Add Code4ML annotated subset with SonarQube and MLScent ratings

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LICENSE

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+Code4ML Annotated Subset
+Copyright (c) 2026 Dimas Maulana Ahmad
+
+This dataset compilation is licensed under the Creative Commons Attribution 4.0
+International License (CC-BY-4.0).
+Full text: https://creativecommons.org/licenses/by/4.0/legalcode
+
+It is derived from Code4ML (Drozdova et al., 2023; https://zenodo.org/records/7312803),
+which is also released under CC-BY-4.0.
+
+The `code_block` snippets are Kaggle notebook code, each published under the Apache
+License 2.0. See NOTICE.txt.

NOTICE.txt

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+This dataset contains code snippets from the Code4ML corpus
+(Drozdova et al., 2023; https://zenodo.org/records/7312803).
+
+Original notice from Code4ML:
+
+Notebooks for this dataset are taken from https://kaggle.com. Each notebook is published under a vanilla Apache 2.0 license and can be found by the related link from the kernels_meta.csv kernel_link field. Those are split into cells and stripped from redundant comments/markdown explanations. Python PEP8 code style fixes might also be enforced. No code-affecting changes to the notebook have been made.

README.md

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 ---
 license: cc-by-4.0
+pretty_name: Code4ML Annotated Subset with SonarQube and MLScent Ratings
+language:
+  - en
+tags:
+  - code
+  - python
+  - machine-learning
+  - code4ml
+  - kaggle
+  - code-quality
+  - static-analysis
+  - sonarqube
+task_categories:
+  - text-generation
+  - text-classification
+size_categories:
+  - 1K<n<10K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: code4ml_annotated.csv
 ---
+
+# Code4ML Annotated Subset with SonarQube and MLScent Ratings
+
+The 7,947 manually annotated Python snippets from the **Code4ML** corpus
+([Zenodo record 7312803](https://zenodo.org/records/7312803)), joined with their task-class
+names and annotated with structural code-quality ratings from **SonarQube** and **MLScent**.
+
+Code4ML contains 2,599,355 code cells from 23,103 Kaggle notebooks. Only this annotated
+subset carries a human-assigned task label, so it is the subset usable for building
+instruction–code pairs. This repository holds that subset in one file. Each snippet was scanned on its own, so
+the ratings describe the snippet, not its whole notebook.
+
+## Dataset summary
+
+| | |
+|---|---|
+| Records | 7,947 |
+| Source file | Code4ML `markup_data.csv`, labels from `vertices.csv` |
+| Task classes | 12 classes, 70 subclasses |
+| Format | single comma-delimited CSV with header |
+| Language | Python |
+
+## Columns
+
+| column | type | description |
+|---|---|---|
+| `id` | int | row index from Code4ML `markup_data.csv` |
+| `code_block` | string | the Python snippet (one notebook cell, may span multiple lines) |
+| `too_long` | string | `Yes`/`No` flag from the Code4ML annotators |
+| `marks` | int | annotator confidence, 1 (lowest) to 5 (highest) |
+| `graph_vertex_id` | int | task-label id from Code4ML `vertices.csv` |
+| `graph_vertex_class` | string | task class, e.g. `Data_Transform`, `Model_Train` |
+| `graph_vertex_subclass` | string | task subclass, e.g. `choose_model_class` |
+| `kernel_id` | int or empty | Kaggle notebook id, recovered by matching `code_block` text against Code4ML `code_blocks.csv`; empty when the text appears in more than one notebook or none |
+| `sqale_rating` | float | SonarQube SQALE rating (1.0 = A … 5.0 = E) |
+| `maintainability_rating` | string | SonarQube maintainability grade `A`–`E` |
+| `code_smells` | int | SonarQube code smells |
+| `complexity` | int | SonarQube cyclomatic complexity |
+| `cognitive_complexity` | int | SonarQube cognitive complexity |
+| `bugs` | int | SonarQube bugs |
+| `sqale_index` | int | SonarQube technical debt (minutes) |
+| `ncloc` | int or empty | non-comment lines of code; empty when SonarQube did not analyse the snippet |
+| `mlscent_analysis_status` | string | `ok`, `no_ml_import` (no ML library import in the snippet or its recovered notebook imports), or `invalid_python` |
+| `mlscent_smell_count` | int | number of MLScent findings |
+| `mlscent_unique_smell_count` | int | number of distinct MLScent smell types |
+| `mlscent_findings_json` | JSON string | all MLScent findings |
+| `mlscent_ml_rating` | string | `A`–`E` from ML-specific findings only, by severity; `N/A` when MLScent did not analyse the snippet |
+| `mlscent_ml_rating_basis` | string | findings and severities behind `mlscent_ml_rating` |
+| `mlscent_ml_findings_json` | JSON string | ML-specific findings used for the rating |
+| `mlscent_ignored_findings_json` | JSON string | finding types not used for the rating |
+| `context_line_count` | int | lines of recovered notebook ML imports prepended for MLScent analysis |
+| `combined_rating` | string | worse of `maintainability_rating` and `mlscent_ml_rating`; `N/A` when either is missing |
+
+## Statistics
+
+Task classes:
+
+| `graph_vertex_class` | Snippets |
+|---|---:|
+| Data_Transform | 2,387 |
+| Debug | 1,446 |
+| Visualization | 862 |
+| Model_Train | 816 |
+| Data_Extraction | 517 |
+| Environment | 441 |
+| Other | 372 |
+| Model_Evaluation | 354 |
+| Data_Export | 350 |
+| EDA | 231 |
+| Hyperparam_Tuning | 160 |
+| Model_Interpretation | 11 |
+
+Annotator confidence (`marks`):
+
+| 1 | 2 | 3 | 4 | 5 |
+|---:|---:|---:|---:|---:|
+| 44 | 210 | 903 | 1,419 | 5,371 |
+
+Other properties:
+
+| Property | Value |
+|---|---|
+| `too_long` = `Yes` | 1,360 |
+| Duplicate `code_block` values | 108 |
+| Lines per snippet (median / p75 / max) | 2 / 6 / 582 |
+
+Ratings:
+
+| Rating | `maintainability_rating` | `mlscent_ml_rating` | `combined_rating` |
+|---|---:|---:|---:|
+| A | 7,816 | 3,045 | 2,996 |
+| B | 84 | 952 | 962 |
+| C | 40 | 56 | 89 |
+| D | 7 | 111 | 117 |
+| E | 0 | 8 | 8 |
+| N/A | — | 3,775 | 3,775 |
+
+| Rating property | Value |
+|---|---|
+| `kernel_id` recovered | 4,373 |
+| `ncloc` empty (not analysed by SonarQube) | 1,003 |
+| `mlscent_analysis_status` = `ok` / `no_ml_import` / `invalid_python` | 4,172 / 3,551 / 224 |
+
+No rows were removed or modified; duplicates and short snippets are kept as in the source.
+
+## How the ratings were produced
+
+1. **SonarQube** Community Edition, default Python profile, through a FastAPI wrapper around
+   `sonar-scanner` (`/scan-batch`).
+2. **MLScent** (`ml-code-smell-detector==0.1.2`), with recovered notebook ML imports
+   prepended as context, through a local rating service (`/rate-batch`).
+3. The data was split into two halves, each rated by its own SonarQube + MLScent instance
+   pair, then merged back by `id`.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("dimsmauls/code4ml-annotated-rated", split="train")
+print(ds[0]["graph_vertex_class"], ds[0]["combined_rating"], ds[0]["code_block"])
+```
+
+## Provenance & attribution
+
+- **Source corpus:** Code4ML, [Zenodo record 7312803](https://zenodo.org/records/7312803),
+  released under CC-BY-4.0.
+- **Original code:** the snippets are Kaggle notebook cells, each published under the
+  **Apache-2.0** license. See `NOTICE.txt`.
+- **This derivative** joins `markup_data.csv` with the class names from `vertices.csv` and adds
+  SonarQube and MLScent rating columns. No code was changed.
+
+## Citation
+
+Please cite the original Code4ML work:
+
+```bibtex
+@article{drozdova2023code4ml,
+  title={Code4ML: a Large-scale Dataset of Annotated Machine Learning Code},
+  author={Drozdova, Anastasia and Trofimova, Ekaterina and Guseva, Polina and Scherbakova, Anna and Ustyuzhanin, Andrey},
+  journal={PeerJ Computer Science},
+  year={2023},
+  doi={10.7717/peerj-cs.1230}
+}
+```
+
+## License
+
+Released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/), the same license
+as Code4ML. The underlying `code_block` snippets are Kaggle notebook code under
+**Apache-2.0**; that attribution is preserved in `NOTICE.txt`.