Add X-CSQA-de data files

README.md

@@ -547,13 +547,13 @@ dataset_info:
     dtype: string
   splits:
   - name: test
-    num_bytes: 234472
+    num_bytes: 234170
     num_examples: 1074
   - name: validation
-    num_bytes: 223122
+    num_bytes: 222840
     num_examples: 1000
-  download_size: 7519903
-  dataset_size: 457594
+  download_size: 242762
+  dataset_size: 457010
 - config_name: X-CSQA-en
   features:
   - name: id
@@ -933,6 +933,12 @@ dataset_info:
   download_size: 207379
   dataset_size: 385717
 configs:
+- config_name: X-CSQA-de
+  data_files:
+  - split: test
+    path: X-CSQA-de/test-*
+  - split: validation
+    path: X-CSQA-de/validation-*
 - config_name: X-CSQA-en
   data_files:
   - split: test

X-CSQA-de/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f908018b4a881753b8b43e2a087df7a5429742fadcbe9e37c9db62743cebc2f
+size 125856

X-CSQA-de/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:81bc3acecabb6ad76dd74c570c84c442224ee73d9436b185ffb6a2a467dea948
+size 116906

dataset_infos.json

@@ -139,48 +139,38 @@
         "features": {
             "id": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "lang": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "question": {
                 "stem": {
                     "dtype": "string",
-                    "id": null,
                     "_type": "Value"
                 },
                 "choices": {
                     "feature": {
                         "label": {
                             "dtype": "string",
-                            "id": null,
                             "_type": "Value"
                         },
                         "text": {
                             "dtype": "string",
-                            "id": null,
                             "_type": "Value"
                         }
                     },
-                    "length": -1,
-                    "id": null,
                     "_type": "Sequence"
                 }
             },
             "answerKey": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             }
         },
-        "post_processed": null,
-        "supervised_keys": null,
-        "task_templates": null,
         "builder_name": "xcsr",
+        "dataset_name": "xcsr",
         "config_name": "X-CSQA-de",
         "version": {
             "version_str": "1.1.0",
@@ -192,27 +182,20 @@
         "splits": {
             "test": {
                 "name": "test",
-                "num_bytes": 234472,
+                "num_bytes": 234170,
                 "num_examples": 1074,
-                "dataset_name": "xcsr"
+                "dataset_name": null
             },
             "validation": {
                 "name": "validation",
-                "num_bytes": 223122,
+                "num_bytes": 222840,
                 "num_examples": 1000,
-                "dataset_name": "xcsr"
-            }
-        },
-        "download_checksums": {
-            "https://inklab.usc.edu/XCSR/xcsr_datasets.zip": {
-                "num_bytes": 7519903,
-                "checksum": "c45b29ece740643252d5402e76be1e33f96f9d6910053f79e80d39887f10c85e"
+                "dataset_name": null
             }
         },
-        "download_size": 7519903,
-        "post_processing_size": null,
-        "dataset_size": 457594,
-        "size_in_bytes": 7977497
+        "download_size": 242762,
+        "dataset_size": 457010,
+        "size_in_bytes": 699772
     },
     "X-CSQA-es": {
         "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",