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README.md

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+---
+license:
+- cc0-1.0
+converted_from: kaggle
+kaggle_id: timmayer/covid-news-articles-2020-2022
+---
+
+# Dataset Card for COVID News Articles (2020 - 2022)
+
+## 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)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://kaggle.com/datasets/timmayer/covid-news-articles-2020-2022
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The dataset encapsulates approximately half a million news articles collected over a period of 2 years during the Coronavirus pandemic onset and surge. It consists of 3 columns - **title**, **content** and **category**. **title** refers to the headline of the news article. **content** refers to the article in itself and **category** denotes the overall context of the news article at a high level. The dataset encapsulates approximately half a million news articles collected over a period of 2 years during the Coronavirus pandemic onset and surge. It consists of 3 columns - **title**, **content** and **category**. **title** refers to the headline of the news article. **content** refers to the article in itself and **category** denotes the overall context of the news article at a high level. 
+
+This dataset can be used to pre-train large language models (LLMs) and demonstrate NLP downstream tasks like binary/multi-class text classification. The dataset can be used to study the difference in behaviors of language models when there is a shift in data. For e.g., the classic transformers based BERT model was trained before the COVID era. By training a masked language model (MLM) using this dataset, we can try to differentiate the behaviors of the original BERT model vs the newly trained models.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+This dataset was shared by [@timmayer](https://kaggle.com/timmayer)
+
+### Licensing Information
+
+The license for this dataset is cc0-1.0
+
+### Citation Information
+
+```bibtex
+[More Information Needed]
+```
+
+### Contributions
+
+[More Information Needed]