Datasets:

Multilinguality:
multilingual
Size Categories:
1K<n<10K
Annotations Creators:
crowdsourced
ArXiv:
Tags:
License:
albertvillanova HF staff commited on
Commit
d25ae7c
1 Parent(s): 5e8b025

Add X-CSQA-zh data files

Browse files
README.md CHANGED
@@ -925,13 +925,13 @@ 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: 197746
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  num_examples: 1074
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  - name: validation
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- num_bytes: 188555
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  num_examples: 1000
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- download_size: 7519903
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- dataset_size: 386301
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  configs:
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  - config_name: X-CSQA-en
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  data_files:
@@ -939,6 +939,12 @@ configs:
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  path: X-CSQA-en/test-*
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  - split: validation
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  path: X-CSQA-en/validation-*
 
 
 
 
 
 
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  ---
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  # Dataset Card for X-CSR
 
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  dtype: string
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  splits:
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  - name: test
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+ num_bytes: 197444
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  num_examples: 1074
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  - name: validation
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+ num_bytes: 188273
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  num_examples: 1000
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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-CSQA-en
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  data_files:
 
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  path: X-CSQA-en/test-*
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  - split: validation
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  path: X-CSQA-en/validation-*
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+ - config_name: X-CSQA-zh
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+ data_files:
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+ - split: test
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+ path: X-CSQA-zh/test-*
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+ - split: validation
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+ path: X-CSQA-zh/validation-*
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  ---
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  # Dataset Card for X-CSR
X-CSQA-zh/test-00000-of-00001.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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X-CSQA-zh/validation-00000-of-00001.parquet ADDED
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dataset_infos.json CHANGED
@@ -73,48 +73,38 @@
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  "features": {
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  "id": {
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  "_type": "Value"
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  "lang": {
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  "builder_name": "xcsr",
 
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  "config_name": "X-CSQA-zh",
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  "version": {
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  "version_str": "1.1.0",
@@ -126,27 +116,20 @@
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  "splits": {
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  "test": {
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  "name": "test",
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- "num_bytes": 197746,
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  "num_examples": 1074,
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- "dataset_name": "xcsr"
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  },
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  "validation": {
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  "name": "validation",
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- "num_bytes": 188555,
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  "num_examples": 1000,
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- "dataset_name": "xcsr"
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  },
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  "X-CSQA-de": {
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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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  "features": {
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  "id": {
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  "dtype": "string",
 
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  "_type": "Value"
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  "answerKey": {
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  "dtype": "string",
 
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  "_type": "Value"
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  }
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  },
 
 
 
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  "builder_name": "xcsr",
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+ "dataset_name": "xcsr",
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  "config_name": "X-CSQA-zh",
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  "version": {
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  "version_str": "1.1.0",
 
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  "splits": {
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  "name": "test",
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  "validation": {
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  "name": "validation",
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  "num_examples": 1000,
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  },
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  "X-CSQA-de": {
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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",