Add 'unsplit' config data files

README.md

@@ -64,10 +64,10 @@ 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:
@@ -78,6 +78,10 @@ configs:
   - 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

@@ -157,5 +157,58 @@
         "download_size": 1287193,
         "dataset_size": 2173401,
         "size_in_bytes": 3460594
+    },
+    "unsplit": {
+        "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": "unsplit",
+        "version": {
+            "version_str": "1.0.0",
+            "major": 1,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 45444017,
+                "num_examples": 416809,
+                "dataset_name": null
+            }
+        },
+        "download_size": 26888538,
+        "dataset_size": 45444017,
+        "size_in_bytes": 72332555
     }
 }

unsplit/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba60fe890562b2770967d63f9d7eb104691e028ca68716cd4e926996ecb31441
+size 26888538