Initial project setup.

.gitattributes

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 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+data/data.jsonl.gz filter=lfs diff=lfs merge=lfs -text

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dataset_infos.json

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+{"description": "\nEmotions is a dataset of English Twitter messages with six basic emotions:\nanger, fear, joy, love, sadness, and surprise. For more detailed information\nplease 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://huggingface.co/datasets/jeffnyman/emotions", "license": "cc-by-sa-4.0", "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": "emotions", "dataset_name": "emotions", "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": "emotions"}, "validation": {"name": "validation", "num_bytes": 214695, "num_examples": 2000, "dataset_name": "emotions"}, "test": {"name": "test", "num_bytes": 217173, "num_examples": 2000, "dataset_name": "emotions"}}, "download_checksums": {"data/train.jsonl.gz": {"num_bytes": 591930, "checksum": null}, "data/validation.jsonl.gz": {"num_bytes": 74018, "checksum": null}, "data/test.jsonl.gz": {"num_bytes": 74935, "checksum": null}}, "download_size": 740883, "dataset_size": 2173401, "size_in_bytes": 2914284}

emotions.py

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+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://huggingface.co/datasets/jeffnyman/emotions"
+
+_LICENSE = "cc-by-sa-4.0"
+
+_URLS = {
+    "split": {
+        "train": "data/train.jsonl.gz",
+        "validation": "data/validation.jsonl.gz",
+        "test": "data/test.jsonl.gz",
+    },
+    "unsplit": {
+        "train": "data/data.jsonl.gz",
+    },
+}
+
+
+class Emotions(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):
+        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):
+        with open(filepath, encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                example = json.loads(line)
+
+                yield idx, example