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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.
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