Convert dataset to Parquet

README.md

@@ -38,16 +38,16 @@ dataset_info:
           '5': surprise
   splits:
   - name: train
-    num_bytes: 1741597
+    num_bytes: 1741533
     num_examples: 16000
   - name: validation
-    num_bytes: 214703
+    num_bytes: 214695
     num_examples: 2000
   - name: test
-    num_bytes: 217181
+    num_bytes: 217173
     num_examples: 2000
-  download_size: 740883
-  dataset_size: 2173481
+  download_size: 1287193
+  dataset_size: 2173401
 - config_name: unsplit
   features:
   - name: text
@@ -68,6 +68,15 @@ dataset_info:
     num_examples: 416809
   download_size: 15388281
   dataset_size: 45445685
+configs:
+- config_name: split
+  data_files:
+  - split: train
+    path: split/train-*
+  - split: validation
+    path: split/validation-*
+  - split: test
+    path: split/test-*
 train-eval-index:
 - config: default
   task: text-classification

dataset_infos.json

@@ -1 +1,161 @@
-{"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}}
+{
+    "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
+    }
+}

split/test-00000-of-00001.parquet

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+size 128987

split/train-00000-of-00001.parquet

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+size 1030740

split/validation-00000-of-00001.parquet

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