albertvillanova HF staff commited on
Commit
209b28a
1 Parent(s): 366e0e8

Fix bug and checksum in irc_disentangle dataset (#4377)

Browse files

* Fix filepath segment and refactor

* Update checksum in metadata JSON

* Fix style

* Update dummy data

Commit from https://github.com/huggingface/datasets/commit/f2ef5e81822f976afe9baf21d0e6697793b2c960

dataset_infos.json CHANGED
@@ -1 +1 @@
1
- {"ubuntu": {"description": "Disentangling conversations mixed together in a single stream of messages is\na difficult task, made harder by the lack of large manually annotated\ndatasets. This new dataset of 77,563 messages manually annotated with\nreply-structure graphs that both disentangle conversations and define\ninternal conversation structure. The dataset is 16 times larger than all\npreviously released datasets combined, the first to include adjudication of\nannotation disagreements, and the first to include context.\n", "citation": "@inproceedings{kummerfeld-etal-2019-large, \n title = \"A Large-Scale Corpus for Conversation Disentanglement\", \n author = \"Kummerfeld, Jonathan K. and \n Gouravajhala, Sai R. and \n Peper, Joseph J. and \n Athreya, Vignesh and \n Gunasekara, Chulaka and \n Ganhotra, Jatin and \n Patel, Siva Sankalp and \n Polymenakos, Lazaros C and \n Lasecki, Walter\", \n booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\", \n month = jul, \n year = \"2019\", \n address = \"Florence, Italy\", \n publisher = \"Association for Computational Linguistics\", \n url = \"https://aclanthology.org/P19-1374\", \n doi = \"10.18653/v1/P19-1374\", \n pages = \"3846--3856\", \n arxiv = \"https://arxiv.org/abs/1810.11118\", \n software = \"https://jkk.name/irc-disentanglement\", \n data = \"https://jkk.name/irc-disentanglement\", \n abstract = \"Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89% of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.\", \n}", "homepage": "https://jkk.name/irc-disentanglement/", "license": "Creative Commons Attribution 4.0 International Public License", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "raw": {"dtype": "string", "id": null, "_type": "Value"}, "ascii": {"dtype": "string", "id": null, "_type": "Value"}, "tokenized": {"dtype": "string", "id": null, "_type": "Value"}, "date": {"dtype": "string", "id": null, "_type": "Value"}, "connections": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "irc_disentangle", "config_name": "ubuntu", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 55970472, "num_examples": 220616, "dataset_name": "irc_disentangle"}, "test": {"name": "test", "num_bytes": 3916881, "num_examples": 15010, "dataset_name": "irc_disentangle"}, "validation": {"name": "validation", "num_bytes": 3079360, "num_examples": 12510, "dataset_name": "irc_disentangle"}}, "download_checksums": {"https://github.com/jkkummerfeld/irc-disentanglement/tarball/master": {"num_bytes": 118470349, "checksum": "9e2c98a15191d729c0dbe10f309d836bd1ab32c0d03ffb2e0e4205287405fc4d"}}, "download_size": 118470349, "post_processing_size": null, "dataset_size": 62966713, "size_in_bytes": 181437062}, "channel_two": {"description": "Disentangling conversations mixed together in a single stream of messages is\na difficult task, made harder by the lack of large manually annotated\ndatasets. This new dataset of 77,563 messages manually annotated with\nreply-structure graphs that both disentangle conversations and define\ninternal conversation structure. The dataset is 16 times larger than all\npreviously released datasets combined, the first to include adjudication of\nannotation disagreements, and the first to include context.\n", "citation": "@inproceedings{kummerfeld-etal-2019-large, \n title = \"A Large-Scale Corpus for Conversation Disentanglement\", \n author = \"Kummerfeld, Jonathan K. and \n Gouravajhala, Sai R. and \n Peper, Joseph J. and \n Athreya, Vignesh and \n Gunasekara, Chulaka and \n Ganhotra, Jatin and \n Patel, Siva Sankalp and \n Polymenakos, Lazaros C and \n Lasecki, Walter\", \n booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\", \n month = jul, \n year = \"2019\", \n address = \"Florence, Italy\", \n publisher = \"Association for Computational Linguistics\", \n url = \"https://aclanthology.org/P19-1374\", \n doi = \"10.18653/v1/P19-1374\", \n pages = \"3846--3856\", \n arxiv = \"https://arxiv.org/abs/1810.11118\", \n software = \"https://jkk.name/irc-disentanglement\", \n data = \"https://jkk.name/irc-disentanglement\", \n abstract = \"Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89% of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.\", \n}", "homepage": "https://jkk.name/irc-disentanglement/", "license": "Creative Commons Attribution 4.0 International Public License", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "raw": {"dtype": "string", "id": null, "_type": "Value"}, "ascii": {"dtype": "string", "id": null, "_type": "Value"}, "tokenized": {"dtype": "string", "id": null, "_type": "Value"}, "connections": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "irc_disentangle", "config_name": "channel_two", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"dev": {"name": "dev", "num_bytes": 197189, "num_examples": 1001, "dataset_name": "irc_disentangle"}, "pilot": {"name": "pilot", "num_bytes": 92514, "num_examples": 501, "dataset_name": "irc_disentangle"}, "test": {"name": "test", "num_bytes": 186494, "num_examples": 1001, "dataset_name": "irc_disentangle"}, "pilot_dev": {"name": "pilot_dev", "num_bytes": 289695, "num_examples": 1501, "dataset_name": "irc_disentangle"}, "all_": {"name": "all_", "num_bytes": 495666, "num_examples": 2602, "dataset_name": "irc_disentangle"}}, "download_checksums": {"https://github.com/jkkummerfeld/irc-disentanglement/tarball/master": {"num_bytes": 118470349, "checksum": "9e2c98a15191d729c0dbe10f309d836bd1ab32c0d03ffb2e0e4205287405fc4d"}}, "download_size": 118470349, "post_processing_size": null, "dataset_size": 1261558, "size_in_bytes": 119731907}}
1
+ {"ubuntu": {"description": "Disentangling conversations mixed together in a single stream of messages is\na difficult task, made harder by the lack of large manually annotated\ndatasets. This new dataset of 77,563 messages manually annotated with\nreply-structure graphs that both disentangle conversations and define\ninternal conversation structure. The dataset is 16 times larger than all\npreviously released datasets combined, the first to include adjudication of\nannotation disagreements, and the first to include context.\n", "citation": "@inproceedings{kummerfeld-etal-2019-large,\n title = \"A Large-Scale Corpus for Conversation Disentanglement\",\n author = \"Kummerfeld, Jonathan K. and\n Gouravajhala, Sai R. and\n Peper, Joseph J. and\n Athreya, Vignesh and\n Gunasekara, Chulaka and\n Ganhotra, Jatin and\n Patel, Siva Sankalp and\n Polymenakos, Lazaros C and\n Lasecki, Walter\",\n booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2019\",\n address = \"Florence, Italy\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/P19-1374\",\n doi = \"10.18653/v1/P19-1374\",\n pages = \"3846--3856\",\n arxiv = \"https://arxiv.org/abs/1810.11118\",\n software = \"https://jkk.name/irc-disentanglement\",\n data = \"https://jkk.name/irc-disentanglement\",\n abstract = \"Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89% of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.\",\n}\n", "homepage": "https://jkk.name/irc-disentanglement/", "license": "Creative Commons Attribution 4.0 International Public License", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "raw": {"dtype": "string", "id": null, "_type": "Value"}, "ascii": {"dtype": "string", "id": null, "_type": "Value"}, "tokenized": {"dtype": "string", "id": null, "_type": "Value"}, "date": {"dtype": "string", "id": null, "_type": "Value"}, "connections": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "irc_disentangle", "config_name": "ubuntu", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 56012854, "num_examples": 220616, "dataset_name": "irc_disentangle"}, "validation": {"name": "validation", "num_bytes": 3081479, "num_examples": 12510, "dataset_name": "irc_disentangle"}, "test": {"name": "test", "num_bytes": 3919900, "num_examples": 15010, "dataset_name": "irc_disentangle"}}, "download_checksums": {"https://github.com/jkkummerfeld/irc-disentanglement/tarball/master": {"num_bytes": 118470210, "checksum": "e5232a65f5e97805a366a19b4c0b127dfcf91981a8681b33855bfb6c72706c2f"}}, "download_size": 118470210, "post_processing_size": null, "dataset_size": 63014233, "size_in_bytes": 181484443}, "channel_two": {"description": "Disentangling conversations mixed together in a single stream of messages is\na difficult task, made harder by the lack of large manually annotated\ndatasets. This new dataset of 77,563 messages manually annotated with\nreply-structure graphs that both disentangle conversations and define\ninternal conversation structure. The dataset is 16 times larger than all\npreviously released datasets combined, the first to include adjudication of\nannotation disagreements, and the first to include context.\n", "citation": "@inproceedings{kummerfeld-etal-2019-large,\n title = \"A Large-Scale Corpus for Conversation Disentanglement\",\n author = \"Kummerfeld, Jonathan K. and\n Gouravajhala, Sai R. and\n Peper, Joseph J. and\n Athreya, Vignesh and\n Gunasekara, Chulaka and\n Ganhotra, Jatin and\n Patel, Siva Sankalp and\n Polymenakos, Lazaros C and\n Lasecki, Walter\",\n booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2019\",\n address = \"Florence, Italy\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/P19-1374\",\n doi = \"10.18653/v1/P19-1374\",\n pages = \"3846--3856\",\n arxiv = \"https://arxiv.org/abs/1810.11118\",\n software = \"https://jkk.name/irc-disentanglement\",\n data = \"https://jkk.name/irc-disentanglement\",\n abstract = \"Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89% of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.\",\n}\n", "homepage": "https://jkk.name/irc-disentanglement/", "license": "Creative Commons Attribution 4.0 International Public License", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "raw": {"dtype": "string", "id": null, "_type": "Value"}, "ascii": {"dtype": "string", "id": null, "_type": "Value"}, "tokenized": {"dtype": "string", "id": null, "_type": "Value"}, "connections": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "irc_disentangle", "config_name": "channel_two", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"dev": {"name": "dev", "num_bytes": 197505, "num_examples": 1001, "dataset_name": "irc_disentangle"}, "pilot": {"name": "pilot", "num_bytes": 92663, "num_examples": 501, "dataset_name": "irc_disentangle"}, "test": {"name": "test", "num_bytes": 186823, "num_examples": 1001, "dataset_name": "irc_disentangle"}, "pilot_dev": {"name": "pilot_dev", "num_bytes": 290175, "num_examples": 1501, "dataset_name": "irc_disentangle"}, "all_": {"name": "all_", "num_bytes": 496524, "num_examples": 2602, "dataset_name": "irc_disentangle"}}, "download_checksums": {"https://github.com/jkkummerfeld/irc-disentanglement/tarball/master": {"num_bytes": 118470210, "checksum": "e5232a65f5e97805a366a19b4c0b127dfcf91981a8681b33855bfb6c72706c2f"}}, "download_size": 118470210, "post_processing_size": null, "dataset_size": 1263690, "size_in_bytes": 119733900}}
dummy/channel_two/1.0.0/dummy_data.zip CHANGED
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dummy/ubuntu/1.0.0/dummy_data.zip CHANGED
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+ size 9194
irc_disentangle.py CHANGED
@@ -119,40 +119,22 @@ class IRCDisentangle(datasets.GeneratorBasedBuilder):
119
 
120
  def _split_generators(self, dl_manager):
121
  """Returns SplitGenerators."""
122
- my_urls = _URL
123
- dl_dir = dl_manager.download_and_extract(my_urls)
124
-
125
- files = dict()
126
  if self.config.name == "ubuntu":
127
- for split in ["train", "dev", "test"]:
128
- files[split] = os.path.join(dl_dir, "jkkummerfeld-irc-disentanglement-fd379e9", "data", split)
129
-
130
  return [
131
  datasets.SplitGenerator(
132
- name=datasets.Split.TRAIN,
133
- gen_kwargs={
134
- "filepath": files["train"],
135
- "split": "train",
136
- },
137
- ),
138
- datasets.SplitGenerator(
139
- name=datasets.Split.TEST,
140
- gen_kwargs={
141
- "filepath": files["test"],
142
- "split": "test",
143
- },
144
- ),
145
- datasets.SplitGenerator(
146
- name=datasets.Split.VALIDATION,
147
  gen_kwargs={
148
- "filepath": files["dev"],
149
- "split": "dev",
150
  },
151
- ),
 
152
  ]
153
-
154
  elif self.config.name == "channel_two":
155
- filepath = os.path.join(dl_dir, "jkkummerfeld-irc-disentanglement-fd379e9", "data", "channel-two")
156
  return [
157
  datasets.SplitGenerator(
158
  name="dev",
119
 
120
  def _split_generators(self, dl_manager):
121
  """Returns SplitGenerators."""
122
+ dl_dir = dl_manager.download_and_extract(_URL)
123
+ filepath = os.path.join(dl_dir, "jkkummerfeld-irc-disentanglement-35f0a40", "data")
124
+ split_names = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "dev", datasets.Split.TEST: "test"}
 
125
  if self.config.name == "ubuntu":
 
 
 
126
  return [
127
  datasets.SplitGenerator(
128
+ name=split,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
129
  gen_kwargs={
130
+ "filepath": os.path.join(filepath, split_name),
131
+ "split": split_name,
132
  },
133
+ )
134
+ for split, split_name in split_names.items()
135
  ]
 
136
  elif self.config.name == "channel_two":
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+ filepath = os.path.join(filepath, "channel-two")
138
  return [
139
  datasets.SplitGenerator(
140
  name="dev",