Datasets:
Tasks:
Question Answering
Sub-tasks:
multiple-choice-qa
Multilinguality:
multilingual
Size Categories:
1K<n<10K
Annotations Creators:
crowdsourced
ArXiv:
Tags:
License:
Commit
•
4595814
1
Parent(s):
8a0429e
Add X-CODAH-de data files
Browse files- README.md +10 -4
- X-CODAH-de/test-00000-of-00001.parquet +3 -0
- X-CODAH-de/validation-00000-of-00001.parquet +3 -0
- dataset_infos.json +8 -26
README.md
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@@ -87,13 +87,13 @@ dataset_info:
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dtype: string
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splits:
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- name: test
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num_bytes:
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num_examples: 1000
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- name: validation
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-
num_bytes:
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num_examples: 300
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download_size:
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dataset_size:
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- config_name: X-CODAH-en
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features:
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- name: id
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@@ -933,6 +933,12 @@ dataset_info:
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download_size: 207379
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dataset_size: 385717
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configs:
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- config_name: X-CODAH-en
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data_files:
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- split: test
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dtype: string
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splits:
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- name: test
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num_bytes: 476087
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num_examples: 1000
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- name: validation
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num_bytes: 138764
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num_examples: 300
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download_size: 259705
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dataset_size: 614851
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- config_name: X-CODAH-en
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features:
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- name: id
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download_size: 207379
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dataset_size: 385717
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configs:
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+
- config_name: X-CODAH-de
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+
data_files:
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- split: test
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path: X-CODAH-de/test-*
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+
- split: validation
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path: X-CODAH-de/validation-*
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- config_name: X-CODAH-en
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data_files:
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- split: test
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X-CODAH-de/test-00000-of-00001.parquet
ADDED
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version https://git-lfs.github.com/spec/v1
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size 198977
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X-CODAH-de/validation-00000-of-00001.parquet
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size 60728
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dataset_infos.json
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"X-CODAH-es": {
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"description": "To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. Specifically, we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR. As our goal is to evaluate different ML-LMs in a unified evaluation protocol for X-CSR, we argue that such translated examples, although might contain noise, can serve as a starting benchmark for us to obtain meaningful analysis, before more human-translated datasets will be available in the future.\n",
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"X-CODAH-es": {
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"description": "To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. Specifically, we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR. As our goal is to evaluate different ML-LMs in a unified evaluation protocol for X-CSR, we argue that such translated examples, although might contain noise, can serve as a starting benchmark for us to obtain meaningful analysis, before more human-translated datasets will be available in the future.\n",
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