para_pat

ds = tfds.load('huggingface:para_pat/el-en')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "el",
            "en"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/cs-en')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "cs",
            "en"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-hu')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "en",
            "hu"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-ro')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "en",
            "ro"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-sk')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "en",
            "sk"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-uk')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "en",
            "uk"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/es-fr')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "es",
            "fr"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/fr-ru')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "fr",
            "ru"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/de-fr')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "translation": {
        "languages": [
            "de",
            "fr"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-ja')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "translation": {
        "languages": [
            "en",
            "ja"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-es')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "translation": {
        "languages": [
            "en",
            "es"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-fr')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "translation": {
        "languages": [
            "en",
            "fr"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/de-en')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "translation": {
        "languages": [
            "de",
            "en"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-ko')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "translation": {
        "languages": [
            "en",
            "ko"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/fr-ja')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "translation": {
        "languages": [
            "fr",
            "ja"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-zh')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "translation": {
        "languages": [
            "en",
            "zh"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-ru')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "translation": {
        "languages": [
            "en",
            "ru"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/fr-ko')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "fr",
            "ko"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/ru-uk')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "ru",
            "uk"
        ],
        "id": null,
        "_type": "Translation"
    }
}

ds = tfds.load('huggingface:para_pat/en-pt')

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

We demonstrate the capabilities of our corpus by training Neural Machine Translation
(NMT) models for the main 9 language pairs, with a total of 18 models.

{
    "index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "family_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "translation": {
        "languages": [
            "en",
            "pt"
        ],
        "id": null,
        "_type": "Translation"
    }
}