Update README.md

README.md

@@ -7,6 +7,7 @@ pretty_name: SPECTER
 ---
 
 # Dataset Card for "ESPECTER"
+
 ## Table of Contents
 - [Dataset Description](#dataset-description)
   - [Dataset Summary](#dataset-summary)
@@ -30,7 +31,9 @@ pretty_name: SPECTER
   - [Licensing Information](#licensing-information)
   - [Citation Information](#citation-information)
   - [Contributions](#contributions)
+  
 ## Dataset Description
+
 - **Homepage:** [https://github.com/allenai/specter](https://github.com/allenai/specter)
 - **Repository:** [More Information Needed](https://github.com/allenai/specter/blob/master/README.md)
 - **Paper:** [More Information Needed](https://arxiv.org/pdf/2004.07180.pdf)
@@ -38,24 +41,39 @@ pretty_name: SPECTER
 - **Size of downloaded dataset files:**
 - **Size of the generated dataset:** 
 - **Total amount of disk used:** 38.3 MB
+
 ### Dataset Summary
-SPECTER: Document-level Representation Learning using Citation-informed Transformers. A new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning.
+
+SPECTER: Document-level Representation Learning using Citation-informed Transformers. 
+A new method to generate document-level embedding of scientific documents based on
+pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. 
+Unlike existing pretrained language models, SPECTER can be easily applied to 
+downstream applications without task-specific fine-tuning.
+
 ### Supported Tasks and Leaderboards
+
 [More Information Needed](https://github.com/allenai/specter)
+
 ### Languages
+
 [More Information Needed](https://github.com/allenai/specter)
+
 ## Dataset Structure
-Specter requires two main files as input to embed the document. A text file with ids of the documents you want to embed and a json metadata file consisting of the title and abstract information. Sample files are provided in the `data/` directory to get you started. Input data format is according to:
+Specter requires two main files as input to embed the document. 
+A text file with ids of the documents you want to embed and a json metadata file
+consisting of the title and abstract information. 
+Sample files are provided in the `data/` directory to get you started.
+Input data format is according to:
+
 metadata.json format:
 
+```
+
 {
     'doc_id': {'title': 'representation learning of scientific documents',
                'abstract': 'we propose a new model for representing abstracts'},
 }
-
-### Data Instances
-### Data Splits
-## Dataset Creation
+```
 
 ### Curation Rationale
 
@@ -119,6 +137,7 @@ metadata.json format:
   year={2020}
 }
 
+```
 SciDocs benchmark
 
 SciDocs evaluation framework consists of a suite of evaluation tasks designed for document-level tasks.
@@ -127,11 +146,7 @@ Link to SciDocs:
 
 - [https://github.com/allenai/scidocs](https://github.com/allenai/scidocs)
 
-```
-
 
 ### Contributions
 
 Thanks to [@armancohan](https://github.com/armancohan), [@sergeyf](https://github.com/sergeyf), [@haroldrubio](https://github.com/haroldrubio), [@jinamshah](https://github.com/jinamshah)
-
----