wiki_dpr

References:

• Code
• Huggingface

psgs_w100.nq.exact

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:wiki_dpr/psgs_w100.nq.exact')

• Description:

This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
It contains 21M passages from wikipedia along with their DPR embeddings.
The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.

• License: No known license
• Version: 0.0.0
• Splits:

Split	Examples
'train'	21015300

• Features:

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "embeddings": {
        "feature": {
            "dtype": "float32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

psgs_w100.nq.compressed

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:wiki_dpr/psgs_w100.nq.compressed')

• Description:

This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
It contains 21M passages from wikipedia along with their DPR embeddings.
The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.

• License: No known license
• Version: 0.0.0
• Splits:

Split	Examples
'train'	21015300

• Features:

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "embeddings": {
        "feature": {
            "dtype": "float32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

psgs_w100.nq.no_index

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:wiki_dpr/psgs_w100.nq.no_index')

• Description:

This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
It contains 21M passages from wikipedia along with their DPR embeddings.
The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.

• License: No known license
• Version: 0.0.0
• Splits:

Split	Examples
'train'	21015300

• Features:

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "embeddings": {
        "feature": {
            "dtype": "float32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

psgs_w100.multiset.exact

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:wiki_dpr/psgs_w100.multiset.exact')

• Description:

This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
It contains 21M passages from wikipedia along with their DPR embeddings.
The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.

• License: No known license
• Version: 0.0.0
• Splits:

Split	Examples
'train'	21015300

• Features:

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "embeddings": {
        "feature": {
            "dtype": "float32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

psgs_w100.multiset.compressed

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:wiki_dpr/psgs_w100.multiset.compressed')

• Description:

This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
It contains 21M passages from wikipedia along with their DPR embeddings.
The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.

• License: No known license
• Version: 0.0.0
• Splits:

Split	Examples
'train'	21015300

• Features:

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "embeddings": {
        "feature": {
            "dtype": "float32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

psgs_w100.multiset.no_index

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:wiki_dpr/psgs_w100.multiset.no_index')

• Description:

This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
It contains 21M passages from wikipedia along with their DPR embeddings.
The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.

• License: No known license
• Version: 0.0.0
• Splits:

Split	Examples
'train'	21015300

• Features:

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "embeddings": {
        "feature": {
            "dtype": "float32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}