Datasets:
cmrc2018

Sub-tasks: extractive-qa
Languages: Chinese
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: crowdsourced
Annotations Creators: crowdsourced
Source Datasets: original
system
HF staff
commited on
Commit
738e4c1
1 Parent(s): a86590a

Update files from the datasets library (from 1.8.0)

Browse files

Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

Files changed (3) hide show
  1. README.md +21 -3
  2. cmrc2018.py +6 -0
  3. dataset_infos.json +1 -1
README.md CHANGED
@@ -1,4 +1,22 @@
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  paperswithcode_id: cmrc-2018
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  ---
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@@ -95,9 +113,9 @@ The data fields are the same among all splits.
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  ### Data Splits
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- | name |train|validation|test|
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- |-------|----:|---------:|---:|
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- |default|10142| 3219|1002|
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  ## Dataset Creation
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  ---
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+ annotations_creators:
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+ - crowdsourced
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+ language_creators:
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+ - crowdsourced
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+ languages:
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+ - zh
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+ licenses:
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+ - cc-by-sa-4-0
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - 10K<n<100K
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - question-answering
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+ task_ids:
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+ - extractive-qa
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  paperswithcode_id: cmrc-2018
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  ---
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  ### Data Splits
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+ | name | train | validation | test |
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+ | ------- | ----: | ---------: | ---: |
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+ | default | 10142 | 3219 | 1002 |
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  ## Dataset Creation
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cmrc2018.py CHANGED
@@ -4,6 +4,7 @@
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  import json
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  import datasets
 
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  # TODO(cmrc2018): BibTeX citation
@@ -75,6 +76,11 @@ class Cmrc2018(datasets.GeneratorBasedBuilder):
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  # Homepage of the dataset for documentation
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  homepage=_URL,
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  citation=_CITATION,
 
 
 
 
 
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  )
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  def _split_generators(self, dl_manager):
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  import json
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  import datasets
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+ from datasets.tasks import QuestionAnsweringExtractive
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  # TODO(cmrc2018): BibTeX citation
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  # Homepage of the dataset for documentation
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  homepage=_URL,
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  citation=_CITATION,
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+ task_templates=[
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+ QuestionAnsweringExtractive(
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+ question_column="question", context_column="context", answers_column="answers"
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+ )
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+ ],
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  )
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  def _split_generators(self, dl_manager):
dataset_infos.json CHANGED
@@ -1 +1 @@
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- {"default": {"description": "A Span-Extraction dataset for Chinese machine reading comprehension to add language\ndiversities in this area. The dataset is composed by near 20,000 real questions annotated\non Wikipedia paragraphs by human experts. We also annotated a challenge set which\ncontains the questions that need comprehensive understanding and multi-sentence\ninference throughout the context.\n", "citation": "@inproceedings{cui-emnlp2019-cmrc2018,\n title = \"A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension\",\n author = \"Cui, Yiming and\n Liu, Ting and\n Che, Wanxiang and\n Xiao, Li and\n Chen, Zhipeng and\n Ma, Wentao and\n Wang, Shijin and\n Hu, Guoping\",\n booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)\",\n month = nov,\n year = \"2019\",\n address = \"Hong Kong, China\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/D19-1600\",\n doi = \"10.18653/v1/D19-1600\",\n pages = \"5886--5891\",\n}\n", "homepage": "https://github.com/ymcui/cmrc2018", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "cmrc2018", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1608065, "num_examples": 1002, "dataset_name": "cmrc2018"}, "train": {"name": "train", "num_bytes": 15519498, "num_examples": 10142, "dataset_name": "cmrc2018"}, "validation": {"name": "validation", "num_bytes": 5189046, "num_examples": 3219, "dataset_name": "cmrc2018"}}, "download_checksums": {"https://worksheets.codalab.org/rest/bundles/0x15022f0c4d3944a599ab27256686b9ac/contents/blob/": {"num_bytes": 7408757, "checksum": "5497aa2f81908e31d6b0e27d99b1f90ab63a8f58fa92fffe5d17cf62eba0c212"}, "https://worksheets.codalab.org/rest/bundles/0x72252619f67b4346a85e122049c3eabd/contents/blob/": {"num_bytes": 3299139, "checksum": "e9ff74231f05c230c6fa88b84441ee334d97234cbb610991cd94b82db00c7f1f"}, "https://worksheets.codalab.org/rest/bundles/0x182c2e71fac94fc2a45cc1a3376879f7/contents/blob/": {"num_bytes": 800221, "checksum": "f3fae95b57da8e03afb2b57467dd221417060ef4d82db13bf22fc88589f3a6f3"}}, "download_size": 11508117, "dataset_size": 22316609, "size_in_bytes": 33824726}}
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+ {"default": {"description": "A Span-Extraction dataset for Chinese machine reading comprehension to add language\ndiversities in this area. The dataset is composed by near 20,000 real questions annotated\non Wikipedia paragraphs by human experts. We also annotated a challenge set which\ncontains the questions that need comprehensive understanding and multi-sentence\ninference throughout the context.\n", "citation": "@inproceedings{cui-emnlp2019-cmrc2018,\n title = {A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension},\n author = {Cui, Yiming and\n Liu, Ting and\n Che, Wanxiang and\n Xiao, Li and\n Chen, Zhipeng and\n Ma, Wentao and\n Wang, Shijin and\n Hu, Guoping},\n booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},\n month = {nov},\n year = {2019},\n address = {Hong Kong, China},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/D19-1600},\n doi = {10.18653/v1/D19-1600},\n pages = {5886--5891}}\n", "homepage": "https://github.com/ymcui/cmrc2018", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "question-answering-extractive", "question_column": "question", "context_column": "context", "answers_column": "answers"}], "builder_name": "cmrc2018", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 15508110, "num_examples": 10142, "dataset_name": "cmrc2018"}, "validation": {"name": "validation", "num_bytes": 5183809, "num_examples": 3219, "dataset_name": "cmrc2018"}, "test": {"name": "test", "num_bytes": 1606931, "num_examples": 1002, "dataset_name": "cmrc2018"}}, "download_checksums": {"https://worksheets.codalab.org/rest/bundles/0x15022f0c4d3944a599ab27256686b9ac/contents/blob/": {"num_bytes": 7408757, "checksum": "5497aa2f81908e31d6b0e27d99b1f90ab63a8f58fa92fffe5d17cf62eba0c212"}, "https://worksheets.codalab.org/rest/bundles/0x72252619f67b4346a85e122049c3eabd/contents/blob/": {"num_bytes": 3299139, "checksum": "e9ff74231f05c230c6fa88b84441ee334d97234cbb610991cd94b82db00c7f1f"}, "https://worksheets.codalab.org/rest/bundles/0x182c2e71fac94fc2a45cc1a3376879f7/contents/blob/": {"num_bytes": 800221, "checksum": "f3fae95b57da8e03afb2b57467dd221417060ef4d82db13bf22fc88589f3a6f3"}}, "download_size": 11508117, "post_processing_size": null, "dataset_size": 22298850, "size_in_bytes": 33806967}}