Update data files

README.md

@@ -7,7 +7,7 @@ language_creators:
 language:
 - en
 license:
-- unknown
+- other
 multilinguality:
 - monolingual
 size_categories:
@@ -71,6 +71,7 @@ train-eval-index:
 tags:
 - emotion-classification
 dataset_info:
+- config_name: split
   features:
   - name: text
     dtype: string
@@ -78,24 +79,44 @@ dataset_info:
     dtype:
       class_label:
         names:
-          0: sadness
-          1: joy
-          2: love
-          3: anger
-          4: fear
-          5: surprise
+          '0': sadness
+          '1': joy
+          '2': love
+          '3': anger
+          '4': fear
+          '5': surprise
   splits:
   - name: train
-    num_bytes: 1741541
+    num_bytes: 1741597
     num_examples: 16000
   - name: validation
-    num_bytes: 214699
+    num_bytes: 214703
     num_examples: 2000
   - name: test
-    num_bytes: 217177
+    num_bytes: 217181
     num_examples: 2000
-  download_size: 2069616
-  dataset_size: 2173417
+  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
 ---
 
 # Dataset Card for "emotion"
@@ -150,49 +171,30 @@ Emotion is a dataset of English Twitter messages with six basic emotions: anger,
 
 ### Data Instances
 
-#### default
-
-- **Size of downloaded dataset files:** 1.97 MB
-- **Size of the generated dataset:** 2.07 MB
-- **Total amount of disk used:** 4.05 MB
-
-An example of 'train' looks as follows.
+An example looks as follows.
 ```
 {
-    "label": 0,
-    "text": "im feeling quite sad and sorry for myself but ill snap out of it soon"
+  "text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
+  "label": 0
 }
 ```
 
-#### emotion
-
-- **Size of downloaded dataset files:** 1.97 MB
-- **Size of the generated dataset:** 2.09 MB
-- **Total amount of disk used:** 4.06 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
 ### Data Fields
 
-The data fields are the same among all splits.
-
-#### default
+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).
 
-#### emotion
-- `text`: a `string` feature.
-- `label`: a `string` feature.
-
 ### Data Splits
 
-| name    | train | validation | test |
-| ------- | ----: | ---------: | ---: |
-| default | 16000 |       2000 | 2000 |
-| emotion | 16000 |       2000 | 2000 |
+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
 
@@ -246,10 +248,11 @@ The data fields are the same among all splits.
 
 ### Licensing Information
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+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",
@@ -268,10 +271,8 @@ The data fields are the same among all splits.
     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.
+Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

data/data.jsonl.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8944e6b35cb42294769ac30cf17bd006231545b2eeecfa59324246e192564d1f
+size 15388281

data/test.jsonl.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4524468d0b7ee8eab07a088216cde7f9278f1c574669504a805ed172df6dad75
+size 74935

data/train.jsonl.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:757a0a73f1483f4b3f94783b774cdbf0831722a2b2c9abb5b820b4614ff6882a
+size 591930

data/validation.jsonl.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50783464882f450f88e61ece964a200e492495eed1472ed520d013bbcd3049be
+size 74018

emotion.py

@@ -1,4 +1,4 @@
-import csv
+import json
 
 import datasets
 from datasets.tasks import TextClassification
@@ -27,14 +27,33 @@ _CITATION = """\
 _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.
 """
-_URL = "https://github.com/dair-ai/emotion_dataset"
-# use dl=1 to force browser to download data instead of displaying it
-_TRAIN_DOWNLOAD_URL = "https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1"
-_VALIDATION_DOWNLOAD_URL = "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1"
-_TEST_DOWNLOAD_URL = "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1"
+
+_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(
@@ -43,26 +62,27 @@ class Emotion(datasets.GeneratorBasedBuilder):
                 {"text": datasets.Value("string"), "label": datasets.ClassLabel(names=class_names)}
             ),
             supervised_keys=("text", "label"),
-            homepage=_URL,
+            homepage=_HOMEPAGE,
             citation=_CITATION,
+            license=_LICENSE,
             task_templates=[TextClassification(text_column="text", label_column="label")],
         )
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-        valid_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
-        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
+        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 csv_file:
-            csv_reader = csv.reader(csv_file, delimiter=";")
-            for id_, row in enumerate(csv_reader):
-                text, label = row
-                yield id_, {"text": text, "label": label}
+        with open(filepath, encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                example = json.loads(line)
+                yield idx, example