Convert dataset to Parquet (#2)

- Convert dataset to Parquet (aa733cddfbffca00259b4f4a058b92d74b65380c)
- Add sts data files (b7f730b37c6eaa998178d7550ac35827fc0bf5ca)
- Add nli data files (0e05c2a3649a814bc206ae51d13d0a38ac991f64)
- Add ner data files (c302a004d918a49b00002e45475a0aeaa239aa1e)
- Add re data files (26a2ff79dc50e1b253243f1a511b031f56a38499)
- Add dp data files (e0e0e06ec993811871d9d81a2777a7fc8a61896c)
- Add mrc data files (9dc841ba15d7f362b0e55b4579307319e3cc167b)
- Add wos data files (5454eaaa94dafb629516e236faa87dbe0501cb09)
- Delete loading script (89d2a4e6e716de45c3e19ffe056665df11b3aac6)
- Delete legacy dataset_infos.json (2e32b5c21562b629dbd5140500af66e5db303bf9)

README.md

@@ -29,96 +29,76 @@ task_ids:
 - topic-classification
 paperswithcode_id: klue
 pretty_name: KLUE
+config_names:
+- dp
+- mrc
+- ner
+- nli
+- re
+- sts
+- wos
+- ynat
 tags:
 - relation-extraction
 dataset_info:
-- config_name: ynat
+- config_name: dp
   features:
-  - name: guid
-    dtype: string
-  - name: title
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': IT과학
-          '1': 경제
-          '2': 사회
-          '3': 생활문화
-          '4': 세계
-          '5': 스포츠
-          '6': 정치
-  - name: url
-    dtype: string
-  - name: date
+  - name: sentence
     dtype: string
+  - name: index
+    list: int32
+  - name: word_form
+    list: string
+  - name: lemma
+    list: string
+  - name: pos
+    list: string
+  - name: head
+    list: int32
+  - name: deprel
+    list: string
   splits:
   - name: train
-    num_bytes: 10109664
-    num_examples: 45678
+    num_bytes: 7899965
+    num_examples: 10000
   - name: validation
-    num_bytes: 2039197
-    num_examples: 9107
-  download_size: 4932555
-  dataset_size: 12148861
-- config_name: sts
+    num_bytes: 1557462
+    num_examples: 2000
+  download_size: 3742577
+  dataset_size: 9457427
+- config_name: mrc
   features:
-  - name: guid
-    dtype: string
-  - name: source
-    dtype: string
-  - name: sentence1
+  - name: title
     dtype: string
-  - name: sentence2
+  - name: context
     dtype: string
-  - name: labels
-    struct:
-    - name: label
-      dtype: float64
-    - name: real-label
-      dtype: float64
-    - name: binary-label
-      dtype:
-        class_label:
-          names:
-            '0': negative
-            '1': positive
-  splits:
-  - name: train
-    num_bytes: 2832921
-    num_examples: 11668
-  - name: validation
-    num_bytes: 122657
-    num_examples: 519
-  download_size: 1349875
-  dataset_size: 2955578
-- config_name: nli
-  features:
-  - name: guid
+  - name: news_category
     dtype: string
   - name: source
     dtype: string
-  - name: premise
+  - name: guid
     dtype: string
-  - name: hypothesis
+  - name: is_impossible
+    dtype: bool
+  - name: question_type
+    dtype: int32
+  - name: question
     dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': entailment
-          '1': neutral
-          '2': contradiction
+  - name: answers
+    sequence:
+    - name: answer_start
+      dtype: int32
+    - name: text
+      dtype: string
   splits:
   - name: train
-    num_bytes: 5719930
-    num_examples: 24998
+    num_bytes: 46505593
+    num_examples: 17554
   - name: validation
-    num_bytes: 673276
-    num_examples: 3000
-  download_size: 1257374
-  dataset_size: 6393206
+    num_bytes: 15583017
+    num_examples: 5841
+  download_size: 30098472
+  dataset_size: 62088610
 - config_name: ner
   features:
   - name: sentence
@@ -144,13 +124,39 @@ dataset_info:
           '12': O
   splits:
   - name: train
-    num_bytes: 19891953
+    num_bytes: 19891905
     num_examples: 21008
   - name: validation
-    num_bytes: 4937579
+    num_bytes: 4937563
     num_examples: 5000
-  download_size: 4308644
-  dataset_size: 24829532
+  download_size: 5265887
+  dataset_size: 24829468
+- config_name: nli
+  features:
+  - name: guid
+    dtype: string
+  - name: source
+    dtype: string
+  - name: premise
+    dtype: string
+  - name: hypothesis
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': entailment
+          '1': neutral
+          '2': contradiction
+  splits:
+  - name: train
+    num_bytes: 5719882
+    num_examples: 24998
+  - name: validation
+    num_bytes: 673260
+    num_examples: 3000
+  download_size: 2056116
+  dataset_size: 6393142
 - config_name: re
   features:
   - name: guid
@@ -215,71 +221,44 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 11145538
+    num_bytes: 11145426
     num_examples: 32470
   - name: validation
-    num_bytes: 2559300
+    num_bytes: 2559272
     num_examples: 7765
-  download_size: 5669259
-  dataset_size: 13704838
-- config_name: dp
-  features:
-  - name: sentence
-    dtype: string
-  - name: index
-    list: int32
-  - name: word_form
-    list: string
-  - name: lemma
-    list: string
-  - name: pos
-    list: string
-  - name: head
-    list: int32
-  - name: deprel
-    list: string
-  splits:
-  - name: train
-    num_bytes: 7900009
-    num_examples: 10000
-  - name: validation
-    num_bytes: 1557506
-    num_examples: 2000
-  download_size: 2033461
-  dataset_size: 9457515
-- config_name: mrc
+  download_size: 8190257
+  dataset_size: 13704698
+- config_name: sts
   features:
-  - name: title
-    dtype: string
-  - name: context
-    dtype: string
-  - name: news_category
+  - name: guid
     dtype: string
   - name: source
     dtype: string
-  - name: guid
+  - name: sentence1
     dtype: string
-  - name: is_impossible
-    dtype: bool
-  - name: question_type
-    dtype: int32
-  - name: question
+  - name: sentence2
     dtype: string
-  - name: answers
-    sequence:
-    - name: answer_start
-      dtype: int32
-    - name: text
-      dtype: string
+  - name: labels
+    struct:
+    - name: label
+      dtype: float64
+    - name: real-label
+      dtype: float64
+    - name: binary-label
+      dtype:
+        class_label:
+          names:
+            '0': negative
+            '1': positive
   splits:
   - name: train
-    num_bytes: 46505665
-    num_examples: 17554
+    num_bytes: 2832889
+    num_examples: 11668
   - name: validation
-    num_bytes: 15583053
-    num_examples: 5841
-  download_size: 19218422
-  dataset_size: 62088718
+    num_bytes: 122641
+    num_examples: 519
+  download_size: 1587855
+  dataset_size: 2955530
 - config_name: wos
   features:
   - name: guid
@@ -296,22 +275,92 @@ dataset_info:
       list: string
   splits:
   - name: train
-    num_bytes: 26677002
+    num_bytes: 26676970
     num_examples: 8000
   - name: validation
-    num_bytes: 3488943
+    num_bytes: 3488911
     num_examples: 1000
-  download_size: 4785657
-  dataset_size: 30165945
-config_names:
-- dp
-- mrc
-- ner
-- nli
-- re
-- sts
-- wos
-- ynat
+  download_size: 6358855
+  dataset_size: 30165881
+- config_name: ynat
+  features:
+  - name: guid
+    dtype: string
+  - name: title
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': IT과학
+          '1': 경제
+          '2': 사회
+          '3': 생활문화
+          '4': 세계
+          '5': 스포츠
+          '6': 정치
+  - name: url
+    dtype: string
+  - name: date
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 10109584
+    num_examples: 45678
+  - name: validation
+    num_bytes: 2039181
+    num_examples: 9107
+  download_size: 5012303
+  dataset_size: 12148765
+configs:
+- config_name: dp
+  data_files:
+  - split: train
+    path: dp/train-*
+  - split: validation
+    path: dp/validation-*
+- config_name: mrc
+  data_files:
+  - split: train
+    path: mrc/train-*
+  - split: validation
+    path: mrc/validation-*
+- config_name: ner
+  data_files:
+  - split: train
+    path: ner/train-*
+  - split: validation
+    path: ner/validation-*
+- config_name: nli
+  data_files:
+  - split: train
+    path: nli/train-*
+  - split: validation
+    path: nli/validation-*
+- config_name: re
+  data_files:
+  - split: train
+    path: re/train-*
+  - split: validation
+    path: re/validation-*
+- config_name: sts
+  data_files:
+  - split: train
+    path: sts/train-*
+  - split: validation
+    path: sts/validation-*
+- config_name: wos
+  data_files:
+  - split: train
+    path: wos/train-*
+  - split: validation
+    path: wos/validation-*
+- config_name: ynat
+  data_files:
+  - split: train
+    path: ynat/train-*
+  - split: validation
+    path: ynat/validation-*
 ---
 
 # Dataset Card for KLUE

dataset_infos.json

@@ -1 +0,0 @@
-{"ynat": {"description": "KLUE (Korean Language Understanding Evaluation)\nKorean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language\nunderstanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible\nto anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain\nunambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics\nfor every task, thus establishing fair comparison across Korean language models.\n", "citation": "@misc{park2021klue,\n      title={KLUE: Korean Language Understanding Evaluation},\n      author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},\n      year={2021},\n      eprint={2105.09680},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://klue-benchmark.com/tasks/66/overview/description", "license": "CC-BY-SA-4.0", "features": {"guid": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 7, "names": ["IT\uacfc\ud559", "\uacbd\uc81c", "\uc0ac\ud68c", "\uc0dd\ud65c\ubb38\ud654", "\uc138\uacc4", "\uc2a4\ud3ec\uce20", "\uc815\uce58"], "names_file": null, "id": null, "_type": "ClassLabel"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "date": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "klue", "config_name": "ynat", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10109664, "num_examples": 45678, "dataset_name": "klue"}, "validation": {"name": "validation", "num_bytes": 2039197, "num_examples": 9107, "dataset_name": "klue"}}, "download_checksums": {"http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000066/data/ynat-v1.tar.gz": {"num_bytes": 4932555, "checksum": "820a4d1d6d1fd83e2a421f856965d3cfc5c93627935ce8c5b27468c6113fc482"}}, "download_size": 4932555, "post_processing_size": null, "dataset_size": 12148861, "size_in_bytes": 17081416}, "sts": {"description": "KLUE (Korean Language Understanding Evaluation)\nKorean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language\nunderstanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible\nto anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain\nunambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics\nfor every task, thus establishing fair comparison across Korean language models.\n", "citation": "@misc{park2021klue,\n      title={KLUE: Korean Language Understanding Evaluation},\n      author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},\n      year={2021},\n      eprint={2105.09680},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://klue-benchmark.com/tasks/67/overview/description", "license": "CC-BY-SA-4.0", "features": {"guid": {"dtype": "string", "id": null, "_type": "Value"}, "source": {"dtype": "string", "id": null, "_type": "Value"}, "sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "labels": {"label": {"dtype": "float64", "id": null, "_type": "Value"}, "real-label": {"dtype": "float64", "id": null, "_type": "Value"}, "binary-label": {"num_classes": 2, "names": ["negative", "positive"], "names_file": null, "id": null, "_type": "ClassLabel"}}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "klue", "config_name": "sts", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2832921, "num_examples": 11668, "dataset_name": "klue"}, "validation": {"name": "validation", "num_bytes": 122657, "num_examples": 519, "dataset_name": "klue"}}, "download_checksums": {"http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000067/data/klue-sts-v1.tar.gz": {"num_bytes": 1349875, "checksum": "539341ba78a3b351c686cf70a448ac7a5886ed95f0719d5e3d2378ba703213bd"}}, "download_size": 1349875, "post_processing_size": null, "dataset_size": 2955578, "size_in_bytes": 4305453}, "nli": {"description": "KLUE (Korean Language Understanding Evaluation)\nKorean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language\nunderstanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible\nto anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain\nunambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics\nfor every task, thus establishing fair comparison across Korean language models.\n", "citation": "@misc{park2021klue,\n      title={KLUE: Korean Language Understanding Evaluation},\n      author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},\n      year={2021},\n      eprint={2105.09680},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://klue-benchmark.com/tasks/68/overview/description", "license": "CC-BY-SA-4.0", "features": {"guid": {"dtype": "string", "id": null, "_type": "Value"}, "source": {"dtype": "string", "id": null, "_type": "Value"}, "premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "klue", "config_name": "nli", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5719930, "num_examples": 24998, "dataset_name": "klue"}, "validation": {"name": "validation", "num_bytes": 673276, "num_examples": 3000, "dataset_name": "klue"}}, "download_checksums": {"http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000068/data/klue-nli-v1.tar.gz": {"num_bytes": 1257374, "checksum": "388be2033ef712072201903795a35b4f86826ee3ed3b62dc0c98e1721baa8850"}}, "download_size": 1257374, "post_processing_size": null, "dataset_size": 6393206, "size_in_bytes": 7650580}, "ner": {"description": "KLUE (Korean Language Understanding Evaluation)\nKorean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language\nunderstanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible\nto anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain\nunambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics\nfor every task, thus establishing fair comparison across Korean language models.\n", "citation": "@misc{park2021klue,\n      title={KLUE: Korean Language Understanding Evaluation},\n      author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},\n      year={2021},\n      eprint={2105.09680},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://klue-benchmark.com/tasks/69/overview/description", "license": "CC-BY-SA-4.0", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 13, "names": ["B-DT", "I-DT", "B-LC", "I-LC", "B-OG", "I-OG", "B-PS", "I-PS", "B-QT", "I-QT", "B-TI", "I-TI", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "klue", "config_name": "ner", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 19891953, "num_examples": 21008, "dataset_name": "klue"}, "validation": {"name": "validation", "num_bytes": 4937579, "num_examples": 5000, "dataset_name": "klue"}}, "download_checksums": {"http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000069/data/klue-ner-v1.tar.gz": {"num_bytes": 4308644, "checksum": "848a89759ac6b7c149c9a00d820726fe2a140c22782201f1a40d856672e7ea8e"}}, "download_size": 4308644, "post_processing_size": null, "dataset_size": 24829532, "size_in_bytes": 29138176}, "re": {"description": "KLUE (Korean Language Understanding Evaluation)\nKorean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language\nunderstanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible\nto anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain\nunambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics\nfor every task, thus establishing fair comparison across Korean language models.\n", "citation": "@misc{park2021klue,\n      title={KLUE: Korean Language Understanding Evaluation},\n      author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},\n      year={2021},\n      eprint={2105.09680},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://klue-benchmark.com/tasks/70/overview/description", "license": "CC-BY-SA-4.0", "features": {"guid": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "subject_entity": {"word": {"dtype": "string", "id": null, "_type": "Value"}, "start_idx": {"dtype": "int32", "id": null, "_type": "Value"}, "end_idx": {"dtype": "int32", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}}, "object_entity": {"word": {"dtype": "string", "id": null, "_type": "Value"}, "start_idx": {"dtype": "int32", "id": null, "_type": "Value"}, "end_idx": {"dtype": "int32", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}}, "label": {"num_classes": 30, "names": ["no_relation", "org:dissolved", "org:founded", "org:place_of_headquarters", "org:alternate_names", "org:member_of", "org:members", "org:political/religious_affiliation", "org:product", "org:founded_by", "org:top_members/employees", "org:number_of_employees/members", "per:date_of_birth", "per:date_of_death", "per:place_of_birth", "per:place_of_death", "per:place_of_residence", "per:origin", "per:employee_of", "per:schools_attended", "per:alternate_names", "per:parents", "per:children", "per:siblings", "per:spouse", "per:other_family", "per:colleagues", "per:product", "per:religion", "per:title"], "names_file": null, "id": null, "_type": "ClassLabel"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "klue", "config_name": "re", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 11145538, "num_examples": 32470, "dataset_name": "klue"}, "validation": {"name": "validation", "num_bytes": 2559300, "num_examples": 7765, "dataset_name": "klue"}}, "download_checksums": {"http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000070/data/klue-re-v1.tar.gz": {"num_bytes": 5669259, "checksum": "b09ceac0d986cc09e42fcda9c7f2873c0eea8ec0629baf91fead36580790f8f5"}}, "download_size": 5669259, "post_processing_size": null, "dataset_size": 13704838, "size_in_bytes": 19374097}, "dp": {"description": "KLUE (Korean Language Understanding Evaluation)\nKorean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language\nunderstanding capability of Korean language models. 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Futhermore, we build an evaluation system and carefully choose evaluations metrics\nfor every task, thus establishing fair comparison across Korean language models.\n", "citation": "@misc{park2021klue,\n      title={KLUE: Korean Language Understanding Evaluation},\n      author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},\n      year={2021},\n      eprint={2105.09680},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://klue-benchmark.com/tasks/71/overview/description", "license": "CC-BY-SA-4.0", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "index": [{"dtype": "int32", "id": null, "_type": "Value"}], "word_form": [{"dtype": "string", "id": null, "_type": "Value"}], "lemma": [{"dtype": "string", "id": null, "_type": "Value"}], "pos": [{"dtype": "string", "id": null, "_type": "Value"}], "head": [{"dtype": "int32", "id": null, "_type": "Value"}], "deprel": [{"dtype": "string", "id": null, "_type": "Value"}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "klue", "config_name": "dp", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7900009, "num_examples": 10000, "dataset_name": "klue"}, "validation": {"name": "validation", "num_bytes": 1557506, "num_examples": 2000, "dataset_name": "klue"}}, "download_checksums": {"http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000071/data/klue-dp-v1.tar.gz": {"num_bytes": 2033461, "checksum": "2c76a3543a50599ac6640ad360ba00eac36e0b5b2363f708a614d6e50844d17b"}}, "download_size": 2033461, "post_processing_size": null, "dataset_size": 9457515, "size_in_bytes": 11490976}, "mrc": {"description": "KLUE (Korean Language Understanding Evaluation)\nKorean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language\nunderstanding capability of Korean language models. 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klue.py

@@ -1,533 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""KLUE (Korean Language Understanding Evaluation) benchmark."""
-
-
-import csv
-import json
-import textwrap
-
-import datasets
-
-
-_KLUE_CITATION = """\
-@misc{park2021klue,
-      title={KLUE: Korean Language Understanding Evaluation},
-      author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},
-      year={2021},
-      eprint={2105.09680},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-"""
-
-_KLUE_DESCRIPTION = """\
-KLUE (Korean Language Understanding Evaluation)
-Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
-understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
-to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain
-unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics
-for every task, thus establishing fair comparison across Korean language models.
-"""
-
-_DATA_URLs = {
-    "ynat": "http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000066/data/ynat-v1.tar.gz",
-    "sts": "http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000067/data/klue-sts-v1.tar.gz",
-    "nli": "http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000068/data/klue-nli-v1.tar.gz",
-    "ner": "http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000069/data/klue-ner-v1.tar.gz",
-    "re": "http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000070/data/klue-re-v1.tar.gz",
-    "dp": "http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000071/data/klue-dp-v1.tar.gz",
-    "mrc": "http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000072/data/klue-mrc-v1.tar.gz",
-    "wos": "http://klue-benchmark.com.s3.amazonaws.com/app/Competitions/000073/data/wos-v1.tar.gz",
-}
-
-_DESCRIPTION_URLs = {
-    "ynat": "https://klue-benchmark.com/tasks/66/overview/description",
-    "sts": "https://klue-benchmark.com/tasks/67/overview/description",
-    "nli": "https://klue-benchmark.com/tasks/68/overview/description",
-    "ner": "https://klue-benchmark.com/tasks/69/overview/description",
-    "re": "https://klue-benchmark.com/tasks/70/overview/description",
-    "dp": "https://klue-benchmark.com/tasks/71/overview/description",
-    "mrc": "https://klue-benchmark.com/tasks/72/overview/description",
-    "wos": "https://klue-benchmark.com/tasks/73/overview/description",
-}
-
-_LICENSE = "CC-BY-SA-4.0"
-
-
-class KlueConfig(datasets.BuilderConfig):
-    """BuilderConfig for KLUE."""
-
-    def __init__(
-        self,
-        features,
-        data_url,
-        url,
-        file_map,
-        **kwargs,
-    ):
-        """BuilderConfig for KLUE."""
-
-        super(KlueConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-        self.features = features
-        self.data_url = data_url
-        self.url = url
-        self.file_map = file_map
-
-
-class Klue(datasets.GeneratorBasedBuilder):
-    """The General Language Understanding Evaluation (GLUE) benchmark."""
-
-    BUILDER_CONFIGS = [
-        KlueConfig(
-            name="ynat",
-            features={
-                "guid": datasets.Value("string"),
-                "title": datasets.Value("string"),
-                "label": datasets.features.ClassLabel(names=["IT과학", "경제", "사회", "생활문화", "세계", "스포츠", "정치"]),
-                "url": datasets.Value("string"),
-                "date": datasets.Value("string"),
-            },
-            description=textwrap.dedent(
-                """\
-            In topic classification (TC), the goal is to predict the topic of a given text
-            snippet. We include TC in our KLUE benchmark, as inferring the topic of a text is a key
-            capability that should be possessed by a language understanding system.
-            Following a typical single  sentence classification task, we introduce YNAT, a Younhap
-            News Agency news headlines for Topic Classification. For Korean, no dataset has been
-            proposed for this task, which motivates us to construct the first Korean topic
-            classification benchmark. In this task, given a news headline, a text classifier must
-            predict a topic which is one of politics, economy, society, culture, world, IT/science,
-            and sports. Macro-F1 score is used to evaluate a system."""
-            ),
-            data_url=_DATA_URLs["ynat"],
-            url=_DESCRIPTION_URLs["ynat"],
-            file_map={
-                "train": "ynat-v1_train.json",
-                "dev": "ynat-v1_dev.json",
-            },
-        ),
-        KlueConfig(
-            name="sts",
-            features={
-                "guid": datasets.Value("string"),
-                "source": datasets.Value("string"),
-                "sentence1": datasets.Value("string"),
-                "sentence2": datasets.Value("string"),
-                "labels": {
-                    "label": datasets.Value("float64"),
-                    "real-label": datasets.Value("float64"),
-                    "binary-label": datasets.ClassLabel(names=["negative", "positive"]),
-                },
-            },
-            description=textwrap.dedent(
-                """\
-            STS is a task which aims to predict the semantic similarity of two input sentences as
-            a real value between 0 and 5. Note that we furthure binarized the prediction scores
-            into two classes with a threshold score 3.0 (paraphrased or not) and evaluated with
-            a classification metric.
-            """
-            ),
-            data_url=_DATA_URLs["sts"],
-            url=_DESCRIPTION_URLs["sts"],
-            file_map={
-                "train": "klue-sts-v1_train.json",
-                "dev": "klue-sts-v1_dev.json",
-            },
-        ),
-        KlueConfig(
-            name="nli",
-            features={
-                "guid": datasets.Value("string"),
-                "source": datasets.Value("string"),
-                "premise": datasets.Value("string"),
-                "hypothesis": datasets.Value("string"),
-                "label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
-            },
-            description=textwrap.dedent(
-                """\
-            NLI is a task to infer the relationship between a hypothesis sentence and a premise
-            sentence. Given the premise, the model determines if the hypothesis is true (entailment),
-            false (contradiction), or undetermined (neutral).
-            """
-            ),
-            data_url=_DATA_URLs["nli"],
-            url=_DESCRIPTION_URLs["nli"],
-            file_map={
-                "train": "klue-nli-v1_train.json",
-                "dev": "klue-nli-v1_dev.json",
-            },
-        ),
-        KlueConfig(
-            name="ner",
-            features={
-                "sentence": datasets.Value("string"),
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "ner_tags": datasets.Sequence(
-                    datasets.ClassLabel(
-                        names=[
-                            "B-DT",
-                            "I-DT",
-                            "B-LC",
-                            "I-LC",
-                            "B-OG",
-                            "I-OG",
-                            "B-PS",
-                            "I-PS",
-                            "B-QT",
-                            "I-QT",
-                            "B-TI",
-                            "I-TI",
-                            "O",
-                        ]
-                    )
-                ),
-            },
-            description=textwrap.dedent(
-                """\
-            NER is a task to detect the boundaries of named entities in unstructured text and to
-            classify the types. A named entity can be of one of predefined entity types such as
-            person, location, organization, time expressions, quantities and monetary values.
-            """
-            ),
-            data_url=_DATA_URLs["ner"],
-            url=_DESCRIPTION_URLs["ner"],
-            file_map={
-                "train": "klue-ner-v1_train.tsv",
-                "dev": "klue-ner-v1_dev.tsv",
-            },
-        ),
-        KlueConfig(
-            name="re",
-            features={
-                "guid": datasets.Value("string"),
-                "sentence": datasets.Value("string"),
-                "subject_entity": {
-                    "word": datasets.Value("string"),
-                    "start_idx": datasets.Value("int32"),
-                    "end_idx": datasets.Value("int32"),
-                    "type": datasets.Value("string"),
-                },
-                "object_entity": {
-                    "word": datasets.Value("string"),
-                    "start_idx": datasets.Value("int32"),
-                    "end_idx": datasets.Value("int32"),
-                    "type": datasets.Value("string"),
-                },
-                "label": datasets.ClassLabel(
-                    names=[
-                        "no_relation",
-                        "org:dissolved",
-                        "org:founded",
-                        "org:place_of_headquarters",
-                        "org:alternate_names",
-                        "org:member_of",
-                        "org:members",
-                        "org:political/religious_affiliation",
-                        "org:product",
-                        "org:founded_by",
-                        "org:top_members/employees",
-                        "org:number_of_employees/members",
-                        "per:date_of_birth",
-                        "per:date_of_death",
-                        "per:place_of_birth",
-                        "per:place_of_death",
-                        "per:place_of_residence",
-                        "per:origin",
-                        "per:employee_of",
-                        "per:schools_attended",
-                        "per:alternate_names",
-                        "per:parents",
-                        "per:children",
-                        "per:siblings",
-                        "per:spouse",
-                        "per:other_family",
-                        "per:colleagues",
-                        "per:product",
-                        "per:religion",
-                        "per:title",
-                    ]
-                ),
-                "source": datasets.Value("string"),
-            },
-            description=textwrap.dedent(
-                """\
-            RE is a task to identify semantic relations between entity pairs in a text. The relation
-            is defined between an entity pair consisting of subject entity and object entity.
-            The goal is then to pick an appropriate relationship between these two entities.
-            """
-            ),
-            data_url=_DATA_URLs["re"],
-            url=_DESCRIPTION_URLs["re"],
-            file_map={
-                "train": "klue-re-v1_train.json",
-                "dev": "klue-re-v1_dev.json",
-            },
-        ),
-        KlueConfig(
-            name="dp",
-            features={
-                "sentence": datasets.Value("string"),
-                "index": [datasets.Value("int32")],
-                "word_form": [datasets.Value("string")],
-                "lemma": [datasets.Value("string")],
-                "pos": [datasets.Value("string")],
-                "head": [datasets.Value("int32")],
-                "deprel": [datasets.Value("string")],
-            },
-            description=textwrap.dedent(
-                """\
-            DP is a task that aims at finding relational information among words.
-            The goal is to predict a graph structure and a dependency label of an input sentence
-            based on the dependency grammar.
-            """
-            ),
-            data_url=_DATA_URLs["dp"],
-            url=_DESCRIPTION_URLs["dp"],
-            file_map={
-                "train": "klue-dp-v1_train.tsv",
-                "dev": "klue-dp-v1_dev.tsv",
-            },
-        ),
-        KlueConfig(
-            name="mrc",
-            features={
-                "title": datasets.Value("string"),
-                "context": datasets.Value("string"),
-                "news_category": datasets.Value("string"),
-                "source": datasets.Value("string"),
-                "guid": datasets.Value("string"),
-                "is_impossible": datasets.Value("bool"),
-                "question_type": datasets.Value("int32"),
-                "question": datasets.Value("string"),
-                "answers": datasets.features.Sequence(
-                    {
-                        "answer_start": datasets.Value("int32"),
-                        "text": datasets.Value("string"),
-                    },
-                ),
-            },
-            description=textwrap.dedent(
-                """\
-            MRC is a task of evaluating model that can answer a question about a given text
-            passage. Specifically, we formulate the task as a span prediction task, where the
-            answer is a text segment (coined as spans) in the passage.
-            """
-            ),
-            data_url=_DATA_URLs["mrc"],
-            url=_DESCRIPTION_URLs["mrc"],
-            file_map={
-                "train": "klue-mrc-v1_train.json",
-                "dev": "klue-mrc-v1_dev.json",
-            },
-        ),
-        KlueConfig(
-            name="wos",
-            features={
-                "guid": datasets.Value("string"),
-                "domains": [datasets.Value("string")],
-                "dialogue": [
-                    {
-                        "role": datasets.Value("string"),
-                        "text": datasets.Value("string"),
-                        "state": [datasets.Value("string")],
-                    }
-                ],
-            },
-            description=textwrap.dedent(
-                """\
-            DST is a task to predict slot and value pairs (dialogue states) from a task-oriented
-            dialogue. The potential pairs are predefined by a given task schema and knowledge
-            base (KB).
-            """
-            ),
-            data_url=_DATA_URLs["wos"],
-            url=_DESCRIPTION_URLs["wos"],
-            file_map={
-                "train": "wos-v1_train.json",
-                "dev": "wos-v1_dev.json",
-            },
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_KLUE_DESCRIPTION,
-            features=datasets.Features(self.config.features),
-            homepage=self.config.url,
-            citation=_KLUE_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        archive = dl_manager.download(self.config.data_url)
-        dir_name = self.config.data_url.split("/")[-1].replace(".tar.gz", "")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "data_file": dir_name + "/" + self.config.file_map["train"],
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "data_file": dir_name + "/" + self.config.file_map["dev"],
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, data_file, files):
-        if self.config.name in ["ynat", "sts", "re"]:
-            for path, f in files:
-                if path == data_file:
-                    f = json.load(f)
-                    for id_, row in enumerate(f):
-                        features = {key: row[key] for key in row if key in self.config.features}
-                        yield id_, features
-                    break
-
-        if self.config.name == "nli":
-            for path, f in files:
-                if path == data_file:
-                    f = json.load(f)
-                    for id_, row in enumerate(f):
-                        # In train file, "source" is written as "genre"
-                        features = {
-                            "guid": row["guid"],
-                            "source": row["source"] if "source" in row else row["genre"],
-                            "premise": row["premise"],
-                            "hypothesis": row["hypothesis"],
-                            "label": row["gold_label"],
-                        }
-                        yield id_, features
-                    break
-
-        if self.config.name == "ner":
-            for path, f in files:
-                if path == data_file:
-                    f = (line.decode("utf-8") for line in f)
-                    reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-                    for _ in range(5):  # skip headers
-                        next(reader)
-                    id_ = -1
-                    for row in reader:
-                        if row:
-                            if row[0].startswith("##"):
-                                id_ += 1
-                                tokens, ner_tags = [], []
-                                sentence = row[1]
-                            else:
-                                tokens.append(row[0])
-                                ner_tags.append(row[1])
-                        else:  # new line
-                            assert len(tokens) == len(ner_tags)
-                            yield id_, {"sentence": sentence, "tokens": tokens, "ner_tags": ner_tags}
-                    break
-
-        if self.config.name == "dp":
-            for path, f in files:
-                if path == data_file:
-                    f = (line.decode("utf-8") for line in f)
-                    reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-                    for _ in range(5):  # skip headers
-                        next(reader)
-                    id_ = -1
-                    for row in reader:
-                        if row:
-                            if row[0].startswith("##"):
-                                id_ += 1
-                                index = []
-                                word_form = []
-                                lemma = []
-                                pos = []
-                                head = []
-                                deprel = []
-                                sentence = row[1]
-                            else:
-                                index.append(row[0])
-                                word_form.append(row[1])
-                                lemma.append(row[2])
-                                pos.append(row[3])
-                                head.append(row[4])
-                                deprel.append(row[5])
-                        else:  # new line
-                            assert len(index) == len(word_form) == len(lemma) == len(pos) == len(head) == len(deprel)
-                            yield id_, {
-                                "sentence": sentence,
-                                "index": index,
-                                "word_form": word_form,
-                                "lemma": lemma,
-                                "pos": pos,
-                                "head": head,
-                                "deprel": deprel,
-                            }
-                    break
-
-        if self.config.name == "mrc":
-            for path, f in files:
-                if path == data_file:
-                    f = json.load(f)
-                    id_ = -1
-                    for example in f["data"]:
-                        title = example.get("title", "")
-                        news_category = example.get("news_category", "")
-                        source = example["source"]
-                        for paragraph in example["paragraphs"]:
-                            context = paragraph["context"].strip()
-                            for qa in paragraph["qas"]:
-                                guid = qa["guid"]
-                                question_type = qa["question_type"]
-                                is_impossible = qa["is_impossible"]
-                                question = qa["question"].strip()
-
-                                if "plausible_answers" in qa:
-                                    qa["answers"].extend(qa["plausible_answers"])
-                                answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                                answers = [answer["text"].strip() for answer in qa["answers"]]
-                                id_ += 1
-
-                                yield id_, {
-                                    "guid": guid,
-                                    "title": title,
-                                    "context": context,
-                                    "news_category": news_category,
-                                    "source": source,
-                                    "question_type": question_type,
-                                    "is_impossible": is_impossible,
-                                    "question": question,
-                                    "answers": {
-                                        "answer_start": answer_starts,
-                                        "text": answers,
-                                    },
-                                }
-                    break
-
-        if self.config.name == "wos":
-            for path, f in files:
-                if path == data_file:
-                    f = json.load(f)
-                    for id_, row in enumerate(f):
-                        guid = row["guid"]
-                        domains = row["domains"]
-                        dialogue = row["dialogue"]
-                        for utterance in dialogue:
-                            if "state" not in utterance:
-                                utterance["state"] = []
-                        yield id_, {"guid": guid, "domains": domains, "dialogue": dialogue}
-                    break

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