Update README.md

README.md

@@ -1,45 +1,44 @@
----
 license: apache-2.0
 task_categories:
-- text-classification
-- token-classification
+  - text-classification
+  - token-classification
 language:
-- en
+  - en
 tags:
-- Science
-- glossary
-- glossaries
-- terms
-- recognition
-pretty_name: Science terms and phrases glossary
+  - science
+  - glossary
+  - terms
+  - entity-recognition
+  - text-simplification
+pretty_name: Science Terms and Phrases Glossary
 size_categories:
-- 10K<n<100K
+  - 10K<n<100K
 ---
 
-# Dataset Card for Dataset Name
-Science terms and phrases glossary
-
-## Dataset Details
-Contains terms and phrases for many subject of science glossaries.
-
-### Dataset Description
-This dataset was gathered by web scraping terms from sources like Wikipedia, NASA, and other scientific references. Some terms were also generated using ChatGPT-4.0.
-
-The dataset is designed for token classification, meaning it contains both scientific and non-scientific words (e.g., common words like "or," "and", and food and animals names).
-
-I trained a classification model on this dataset and found that it has a bias toward recognizing entitie's names as scientific terms. Therefore, for classification, it is recommended to use spaCy's entity recognition, such as:
-"spacy.load("en_core_web_sm")"
+# Dataset Card for Science Terms and Phrases Glossary
 
+## Dataset Details  
+This dataset contains scientific terms and phrases from various disciplines, compiled from multiple sources.
 
+### Dataset Description  
+The dataset was created by web scraping scientific glossaries from sources like Wikipedia, NASA, and other academic references. Additionally, some terms were generated using ChatGPT-4.0.  
 
-## Dataset Creation 
-Gathered by web scraping terms from sources like Wikipedia, NASA, and other scientific references. Some terms were also generated using ChatGPT-4.0.
+It is designed for **token classification**, meaning it includes both scientific and non-scientific words (e.g., common words like *"or"*, *"and"*, as well as names of foods and animals).  
 
-### Curation Rationale
-Simplify Science papers
+I trained a classification model on this dataset and found that it tends to misclassify named entities (such as people’s names) as scientific terms. Therefore, for better classification results, it is recommended to use **spaCy’s entity recognition**, such as:
 
+```python
+import spacy
+nlp = spacy.load("en_core_web_sm")
+```
+## Dataset Creation
+The dataset was compiled through a combination of:
 
+Web scraping from Wikipedia, NASA, and other scientific glossaries.
+AI-generated terms using ChatGPT-4.0.
 
-## Dataset Card Contact
+### Dataset Goal
+The primary objective of this dataset is to aid in the simplification of scientific papers by improving entity recognition and classification.
 
-For more information you can contact me via email: jonatco1@sce.ac.il
+## Contact Information
+For more details or inquiries, you can reach me via email: jonicohen97@gmail.com