xcsr

ds = tfds.load('huggingface:xcsr/X-CSQA-en')

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.

{
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}

ds = tfds.load('huggingface:xcsr/X-CSQA-zh')

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.

{
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        "_type": "Value"
    },
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}

ds = tfds.load('huggingface:xcsr/X-CSQA-de')

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.

{
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        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
            "stem": {
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}

ds = tfds.load('huggingface:xcsr/X-CSQA-es')

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.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
            "stem": {
                "dtype": "string",
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            },
            "choices": {
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    },
    "answerKey": {
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        "id": null,
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    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-fr')

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.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
            "stem": {
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            },
            "choices": {
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                },
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            }
        },
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        "_type": "Sequence"
    },
    "answerKey": {
        "dtype": "string",
        "id": null,
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    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-it')

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.

{
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        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
            "stem": {
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            },
            "choices": {
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        },
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    },
    "answerKey": {
        "dtype": "string",
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    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-jap')

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.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
            "stem": {
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            },
            "choices": {
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                    },
                    "text": {
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                    }
                },
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            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "answerKey": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-nl')

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.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
            "stem": {
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            },
            "choices": {
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                    },
                    "text": {
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                    }
                },
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            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "answerKey": {
        "dtype": "string",
        "id": null,
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    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-pl')

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.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
            "stem": {
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            },
            "choices": {
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                },
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        },
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    },
    "answerKey": {
        "dtype": "string",
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    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-pt')

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.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
            "stem": {
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            },
            "choices": {
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                },
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        },
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    },
    "answerKey": {
        "dtype": "string",
        "id": null,
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    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-ru')

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.

{
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        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
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            },
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    },
    "answerKey": {
        "dtype": "string",
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    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-ar')

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.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
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    },
    "question": {
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    "answerKey": {
        "dtype": "string",
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}

ds = tfds.load('huggingface:xcsr/X-CSQA-vi')

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.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
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    "answerKey": {
        "dtype": "string",
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    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-hi')

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.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
            "stem": {
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            },
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    },
    "answerKey": {
        "dtype": "string",
        "id": null,
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    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-sw')

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.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lang": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "feature": {
            "stem": {
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    "answerKey": {
        "dtype": "string",
        "id": null,
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    }
}

ds = tfds.load('huggingface:xcsr/X-CSQA-ur')

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.