Update README.md

README.md

@@ -1,23 +1,125 @@
 ---
-dataset_info:
-  features:
-  - name: SID
-    dtype: string
-  - name: TYPE
-    dtype: string
-  - name: SRC
-    dtype: string
-  - name: ASPECT
-    dtype: string
-  - name: SENTIMENT
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2034380
-    num_examples: 8818
-  download_size: 765164
-  dataset_size: 2034380
+annotations_creators:
+- expert-generated
+language:
+- ko
+language_creators:
+- expert-generated
+license: cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: KorFin-ABSA
+size_categories:
+- 1K<n<10K
+source_datasets:
+- klue
+tags:
+- sentiment analysis
+- aspect based sentiment analysis
+- finance
+task_categories:
+- text-classification
+task_ids:
+- topic-classification
+- sentiment-classification
 ---
-# Dataset Card for "korfin-asc"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# Dataset Card for KorFin-ABSA
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+### Dataset Summary
+
+The KorFin-ASC is an extension of KorFin-ABSA including 8818 samples with (aspect, polarity) pairs annotated. 
+The samples were collected from [KLUE-TC](https://klue-benchmark.com/tasks/66/overview/description) and 
+analyst reports from [Naver Finance](https://finance.naver.com). 
+Annotation of the dataset is described in the paper [Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance](https://arxiv.org/abs/2301.03136).
+
+
+### Supported Tasks and Leaderboards
+
+This dataset supports the following tasks:
+
+* Aspect-Based Sentiment Classification
+
+### Languages
+
+Korean
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance consists of a single sentence, aspect, and corresponding polarity (POSITIVE/NEGATIVE/NEUTRAL).
+
+```
+{
+  "title": "LGU＋ 1분기 영업익 1천706억원…마케팅 비용 감소",
+  "aspect": "LG U+",
+  'sentiment': 'NEUTRAL', 
+  'url': 'https://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=105&sid2=227&oid=001&aid=0008363739', 
+  'annotator_id': 'A_01', 
+  'Type': 'single'
+}
+
+```
+
+### Data Fields
+
+* title: 
+* aspect: 
+* sentiment: 
+* url: 
+* annotator_id: 
+* url: 
+
+
+### Data Splits
+
+The dataset currently does not contain standard data splits.
+
+## Additional Information
+
+You can download the data via:
+```
+from datasets import load_dataset
+dataset = load_dataset("amphora/KorFin-ASC")
+``` 
+Please find more information about the code and how the data was collected in the paper [Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance](https://arxiv.org/abs/2301.03136).
+The best-performing model on this dataset can be found at [link](https://huggingface.co/amphora/KorFinASC-XLM-RoBERTa).
+
+### Licensing Information
+
+KorFin-ASC is licensed under the terms of the [cc-by-sa-4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+
+### Citation Information
+
+Please cite this data using: 
+
+```
+@article{son2023removing,
+  title={Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance},
+  author={Son, Guijin and Lee, Hanwool and Kang, Nahyeon and Hahm, Moonjeong},
+  journal={arXiv preprint arXiv:2301.03136},
+  year={2023}
+}
+```
+
+### Contributions
+
+Thanks to [@Albertmade](https://github.com/h-albert-lee), [@amphora](https://github.com/guijinSON) for making this dataset.