Convert dataset to Parquet (#12)

- Convert dataset to Parquet (cd6ec7974b8bfd3b0180aaec4217ef430a15acce)
- Add 'unsplit' config data files (4b41f73c72b4ca6e24a641b551ac8f3b1090ed57)
- Delete loading script (0f2013aff0f120885b4892bddfe59faafa13a310)
- Delete data file (6d44c341e752cddbb545a0e4f4a41ecb09da5744)
- Delete legacy dataset_infos.json (23abdc09990f9cc02a701b82af668d46d8235b77)
- Delete data file (52bb8b6ef67e33229acd43b70874d585cbd45c95)
- Delete data file (b3ebb3b4fd4045c7d98862853d63c5b19582c254)
- Delete data file (106c232c0a63f3f06ae5d80d141114a00604e969)

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
@@ -64,10 +64,24 @@ dataset_info:
           '5': surprise
   splits:
   - name: train
-    num_bytes: 45445685
+    num_bytes: 45444017
     num_examples: 416809
-  download_size: 15388281
-  dataset_size: 45445685
+  download_size: 26888538
+  dataset_size: 45444017
+configs:
+- config_name: split
+  data_files:
+  - split: train
+    path: split/train-*
+  - split: validation
+    path: split/validation-*
+  - split: test
+    path: split/test-*
+  default: true
+- config_name: unsplit
+  data_files:
+  - split: train
+    path: unsplit/train-*
 train-eval-index:
 - config: default
   task: text-classification

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/test.jsonl.gz → split/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-oid sha256:4524468d0b7ee8eab07a088216cde7f9278f1c574669504a805ed172df6dad75
-size 74935
+oid sha256:6f8407fa1ca9c310f55781f082ed73812f6551e8dda2c61973123a121869245b
+size 128987

data/validation.jsonl.gz → split/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-oid sha256:50783464882f450f88e61ece964a200e492495eed1472ed520d013bbcd3049be
-size 74018
+oid sha256:10817f0f2ea42358bc62f69a09dfb8bd71701727df6d5a387bea742f3ea06417
+size 1030740

data/train.jsonl.gz → split/validation-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-oid sha256:757a0a73f1483f4b3f94783b774cdbf0831722a2b2c9abb5b820b4614ff6882a
-size 591930
+oid sha256:c70f0e660b5ebd1ea9a37d2a851f516f08a6d6477cdfc11be204e22a2f1102fd
+size 127466

data/data.jsonl.gz → unsplit/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-oid sha256:8944e6b35cb42294769ac30cf17bd006231545b2eeecfa59324246e192564d1f
-size 15388281
+oid sha256:ba60fe890562b2770967d63f9d7eb104691e028ca68716cd4e926996ecb31441
+size 26888538