Upload FlutterFT 5,000-record training corpus

.gitattributes

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README.md

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+---
+language:
+  - en
+task_categories:
+  - text-generation
+  - code-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.

train.jsonl

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+oid sha256:f33f3026ee6dcf9561eedd495205ee396e9c0d634dbbbe466f38ea34e5f8a25e
+size 41374801