| --- |
| language: |
| - en |
| license: apache-2.0 |
| task_categories: |
| - text-classification |
| tags: |
| - license-detection |
| - fossology |
| - nirjas |
| - binary-classification |
| - code |
| - legal |
| pretty_name: Nirjas Gate Dataset |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Nirjas Gate Dataset |
|
|
| Binary classification dataset for the **Nirjas gate** — a recall-first classifier that answers: |
| *"Is this text actual license text worth routing to Atarashi for identification?"* |
|
|
| This is a narrow gate, not a broad "license-related" detector. Its job is to route actual license |
| bodies (MIT text, GPL headers, Apache notices) to Atarashi while correctly rejecting license |
| references ("see LICENSE file"), copyright notices, code comments, and package metadata. |
|
|
| ## Dataset Summary |
|
|
| | split | total | license_related | not_license_related | |
| |---|---|---|---| |
| | train | 55,610 | 30,232 | 25,378 | |
| | validation | 6,877 | 3,707 | 3,170 | |
| | test | 6,895 | 3,770 | 3,125 | |
| |
| Splits are **content-hash frozen** — each text always lands in the same split regardless of |
| dataset rebuilds, so benchmark deltas are real and duplicates cannot leak across train/test. |
| |
| ## Dataset Structure |
| |
| ### Columns |
| |
| - `text` — the text fragment (license body, code comment, legal notice, etc.) |
| - `label` — `0 = license_related`, `1 = not_license_related` (ClassLabel) |
| - `source` — origin: `scancode`, `fossology`, `generated`, `synthetic`, `shortform`, `augmented`, `code_corpus` |
| - `negative_type` — for negatives: `generic_code_comment`, `same_register_notice`, `license_discussion`, `copyright_discussion`, `todo_fixme`, `commented_code` |
|
|
| ### Label Convention |
|
|
| | label | int | meaning | |
| |---|---|---| |
| | `license_related` | 0 | actual license text — route to Atarashi | |
| | `not_license_related` | 1 | everything else — discard | |
|
|
| ### Positive sources (train) |
|
|
| | source | count | |
| |---|---| |
| | scancode (ScanCode LicenseDB) | 18,931 | |
| | shortform (SPDX identifiers + snippets) | 4,820 | |
| | synthetic/rare (LLM-augmented) | 4,262 | |
| | fossology (licenseRef.json) | 1,681 | |
| | augmented | 538 | |
|
|
| ### Negative types (train) |
|
|
| | type | count | description | |
| |---|---|---| |
| | `generic_code_comment` | 6,324 | real source comments from the-stack-smol | |
| | `same_register_notice` | 5,177 | boilerplate legal-register non-licenses | |
| | `todo_fixme` | 4,264 | TODO/FIXME/HACK comments | |
| | `license_discussion` | 4,232 | commentary about licenses, not license text | |
| | `copyright_discussion` | 2,769 | copyright attribution, not license grants | |
| | `commented_code` | 2,612 | commented-out source code | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("rycerzes/nirjas-dataset") |
| train = ds["train"] |
| ``` |
|
|
| ## Deployed Gate |
|
|
| The deployed model is `potion-base-32M` (`StaticModelForClassification`) at threshold **0.20** |
| (recall-first, NOT argmax). At this threshold: |
|
|
| | metric | synthetic test set | real corpus (the-stack-smol + ScanCode, n=600) | |
| |---|---|---| |
| | License recall | 0.9952 | 1.0000 | |
| | FPR | 0.0227 | 0.0133 | |
|
|
| ## Generation Pipeline |
|
|
| Built by [`minerva-dataset-pipeline`](https://github.com/fossology/Minerva-Dataset-Generation/pull/6): |
|
|
| 1. Fetch license texts from ScanCode LicenseDB + FOSSology licenseRef.json |
| 2. Sliding-window split with junk-fragment filtering |
| 3. LLM-assisted augmentation for rare licenses |
| 4. Hard negative generation (LLM + template-based) |
| 5. Content-hash frozen train/val/test split |
| 6. Class balancing |
|
|
| ## Downstream Task |
|
|
| Part of the [FOSSology Nirjas](https://github.com/fossology/Nirjas) ML pipeline: |
|
|
| ``` |
| source file → Nirjas gate → [license_related] → Atarashi (which license?) |
| ``` |
|
|
