xed_en_fi

参考文献:

• コード
• ハグフェイス

en_annotated

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:xed_en_fi/en_annotated')

• 説明：

A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.

• ライセンス: ライセンス: クリエイティブ コモンズ 表示 4.0 国際ライセンス (CC-BY)
• バージョン: 1.1.0
• 分割:

• 特徴：

{
    "sentence": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 9,
            "names": [
                "neutral",
                "anger",
                "anticipation",
                "disgust",
                "fear",
                "joy",
                "sadness",
                "surprise",
                "trust"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

en_neutral

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:xed_en_fi/en_neutral')

• 説明：

A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.

• ライセンス: ライセンス: クリエイティブ コモンズ 表示 4.0 国際ライセンス (CC-BY)
• バージョン: 1.1.0
• 分割:

• 特徴：

{
    "sentence": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "num_classes": 9,
        "names": [
            "neutral",
            "anger",
            "anticipation",
            "disgust",
            "fear",
            "joy",
            "sadness",
            "surprise",
            "trust"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

fi_annotated

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:xed_en_fi/fi_annotated')

• 説明：

A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.

• ライセンス: ライセンス: クリエイティブ コモンズ 表示 4.0 国際ライセンス (CC-BY)
• バージョン: 1.1.0
• 分割:

• 特徴：

{
    "sentence": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 9,
            "names": [
                "neutral",
                "anger",
                "anticipation",
                "disgust",
                "fear",
                "joy",
                "sadness",
                "surprise",
                "trust"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

fi_neutral

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:xed_en_fi/fi_neutral')

• 説明：

A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.

• ライセンス: ライセンス: クリエイティブ コモンズ 表示 4.0 国際ライセンス (CC-BY)
• バージョン: 1.1.0
• 分割:

• 特徴：

{
    "sentence": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "num_classes": 9,
        "names": [
            "neutral",
            "anger",
            "anticipation",
            "disgust",
            "fear",
            "joy",
            "sadness",
            "surprise",
            "trust"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}