Updating documentation.

README.md

@@ -1,3 +1,197 @@
 ---
+pretty_name: Emotions
 license: cc-by-sa-4.0
+language:
+  - en
+size_categories:
+  - 10K<n<100K
+task_categories:
+  - text-classification
+task_ids:
+  - multi-class-classification
+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
+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
 ---
+
+# Dataset Card for "emotions"
+
+## Table of Contents
+
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Paper:** [CARER: Contextualized Affect Representations for Emotion Recognition](https://aclanthology.org/D18-1404/)
+- **Size of downloaded dataset files:** 16.13 MB
+- **Size of the generated dataset:** 47.62 MB
+- **Total amount of disk used:** 63.75 MB
+
+### Dataset Summary
+
+Emotions 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. Note that the paper does contain a larger data set with eight emotions being considered.
+
+## Dataset Structure
+
+### Data Instances
+
+An example bit of data looks like this:
+
+```
+{
+  "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 two configurations.
+
+- split: with a total of 20,000 examples split into train, validation and test.
+- 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 |
+
+## Additional Information
+
+### Licensing Information
+
+The dataset should be used for educational and research purposes only. It is licensed under Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).
+
+### 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.",
+}
+```