Update parquet files

README.md

@@ -1,279 +0,0 @@
----
-pretty_name: Emotion
-annotations_creators:
-- machine-generated
-language_creators:
-- machine-generated
-language:
-- en
-license:
-- other
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- text-classification
-task_ids:
-- multi-class-classification
-paperswithcode_id: emotion
-train-eval-index:
-- config: default
-  task: text-classification
-  task_id: multi_class_classification
-  splits:
-    train_split: train
-    eval_split: test
-  col_mapping:
-    text: text
-    label: target
-  metrics:
-  - type: accuracy
-    name: Accuracy
-  - type: f1
-    name: F1 macro
-    args:
-      average: macro
-  - type: f1
-    name: F1 micro
-    args:
-      average: micro
-  - type: f1
-    name: F1 weighted
-    args:
-      average: weighted
-  - type: precision
-    name: Precision macro
-    args:
-      average: macro
-  - type: precision
-    name: Precision micro
-    args:
-      average: micro
-  - type: precision
-    name: Precision weighted
-    args:
-      average: weighted
-  - type: recall
-    name: Recall macro
-    args:
-      average: macro
-  - type: recall
-    name: Recall micro
-    args:
-      average: micro
-  - type: recall
-    name: Recall weighted
-    args:
-      average: weighted
-tags:
-- emotion-classification
-dataset_info:
-- config_name: split
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': sadness
-          '1': joy
-          '2': love
-          '3': anger
-          '4': fear
-          '5': surprise
-  splits:
-  - name: train
-    num_bytes: 1741597
-    num_examples: 16000
-  - name: validation
-    num_bytes: 214703
-    num_examples: 2000
-  - name: test
-    num_bytes: 217181
-    num_examples: 2000
-  download_size: 740883
-  dataset_size: 2173481
-- config_name: unsplit
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': sadness
-          '1': joy
-          '2': love
-          '3': anger
-          '4': fear
-          '5': surprise
-  splits:
-  - name: train
-    num_bytes: 45445685
-    num_examples: 416809
-  download_size: 15388281
-  dataset_size: 45445685
-duplicated_from: emotion
----
-
-# Dataset Card for "emotion"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 3.95 MB
-- **Size of the generated dataset:** 4.16 MB
-- **Total amount of disk used:** 8.11 MB
-
-### Dataset Summary
-
-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.
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-An example looks as follows.
-```
-{
-  "text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
-  "label": 0
-}
-```
-
-### Data Fields
-
-The data fields are:
-- `text`: a `string` feature.
-- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
-
-### Data Splits
-
-The dataset has 2 configurations:
-- split: with a total of 20_000 examples split into train, validation and split
-- unsplit: with a total of 416_809 examples in a single train split
-
-| name    |  train | validation | test |
-|---------|-------:|-----------:|-----:|
-| split   |  16000 |       2000 | 2000 |
-| unsplit | 416809 |        n/a |  n/a |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-The dataset should be used for educational and research purposes only.
-
-### Citation Information
-
- If you use this dataset, please cite:
-```
-@inproceedings{saravia-etal-2018-carer,
-    title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
-    author = "Saravia, Elvis  and
-      Liu, Hsien-Chi Toby  and
-      Huang, Yen-Hao  and
-      Wu, Junlin  and
-      Chen, Yi-Shin",
-    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
-    month = oct # "-" # nov,
-    year = "2018",
-    address = "Brussels, Belgium",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/D18-1404",
-    doi = "10.18653/v1/D18-1404",
-    pages = "3687--3697",
-    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.",
-}
-```
-
-### Contributions
-
-Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"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}}

emotion.py

@@ -1,88 +0,0 @@
-import json
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """\
-@inproceedings{saravia-etal-2018-carer,
-    title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
-    author = "Saravia, Elvis  and
-      Liu, Hsien-Chi Toby  and
-      Huang, Yen-Hao  and
-      Wu, Junlin  and
-      Chen, Yi-Shin",
-    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
-    month = oct # "-" # nov,
-    year = "2018",
-    address = "Brussels, Belgium",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/D18-1404",
-    doi = "10.18653/v1/D18-1404",
-    pages = "3687--3697",
-    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.",
-}
-"""
-
-_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.
-"""
-
-_HOMEPAGE = "https://github.com/dair-ai/emotion_dataset"
-
-_LICENSE = "The dataset should be used for educational and research purposes only"
-
-_URLS = {
-    "split": {
-        "train": "data/train.jsonl.gz",
-        "validation": "data/validation.jsonl.gz",
-        "test": "data/test.jsonl.gz",
-    },
-    "unsplit": {
-        "train": "data/data.jsonl.gz",
-    },
-}
-
-
-class Emotion(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="split", version=VERSION, description="Dataset split in train, validation and test"
-        ),
-        datasets.BuilderConfig(name="unsplit", version=VERSION, description="Unsplit dataset"),
-    ]
-    DEFAULT_CONFIG_NAME = "split"
-
-    def _info(self):
-        class_names = ["sadness", "joy", "love", "anger", "fear", "surprise"]
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {"text": datasets.Value("string"), "label": datasets.ClassLabel(names=class_names)}
-            ),
-            supervised_keys=("text", "label"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        paths = dl_manager.download_and_extract(_URLS[self.config.name])
-        if self.config.name == "split":
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": paths["train"]}),
-                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": paths["validation"]}),
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": paths["test"]}),
-            ]
-        else:
-            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": paths["train"]})]
-
-    def _generate_examples(self, filepath):
-        """Generate examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                example = json.loads(line)
-                yield idx, example

data/validation.jsonl.gz → split/emotion-test.parquet

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data/train.jsonl.gz → split/emotion-train.parquet

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data/data.jsonl.gz → unsplit/emotion-train.parquet

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