Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-classification
+- text-generation
+- sentence-similarity
+language:
+- en
+tags:
+- code
+pretty_name: gfg
+size_categories:
+- 1K<n<10K
+---
+
+### Dataset Summary
+
+The `geeksforgeeks` dataset is a curated collection of **9,438 educational articles** scraped from [GeeksforGeeks](https://www.geeksforgeeks.org), a widely-used platform for learning programming, computer science, and data science topics. Each entry includes metadata such as publication date, title, full text, topic, URL, and precomputed vector embeddings **(OpenAI's text-embedding-3-large)**.
+
+
+---
+
+### Dataset Structure
+
+Each entry includes the following fields:
+
+| Field Name       | Type        | Description                                                                 |
+|------------------|-------------|-----------------------------------------------------------------------------|
+| `date`           | string      | Date the article was published (format: DD MMM, YYYY).                      |
+| `text`           | string      | Full article content.                                                       |
+| `topic`          | string      | Specific topic/category of the article.                                     |
+| `url`            | string      | Original URL of the article.                                                |
+| `root_topic`     | string      | Higher-level umbrella topic (e.g., "Data Science & ML").                    |
+| `heading`        | string      | Title of the article.                                                       |
+| `related_topics` | list[string]| Additional relevant tags/topics.                                            |
+| `author`         | string      | Article author (It's just Geeks for Geeks).                                 |
+| `vector`         | list[float] | Vector embedding (OpenAI's text-embedding-3-large)                          |
+
+---
+
+### Dataset Stats
+
+- **9,438** unique articles
+- **9,399** distinct vector embeddings (some rows may be missing embeddings)
+- **63** root topic categories
+
+This dataset is useful for:
+- Text embedding search
+- Educational content recommendation
+- Topic modeling
+- Sentence similarity training
+- LLM evaluation datasets
+- Knowledge graph construction
+---
+
+### Data Collection
+
+Articles were collected using a hybrid scraping approach via **Selenium** and **BeautifulSoup**, capturing dynamic and static content across GeeksforGeeks’. Only publicly accessible, educational content was included.
+
+---
+
+
+### Intended Uses
+
+This dataset is released without a specific target application. Researchers and developers may find it valuable for:
+- Educational NLP tasks
+- Search engine benchmarking
+- Semantic similarity experiments
+- Dataset distillation
+- LLM performance evaluation
+
+---
+
+### License
+
+No explicit license has been assigned to this dataset. Users are encouraged to **use it for educational and research purposes only** and **respect the rights of the original content owners (GeeksforGeeks.org)**.
+
+---