- _CITATION = ""
36
  _LICENSE = "CC0: Public Domain"
37
  _HOMEPAGE = "https://www.kaggle.com/savasy/ttc4900"
 
38
  _FILENAME = "7allV03.csv"
39
 
40
 
@@ -60,18 +83,6 @@ class TTC4900(datasets.GeneratorBasedBuilder):
60
  ),
61
  ]
62
 
63
- @property
64
- def manual_download_instructions(self):
65
- return """\
66
- You need to go to https://www.kaggle.com/savasy/ttc4900,
67
- and manually download the ttc4900. Once it is completed,
68
- a file named archive.zip will be appeared in your Downloads folder
69
- or whichever folder your browser chooses to save files to. You then have
70
- to unzip the file and move 7allV03.csv under <path/to/folder>.
71
- The <path/to/folder> can e.g. be "~/manual_data".
72
- ttc4900 can then be loaded using the following command `datasets.load_dataset("ttc4900", data_dir="<path/to/folder>")`.
73
- """
74
-
75
  def _info(self):
76
  return datasets.DatasetInfo(
77
  description=_DESCRIPTION,
@@ -90,21 +101,18 @@ class TTC4900(datasets.GeneratorBasedBuilder):
90
  license=_LICENSE,
91
  # Citation for the dataset
92
  citation=_CITATION,
 
93
  )
94
 
95
  def _split_generators(self, dl_manager):
96
  """Returns SplitGenerators."""
97
- path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
98
- if not os.path.exists(path_to_manual_file):
99
- raise FileNotFoundError(
100
- "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('ttc4900', data_dir=...)` that includes a file name {}. Manual download instructions: {})".format(
101
- path_to_manual_file, _FILENAME, self.manual_download_instructions
102
- )
103
- )
104
  return [
105
- datasets.SplitGenerator(
106
- name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(path_to_manual_file, _FILENAME)}
107
- )
108
  ]
109
 
110
  def _generate_examples(self, filepath):
 
17
 
18
 
19
  import csv
 
20
 
21
  import datasets
22
+ from datasets.tasks import TextClassification
23
 
24
 
25
  logger = datasets.logging.get_logger(__name__)
 
30
  http://www.kemik.yildiz.edu.tr/
31
  The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth.
32
  We named TTC4900 by mimicking the name convention of TTC 3600 dataset shared by the study http://journals.sagepub.com/doi/abs/10.1177/0165551515620551
33
+
34
+ If you use the dataset in a paper, please refer https://www.kaggle.com/savasy/ttc4900 as footnote and cite one of the papers as follows:
35
+
36
+ - A Comparison of Different Approaches to Document Representation in Turkish Language, SDU Journal of Natural and Applied Science, Vol 22, Issue 2, 2018
37
+ - A comparative analysis of text classification for Turkish language, Pamukkale University Journal of Engineering Science Volume 25 Issue 5, 2018
38
+ - A Knowledge-poor Approach to Turkish Text Categorization with a Comparative Analysis, Proceedings of CICLING 2014, Springer LNCS, Nepal, 2014.
39
+ """
40
+
41
+ _CITATION = """\
42
+ @article{doi:10.5505/pajes.2018.15931,
43
+ author = {Yıldırım, Savaş and Yıldız, Tuğba},
44
+ title = {A comparative analysis of text classification for Turkish language},
45
+ journal = {Pamukkale Univ Muh Bilim Derg},
46
+ volume = {24},
47
+ number = {5},
48
+ pages = {879-886},
49
+ year = {2018},
50
+ doi = {10.5505/pajes.2018.15931},
51
+ note ={doi: 10.5505/pajes.2018.15931},
52
+
53
+ URL = {https://dx.doi.org/10.5505/pajes.2018.15931},
54
+ eprint = {https://dx.doi.org/10.5505/pajes.2018.15931}
55
+ }
56
  """
57
 
 
58
  _LICENSE = "CC0: Public Domain"
59
  _HOMEPAGE = "https://www.kaggle.com/savasy/ttc4900"
60
+ _DOWNLOAD_URL = "https://raw.githubusercontent.com/savasy/TurkishTextClassification/master"
61
  _FILENAME = "7allV03.csv"
62
 
63
 
 
83
  ),
84
  ]
85
 
 
 
 
 
 
 
 
 
 
 
 
 
86
  def _info(self):
87
  return datasets.DatasetInfo(
88
  description=_DESCRIPTION,
 
101
  license=_LICENSE,
102
  # Citation for the dataset
103
  citation=_CITATION,
104
+ task_templates=[TextClassification(text_column="text", label_column="category")],
105
  )
106
 
107
  def _split_generators(self, dl_manager):
108
  """Returns SplitGenerators."""
109
+
110
+ urls_to_download = {
111
+ "train": _DOWNLOAD_URL + "/" + _FILENAME,
112
+ }
113
+ downloaded_files = dl_manager.download(urls_to_download)
 
 
114
  return [
115
+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
 
 
116
  ]
117
 
118
  def _generate_examples(self, filepath):