Update README.md

README.md

@@ -3,3 +3,70 @@ license: other
 license_name: psf2
 license_link: https://docs.python.org/3/license.html
 ---
+# Python Official Documentation Training Dataset
+
+An Apache Arrow formatted, tokenized dataset created directly from the official **Python Documentation (500+ pages)**. This dataset is optimized for training and fine-tuning language models on core Python concepts, standard library usage, syntax rules, and official programming guidelines.
+
+---
+
+## Dataset Overview
+
+* **Dataset Name:** `python-training-dataset`
+* **Source Material:** Official Python Documentation (500+ pages)
+* **Format:** Apache Arrow (`data-00000-of-00001.arrow`)
+
+
+* **License:** Python Software Foundation License (`psf2`)
+
+
+* **Primary Feature:** Pre-tokenized sequence arrays (`input_ids`)
+
+
+
+---
+
+## Dataset Structure
+
+### Data Schema
+
+The dataset contains pre-tokenized token ID lists designed for immediate ingestion into transformer-based neural network models:
+
+| Feature | Data Type | Description |
+| --- | --- | --- |
+| `input_ids` | `List(int32)` | Tokenized integer sequence representations derived from Python's official documentation
+
+ |
+
+---
+
+## Quickstart & Loading
+
+You can load this dataset directly using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load dataset from Hugging Face Hub
+dataset = load_dataset("JayeshSharma/python-training-dataset")
+
+# Inspect dataset structure
+print(dataset)
+
+# Access a single tokenized sequence
+sample = dataset["train"][0]
+print("Token IDs sample:", sample["input_ids"][:10])
+
+```
+
+---
+
+## Intended Use & Applications
+
+* **Language Model Pre-training & Fine-tuning:** Enhance LLMs with structured knowledge from official Python language references and standard library documentation.
+
+
+* **Code Understanding & Generation:** Train models to understand Python syntax, idiomatic code patterns, and core library implementations.
+
+> **Note:** Because the text is already converted to `input_ids`, pass these tokens through your corresponding tokenizer's `decode()` method if you need to recover the raw text representations.
+> 
+>