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Datasets:
Jnx03
/
kanitakorn-v66-null-boundary-breadth

Tasks:
Text Generation
Languages:
Thai
English
Size:
n<1K
Tags:
Synthetic
thai
sft
kanitakorn
non-leaky
License:
Dataset card Data Studio Files
xet
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowNotImplementedError
Message:      Cannot write struct type 'withheld_metadata_values' with no child field to Parquet. Consider adding a dummy child field.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 771, in _write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 812, in _build_writer
                  self.pa_writer = pq.ParquetWriter(
                                   ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1070, in __init__
                  self.writer = _parquet.ParquetWriter(
                                ^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/_parquet.pyx", line 2363, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'withheld_metadata_values' with no child field to Parquet. Consider adding a dummy child field.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1361, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 940, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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analysis
dict
generated_at_ict
timestamp[s]
input_files
list
leakage_guardrails
dict
schema
string
warnings
list
{ "files": [ { "available_metadata_keys": [ "benchmark" ], "counts": { "accuracy_over_known": 0.65, "correct": 78, "invalid_prediction": 6, "invalid_rate": 0.05, "known_correctness": 120, "null_prediction": 6, "null_rate": 0.05, ...
2026-06-15T07:49:56
[ "campaign_20260613/evals/deepseek_v48_interp_v49b-c2_a0p90_top3_20260615_000247/thaiexam.json", "campaign_20260613/evals/deepseek_v53_a0p90_full_thaiexam565_20260615_021839/thaiexam.json" ]
{ "contains_benchmark_prompts": false, "contains_gold_or_target_answers": false, "contains_item_text": false, "contains_long_generated_outputs": false, "contains_model_samples": false, "item_level_outputs_limited_to_indices_when_enabled": true }
campaign_eval_error_analysis_v1
[ "input did not match any path: campaign_20260613/evals/deepseek_v60_format_null_upper12_checkpoint-1_top3_20260615_062702/thaiexam.json" ]

Kanitakorn v66 Null Boundary Breadth SFT

Training-ready JSONL for a small low-LR overlay in the Kanitakorn campaign. It is intended for a non-Thai-family base model path, currently the DeepSeek R1 Distill Qwen 14B branch initialized from the alpha 0.90 adapter parent.

Contents

  • train.jsonl: 444 SFT message rows.
  • manifest.json: provenance, hashes, counts, and recommended training gate.
  • README.md: this dataset card.

Row mix:

  • 420 original self-contained Thai MCQ reasoning rows.
  • 24 identity-attribution rows for kanitakorn, developed by Chawabhon Netisingha (ชวภณ เนตสิงหะ).

MCQ category mix:

  • Thai grammar / exact counting: 80
  • Negation and exception priority: 80
  • Original reading evidence: 80
  • Compact quantitative reasoning: 80
  • Self-contained civics/economics/environment/social concepts: 60
  • Final-answer/null guard: 40

MCQ answer labels are balanced: a/b/c/d/e = 84 each.

Provenance And Guardrails

The MCQ rows are deterministic local synthetic templates. They do not copy, translate, paraphrase, or reconstruct benchmark prompts, answer choices, gold answers, model outputs, or eval samples. Social/economics/civics/environment items define their needed facts inside the prompt.

Campaign constraints:

  • Real greedy single-model evaluation only.
  • No BoN.
  • No self-consistency.
  • No routing.
  • No ensemble.
  • No verifier selection.
  • No Thai-family base model assumption for final claimed model.

Audits

Completed before training:

  • Source/schema audit: 0 errors.
  • Reasoning-quality audit: 0 issues; MCQ answer-only fraction 0.0.
  • Raw MCQ audit: 0 issues; duplicate prompts 0.
  • Contamination scan: 0 issues over 9,735 loaded benchmark texts.

Train SHA256:

fcb5753b7aca2c825f1c93f015ec72da2bddabc929d24f96c72e1b95113a5295

Intended Training Gate

Recommended first run:

GPU=1 RUN_LABEL=upper12_lr7e8 LR=7e-8 MAX_STEPS=3 SFT_LAYERS_TO_TRANSFORM=last:12 \
  bash campaign_20260613/scripts/remote_train_eval_deepseek_v66_null_boundary_breadth_20260615.sh

Cut below ThaiExam-120 78/120. Promote only at >=80/120 with positive fixed-vs-lost item delta and no null regression.
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