Datasets:
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license: apache-2.0
task_categories:
- text-classification
tags:
- code
- language-identification
- qwen
pretty_name: Qwen Code Language-ID SFT Dataset
---
# Accuknoxtechnologies/CodeLanguage
SFT dataset for fine-tuning a Qwen-based guard that detects which programming languages appear in a user prompt. Each row pairs a natural-language + code prompt with a JSON `target` enumerating the detected languages.
This release combines the previously-separate train + test CSVs into a single `train` split (source files: `code_langid.csv`, `test_dataset_langid.csv`).
## Schema
| column | description |
|---|---|
| `prompt` | user message, possibly containing one or more code snippets |
| `target` | JSON: `{"is_valid": bool, "category": {"<Lang>": true, ...}}` |
| `kind` | one of `single`, `multi`, `benign` |
## Total Records
| rows | single | multi | benign | invalid (`is_valid=false`) |
|---:|---:|---:|---:|---:|
| 10500 | 7349 | 2101 | 1050 | 1050 |
## Supported Labels
25 programming languages:
`AWK`, `Bash`, `Batch`, `C`, `C#`, `C++`, `Dockerfile`, `Go`, `Java`, `JavaScript`, `Kotlin`, `Lua`, `Makefile`, `Perl`, `PowerShell`, `Python`, `R`, `Ruby`, `Rust`, `SQL`, `Scala`, `Swift`, `Terraform`, `YAML`, `jq`
| label | rows containing label |
|---|---:|
| `Go` | 508 |
| `PowerShell` | 505 |
| `Lua` | 504 |
| `Terraform` | 502 |
| `Rust` | 498 |
| `AWK` | 496 |
| `Kotlin` | 496 |
| `JavaScript` | 495 |
| `Batch` | 495 |
| `C` | 492 |
| `C++` | 491 |
| `Scala` | 486 |
| `Ruby` | 486 |
| `jq` | 480 |
| `R` | 479 |
| `Dockerfile` | 475 |
| `Swift` | 472 |
| `C#` | 469 |
| `Makefile` | 467 |
| `Bash` | 466 |
| `Perl` | 466 |
| `SQL` | 465 |
| `Python` | 462 |
| `Java` | 460 |
| `YAML` | 448 |
## Token-wise Bucket Split
Tokenized with `Qwen/Qwen2.5-0.5B` (matches the training tokenizer).
| 0-128 | 129-256 | 257-512 | 513-1024 | 1025-2048 | 2049+ | min | mean | p50 | p95 | max |
|---|---|---|---|---|---|---|---|---|---|---|
| 1529 | 1725 | 2398 | 2759 | 2060 | 29 | 23 | 581.8 | 449 | 1361 | 3035 |
## Languages (natural language of the prompts)
_Per-prompt natural-language detection (English/Korean/etc.) is not computed in this card revision. Prompts are predominantly English by construction (see `build_dataset.py`)._
## Reproduction
Generated by `gpu-vm-training-langid/build_dataset.py` and pushed by `gpu-vm-training-langid/hf_dataset_push/push_dataset.py`.
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