Update README.md

README.md

@@ -1,8 +1,12 @@
 ---
 dataset_info:
   features:
+  - name: id
+    dtype: int64
   - name: user_name
     dtype: string
+  - name: image
+    dtype: image
   - name: post_message
     dtype: string
   - name: timestamp_post
@@ -15,10 +19,6 @@ dataset_info:
     dtype: string
   - name: label
     dtype: int64
-  - name: image
-    dtype: image
-  - name: id
-    dtype: int64
   splits:
   - name: train
     num_bytes: 662801454.408
@@ -31,3 +31,46 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# ReINTEL: A Multimodal Data Challenge for Responsible Information Identification on Social Network Sites
+
+We are running a competition based on this dataset at: https://aihub.ml/competitions/795. Top 3 solutions will be invited to submit technical report. The challenge will be concluded with a result paper, to be included in the proceeding of the [Reliable AI workshop at ACML](https://workshop2024.reliable-ai.org/).
+
+
+Please check our paper for more details: https://aclanthology.org/2020.vlsp-1.16.pdf
+>This paper reports on the ReINTEL Shared Task for Responsible Information Identification on social network sites, which is hosted at the seventh annual workshop on Vietnamese Language and Speech Processing (VLSP 2020). Given a piece of news with respective textual, visual content and metadata, participants are required to classify whether the news is `reliable' or `unreliable'. In order to generate a fair benchmark, we introduce a novel human-annotated dataset of over 10,000 news collected from a social network in Vietnam. All models will be evaluated in terms of AUC-ROC score, a typical evaluation metric for classification. The competition was run on the Codalab platform. Within two months, the challenge has attracted over 60 participants and recorded nearly 1,000 submission entries.
+
+## Load Dataset
+
+Load the dataset using the ```datasets``` library in ```python```:
+
+```
+from datasets import load_dataset
+
+train_dataset = load_dataset("ReliableAI/ReINTEL", split="train") # with ground-truth labels
+test_dataset = load_dataset("ReliableAI/ReINTEL", split="test") # without ground-truth labels
+```
+
+## Data Format
+
+Each instance includes 6 main attributes with/without a binary target label as follows:
+
+id: unique id for a news post on SNSs
+
+user_name: the anonymized id of the owner
+
+post_message: the text content of the news 
+
+timestamp_post: the time when the news is posted 
+
+image: image of type ```PIL.JpegImagePlugin.JpegImageFile``` associated with the news
+
+num_like_post: the number of likes that the news is received
+
+num_comment_post: the number of comment that the news is received
+
+num_share_post: the number of shares that the news is received
+
+label: a manually annotated label which marks the news as potentially unreliable
+- 1: unreliable
+- 0: reliable