| --- |
| language: |
| - en |
| task_categories: |
| - text-generation |
| size_categories: |
| - 1K<n<10K |
| license: mit |
| pretty_name: FlutterFT |
| --- |
| |
| # FlutterFT |
|
|
| FlutterFT is a 5,000-record English-language instruction-tuning corpus for |
| Flutter/Dart code generation, spanning seven task types: question-to-code, |
| test generation, bug fixing, refactoring, completion, code explanation, and |
| API usage. Each record is a chat-format (system/user/assistant) example. |
|
|
| ## Important notice on source provenance and licensing |
|
|
| **Source repository, commit, and license information for the underlying code |
| snippets was not recorded during data collection and has not been |
| verified per record.** The code snippets in this corpus were collected from |
| public GitHub repositories, but public visibility of a repository does not by |
| itself grant permission to redistribute its code under a new license. This |
| dataset's own packaging (this card, the JSONL structure, associated tooling) |
| is released under the MIT license, but that does not and cannot override the |
| license terms of the original source code, which are unknown here. |
|
|
| **Use this dataset at your own risk and responsibility.** If you are the |
| author of any code that appears in this corpus and object to its inclusion, |
| please open a discussion on this repository and it will be addressed. |
|
|
| ## Dataset structure |
|
|
| | Task type | Records | |
| | --- | ---: | |
| | question_to_code | 2,845 | |
| | code_to_explanation | 513 | |
| | test_generation | 513 | |
| | bug_fix | 394 | |
| | refactor | 393 | |
| | completion | 312 | |
| | api_usage | 30 | |
| |
| Each line in `train.jsonl` is a JSON object: |
| |
| ```json |
| { |
| "id": "seed_000001", |
| "messages": [ |
| {"role": "system", "content": "You are a Flutter and Dart coding assistant."}, |
| {"role": "user", "content": "..."}, |
| {"role": "assistant", "content": "..."} |
| ], |
| "task_type": "question_to_code", |
| "source": {}, |
| "metadata": {}, |
| "split": "train", |
| "split_origin": "..." |
| } |
| ``` |
| |
| ## Dataset creation |
|
|
| The pipeline started from 4,400 raw English question/code pairs, applied |
| deterministic quality rules plus full-context LLM-assisted review, derived |
| additional task variants (test generation, bug fixing, refactoring, |
| completion, explanation) from accepted roots, and checked for exact |
| duplicates and near-duplicate leakage against the held-out benchmark |
| (see the companion `flutterbench` dataset). |
|
|
| ## Known limitations |
|
|
| - Task distribution is dominated by `question_to_code` (~57%); `api_usage` is |
| a small subset (30 records). |
| - Quality varies: some records are large or contain formatting artifacts from |
| automated generation. |
| - Only one downstream model family (Qwen2.5-Coder-3B-Instruct) has been |
| evaluated against this data; results should not be assumed to generalize. |
| - No functional/compilation verification was performed on these training |
| examples themselves (functional verification exists for a subset of the |
| companion benchmark, see `flutterbench`). |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the repository. A predecessor study on |
| Turkish Flutter code generation is: |
|
|
| Uluırmak, B. A., & Kurban, R. (2025). Fine-tuning Large Language Models for |
| Turkish Flutter Code Generation. *Sakarya University Journal of Computer and |
| Information Sciences*, 8(4), 637-650. |
|
|
