feat: publish Algomyth Code Generation Benchmark (1,000 algorithmic questions)

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README.md

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+---
+annotations_creators:
+- machine-generated
+language:
+- en
+license: mit
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- code-generation
+- python
+- algorithms
+- synthetic-benchmark
+- computer-science
+task_categories:
+- text-generation
+- text2text-generation
+pretty_name: Algomyth Code Generation Benchmark
+dataset_info:
+  features:
+  - name: Algorithm Name
+    dtype: string
+  - name: Algorithm Type
+    dtype: string
+  - name: Time Complexity
+    dtype: string
+  - name: Space Complexity
+    dtype: string
+  - name: Data Structure
+    dtype: string
+  - name: Algorithm Paradigm
+    dtype: string
+  - name: Question
+    dtype: string
+  - name: Input
+    dtype: string
+  - name: Expected Output
+    dtype: string
+  - name: Python Code Snippet
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1264865
+    num_examples: 2000
+---
+
+# Algomyth Code Generation Benchmark
+
+Part of the **Axiyon Intelligence / Producttolaunch** Research Ecosystem.  
+**Author:** Abdullah Ashraf ([@abdullahashraf122](https://huggingface.co/abdullahashraf122))  
+**GitHub Ecosystem:** [https://github.com/abdullah00ashraf](https://github.com/abdullah00ashraf)
+
+## Dataset Description
+Algomyth is an algorithmic evaluation and synthetic prompt benchmark designed for evaluating LLM code-generation accuracy across searching, sorting, and discrete mathematical algorithms. It features 1,000 standard algorithmic challenge formulations paired with 1,000 varied prompt phrasings, expected complexity boundaries, input test cases, and reference Python implementations.
+
+### Features
+* **Algorithm Name**: e.g. Linear Search, Binary Search
+* **Algorithm Type**: Category (Searching, Sorting, Graph, DP)
+* **Time & Space Complexity**: Theoretical Big-O bounds ($O(n)$, $O(1)$)
+* **Question / Prompt**: Natural language user query prompt variations
+* **Expected Output**: Ground-truth evaluation result
+* **Python Code Snippet**: Canonical reference implementation
+
+### Usage
+```python
+from datasets import load_dataset
+dataset = load_dataset("abdullahashraf122/algomyth-code-generation-benchmark")
+print(dataset["train"][0])
+```

data/linear_search_1000_questions.parquet

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data/linear_search_1000_varied_questions.parquet

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