Code Question Answering Dataset With Multilingual Code Context

via “multi-language code representation and tokenization”

250GB curated code dataset for StarCoder training.

Unique: Explicitly supports 86 languages with language-aware metadata, enabling models to learn language-specific syntax and patterns. Preserves raw code rather than pre-tokenizing, allowing flexible tokenizer choices downstream.

vs others: Broader language coverage than CodeSearchNet (14 languages) and more flexible than pre-tokenized datasets like Codex, enabling researchers to experiment with different tokenization strategies and language-specific fine-tuning.

via “multi-language code generation from natural language prompts”

Meta's 70B specialized code generation model.

Unique: Trained on 1 trillion tokens of code data (10x more than typical LLMs) with explicit multi-language support across 15+ languages, enabling stronger cross-language idiom understanding than general-purpose models. The 100K context window (vs. 4-8K in most alternatives) enables repository-level code understanding and generation that respects project-wide patterns.

vs others: Outperforms GPT-3.5 and open-source alternatives on HumanEval (67.8%) and MBPP benchmarks due to code-specific pretraining, while remaining fully open-source and free for commercial use unlike Copilot or Claude.

via “multi-language code representation with language-specific tokenization”

783 GB curated code dataset from 86 languages with PII redaction.

Unique: Explicit language-specific representation across 86 languages with language-aware tokenization, rather than treating code as generic text — enables models to learn language idioms and syntax-specific patterns

vs others: More comprehensive language coverage (86 languages) than CodeSearchNet (~10 languages) and more language-aware than generic code datasets, improving multilingual code generation

via “code question-answering dataset with multilingual code context”

Dataset by NTU-NLP-sg. 6,65,024 downloads.

Unique: Combines code snippets with expert-generated question-answer pairs across multiple languages, enabling training of code understanding models through both extractive and abstractive QA formulations — integrates code comprehension with natural language generation in a multilingual context

vs others: Broader scope than CoQA (conversational QA on text) applied to code, and more multilingual than CodeQA which focuses primarily on Java and Python

via “code-problem contextual answering”