Update README.md

README.md

@@ -3,69 +3,28 @@ task_categories:
 - text-classification
 
 ---
-# AutoTrain Dataset for project: github-emotion-surprise
+# AutoTrain Dataset for project: github-emotion-love
 
 ## Dataset Description
 
 Dataset used in the paper: Imran et al., ["Data Augmentation for Improving Emotion Recognition in Software Engineering Communication"](https://arxiv.org/abs/2208.05573), ASE-2022.
 
-### Languages
-
-The BCP-47 code for the dataset's language is unk.
+This is an annotated dataset of 2000 GitHub comments. Six basic emotions are annotated. They are Anger, Love, Fear, Joy, Sadness and Surprise. This repository contains annotations of all emotions.
 
 ## Dataset Structure
 
-### Data Instances
-
-A sample from this dataset looks as follows:
-
-```json
-[
-  {
-    "feat_id": 704844644,
-    "text": "This change doesn't affect anything but makes the code more clear. If you look at the line about, `currentUrlTree` is set to `urlAfterRedirects`.",
-    "feat_Anger": 0,
-    "feat_Love": 0,
-    "feat_Fear": 0,
-    "feat_Joy": 1,
-    "feat_Sadness": 0,
-    "target": 0
-  },
-  {
-    "feat_id": 886568180,
-    "text": "Thanks very much for your feedback [USER] Your point is totally fair. My intention was to highlight that camelCase or dash-case class names are perfectly fine to use in Angular templates. Most people, especially beginners, do not know that and end up using the `ngClass` directive. Do you think that rewording the alert towards that direction would make sense?",
-    "feat_Anger": 0,
-    "feat_Love": 1,
-    "feat_Fear": 0,
-    "feat_Joy": 0,
-    "feat_Sadness": 0,
-    "target": 0
-  }
-]
-```
+Dataset is in CSV format. The columns are:
 
-### Dataset Fields
+```id, modified_comment, Anger, Love, Fear, Joy, Sadness, Surprise```
 
-The dataset has the following fields (also called "features"):
+Here, `id` is a unique id for each comment. Each emotion is marked as 1 or 0.
 
-```json
-{
-  "feat_id": "Value(dtype='int64', id=None)",
-  "text": "Value(dtype='string', id=None)",
-  "feat_Anger": "Value(dtype='int64', id=None)",
-  "feat_Love": "Value(dtype='int64', id=None)",
-  "feat_Fear": "Value(dtype='int64', id=None)",
-  "feat_Joy": "Value(dtype='int64', id=None)",
-  "feat_Sadness": "Value(dtype='int64', id=None)",
-  "target": "ClassLabel(num_classes=2, names=['0', '1'], id=None)"
-}
-```
 
 ### Dataset Splits
 
-This dataset is split into a train and validation split. The split sizes are as follow:
+This dataset is split into a train and test split. The split sizes are as follows:
 
 | Split name   | Num samples         |
 | ------------ | ------------------- |
 | train        | 1600 |
-| valid        | 400 |
+| test        | 400 |