Update README.md

README.md

@@ -1,8 +1,15 @@
 ---
 license: other
-license_name: psf2
-license_link: https://docs.python.org/3/license.html
+license_name: psf-2.0
+pretty_name: Python Documentation Training Dataset
+language:
+- en
+tags:
+- python
+- code
+- documentation
 ---
+
 # 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.
@@ -14,15 +21,9 @@ An Apache Arrow formatted, tokenized dataset created directly from the official
 * **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`)
-
-
+* **License:** Python Software Foundation License (`psf-2.0`)
 * **Primary Feature:** Pre-tokenized sequence arrays (`input_ids`)
 
-
-
 ---
 
 ## Dataset Structure
@@ -32,10 +33,8 @@ An Apache Arrow formatted, tokenized dataset created directly from the official
 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
-
- |
+| :--- | :--- | :--- |
+| `input_ids` | `List(int32)` | Tokenized integer sequence representations derived from Python's official documentation |
 
 ---
 
@@ -54,19 +53,4 @@ 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.
-> 
->
+print("Token IDs sample:", sample["input_ids"][:10])