Attend the Women in ML Symposium on December 7 Register now

xglue

References:

ner

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/ner')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.de' 3007
'test.en' 3454
'test.es' 1523
'test.nl' 5202
'train' 14042
'validation.de' 2874
'validation.en' 3252
'validation.es' 1923
'validation.nl' 2895
  • Features:
{
    "words": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "ner": {
        "feature": {
            "num_classes": 9,
            "names": [
                "O",
                "B-PER",
                "I-PER",
                "B-ORG",
                "I-ORG",
                "B-LOC",
                "I-LOC",
                "B-MISC",
                "I-MISC"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

pos

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/pos')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.ar' 679
'test.bg' 1115
'test.de' 976
'test.el' 455
'test.en' 2076
'test.es' 425
'test.fr' 415
'test.hi' 1683
'test.it' 481
'test.nl' 595
'test.pl' 2214
'test.ru' 600
'test.th' 497
'test.tr' 982
'test.ur' 534
'test.vi' 799
'test.zh' 499
'train' 25376
'validation.ar' 908
'validation.bg' 1114
'validation.de' 798
'validation.el' 402
'validation.en' 2001
'validation.es' 1399
'validation.fr' 1475
'validation.hi' 1658
'validation.it' 563
'validation.nl' 717
'validation.pl' 2214
'validation.ru' 578
'validation.th' 497
'validation.tr' 987
'validation.ur' 551
'validation.vi' 799
'validation.zh' 499
  • Features:
{
    "words": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "pos": {
        "feature": {
            "num_classes": 17,
            "names": [
                "ADJ",
                "ADP",
                "ADV",
                "AUX",
                "CCONJ",
                "DET",
                "INTJ",
                "NOUN",
                "NUM",
                "PART",
                "PRON",
                "PROPN",
                "PUNCT",
                "SCONJ",
                "SYM",
                "VERB",
                "X"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

mlqa

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/mlqa')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.ar' 5335
'test.de' 4517
'test.en' 11590
'test.es' 5253
'test.hi' 4918
'test.vi' 5495
'test.zh' 5137
'train' 87599
'validation.ar' 517
'validation.de' 512
'validation.en' 1148
'validation.es' 500
'validation.hi' 507
'validation.vi' 511
'validation.zh' 504
  • Features:
{
    "context": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answers": {
        "feature": {
            "answer_start": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

nc

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/nc')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.de' 10000
'test.en' 10000
'test.es' 10000
'test.fr' 10000
'test.ru' 10000
'train' 100000
'validation.de' 10000
'validation.en' 10000
'validation.es' 10000
'validation.fr' 10000
'validation.ru' 10000
  • Features:
{
    "news_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "news_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "news_category": {
        "num_classes": 10,
        "names": [
            "foodanddrink",
            "sports",
            "travel",
            "finance",
            "lifestyle",
            "news",
            "entertainment",
            "health",
            "video",
            "autos"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

xnli

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/xnli')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.ar' 5010
'test.bg' 5010
'test.de' 5010
'test.el' 5010
'test.en' 5010
'test.es' 5010
'test.fr' 5010
'test.hi' 5010
'test.ru' 5010
'test.sw' 5010
'test.th' 5010
'test.tr' 5010
'test.ur' 5010
'test.vi' 5010
'test.zh' 5010
'train' 392702
'validation.ar' 2490
'validation.bg' 2490
'validation.de' 2490
'validation.el' 2490
'validation.en' 2490
'validation.es' 2490
'validation.fr' 2490
'validation.hi' 2490
'validation.ru' 2490
'validation.sw' 2490
'validation.th' 2490
'validation.tr' 2490
'validation.ur' 2490
'validation.vi' 2490
'validation.zh' 2490
  • Features:
{
    "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"
    }
}

paws-x

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/paws-x')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.de' 2000
'test.en' 2000
'test.es' 2000
'test.fr' 2000
'train' 49401
'validation.de' 2000
'validation.en' 2000
'validation.es' 2000
'validation.fr' 2000
  • Features:
{
    "sentence1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "sentence2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 2,
        "names": [
            "different",
            "same"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

qadsm

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/qadsm')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.de' 10000
'test.en' 10000
'test.fr' 10000
'train' 100000
'validation.de' 10000
'validation.en' 10000
'validation.fr' 10000
  • Features:
{
    "query": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "ad_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "ad_description": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "relevance_label": {
        "num_classes": 2,
        "names": [
            "Bad",
            "Good"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

wpr

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/wpr')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.de' 9997
'test.en' 10004
'test.es' 10006
'test.fr' 10020
'test.it' 10001
'test.pt' 10015
'test.zh' 9999
'train' 99997
'validation.de' 10004
'validation.en' 10008
'validation.es' 10004
'validation.fr' 10005
'validation.it' 10003
'validation.pt' 10001
'validation.zh' 10002
  • Features:
{
    "query": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "web_page_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "web_page_snippet": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "relavance_label": {
        "num_classes": 5,
        "names": [
            "Bad",
            "Fair",
            "Good",
            "Excellent",
            "Perfect"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

qam

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/qam')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.de' 10000
'test.en' 10000
'test.fr' 10000
'train' 100000
'validation.de' 10000
'validation.en' 10000
'validation.fr' 10000
  • Features:
{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 2,
        "names": [
            "False",
            "True"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

qg

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/qg')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.de' 10000
'test.en' 10000
'test.es' 10000
'test.fr' 10000
'test.it' 10000
'test.pt' 10000
'train' 100000
'validation.de' 10000
'validation.en' 10000
'validation.es' 10000
'validation.fr' 10000
'validation.it' 10000
'validation.pt' 10000
  • Features:
{
    "answer_passage": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

ntg

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:xglue/ntg')
  • Description:
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)

For more information, please take a look at https://microsoft.github.io/XGLUE/.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test.de' 10000
'test.en' 10000
'test.es' 10000
'test.fr' 10000
'test.ru' 10000
'train' 300000
'validation.de' 10000
'validation.en' 10000
'validation.es' 10000
'validation.fr' 10000
'validation.ru' 10000
  • Features:
{
    "news_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "news_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}