dataset_infos.json

{
    "default": {
        "description": "Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.\n",
        "citation": "@inproceedings{saravia-etal-2018-carer,\n    title = \"{CARER}: Contextualized Affect Representations for Emotion Recognition\",\n    author = \"Saravia, Elvis  and\n      Liu, Hsien-Chi Toby  and\n      Huang, Yen-Hao  and\n      Wu, Junlin  and\n      Chen, Yi-Shin\",\n    booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\",\n    month = oct # \"-\" # nov,\n    year = \"2018\",\n    address = \"Brussels, Belgium\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D18-1404\",\n    doi = \"10.18653/v1/D18-1404\",\n    pages = \"3687--3697\",\n    abstract = \"Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.\",\n}\n",
        "homepage": "https://github.com/dair-ai/emotion_dataset",
        "license": "",
        "features": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "label": {
                "num_classes": 6,
                "names": [
                    "sadness",
                    "joy",
                    "love",
                    "anger",
                    "fear",
                    "surprise"
                ],
                "names_file": null,
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "post_processed": null,
        "supervised_keys": {
            "input": "text",
            "output": "label"
        },
        "task_templates": [
            {
                "task": "text-classification",
                "text_column": "text",
                "label_column": "label",
                "labels": [
                    "anger",
                    "fear",
                    "joy",
                    "love",
                    "sadness",
                    "surprise"
                ]
            }
        ],
        "builder_name": "emotion",
        "config_name": "default",
        "version": {
            "version_str": "0.0.0",
            "description": null,
            "major": 0,
            "minor": 0,
            "patch": 0
        },
        "splits": {
            "train": {
                "name": "train",
                "num_bytes": 1741541,
                "num_examples": 16000,
                "dataset_name": "emotion"
            },
            "validation": {
                "name": "validation",
                "num_bytes": 214699,
                "num_examples": 2000,
                "dataset_name": "emotion"
            },
            "test": {
                "name": "test",
                "num_bytes": 217177,
                "num_examples": 2000,
                "dataset_name": "emotion"
            }
        },
        "download_checksums": {
            "https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1": {
                "num_bytes": 1658616,
                "checksum": "3ab03d945a6cb783d818ccd06dafd52d2ed8b4f62f0f85a09d7d11870865b190"
            },
            "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1": {
                "num_bytes": 204240,
                "checksum": "34faaa31962fe63cdf5dbf6c132ef8ab166c640254ab991af78f3aea375e79ef"
            },
            "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1": {
                "num_bytes": 206760,
                "checksum": "60f531690d20127339e7f054edc299a82c627b5ec0dd5d552d53d544e0cfcc17"
            }
        },
        "download_size": 2069616,
        "post_processing_size": null,
        "dataset_size": 2173417,
        "size_in_bytes": 4243033
    },
    "split": {
        "description": "Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.\n",
        "citation": "@inproceedings{saravia-etal-2018-carer,\n    title = \"{CARER}: Contextualized Affect Representations for Emotion Recognition\",\n    author = \"Saravia, Elvis  and\n      Liu, Hsien-Chi Toby  and\n      Huang, Yen-Hao  and\n      Wu, Junlin  and\n      Chen, Yi-Shin\",\n    booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\",\n    month = oct # \"-\" # nov,\n    year = \"2018\",\n    address = \"Brussels, Belgium\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D18-1404\",\n    doi = \"10.18653/v1/D18-1404\",\n    pages = \"3687--3697\",\n    abstract = \"Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.\",\n}\n",
        "homepage": "https://github.com/dair-ai/emotion_dataset",
        "license": "The dataset should be used for educational and research purposes only",
        "features": {
            "text": {
                "dtype": "string",
                "_type": "Value"
            },
            "label": {
                "names": [
                    "sadness",
                    "joy",
                    "love",
                    "anger",
                    "fear",
                    "surprise"
                ],
                "_type": "ClassLabel"
            }
        },
        "supervised_keys": {
            "input": "text",
            "output": "label"
        },
        "task_templates": [
            {
                "task": "text-classification",
                "label_column": "label"
            }
        ],
        "builder_name": "parquet",
        "dataset_name": "emotion",
        "config_name": "split",
        "version": {
            "version_str": "1.0.0",
            "major": 1,
            "minor": 0,
            "patch": 0
        },
        "splits": {
            "train": {
                "name": "train",
                "num_bytes": 1741533,
                "num_examples": 16000,
                "dataset_name": null
            },
            "validation": {
                "name": "validation",
                "num_bytes": 214695,
                "num_examples": 2000,
                "dataset_name": null
            },
            "test": {
                "name": "test",
                "num_bytes": 217173,
                "num_examples": 2000,
                "dataset_name": null
            }
        },
        "download_size": 1287193,
        "dataset_size": 2173401,
        "size_in_bytes": 3460594
    }
}