datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
code_qa_10Kpython-code-instructions-85k-mypo-qaqc
joshuasundance/python-code-instructions-85k-mypo QA/QC artifact
This dataset repo is a QA/QC derivative generated by myponline.
What is included
Root-level train.parquet / validation.parquet / test.parquet with full QA/QC annotations.
filtered_basic/ with rows that pass structural QA/QC checks.
filtered_strict/ with rows whose chosen side passes structural QA/QC plus standalone ruff and mypy --strict.
summary.json with aggregate counts and provenance.… See the full description on the dataset page: https://huggingface.co/datasets/joshuasundance/python-code-instructions-85k-mypo-qaqc.CodeQA-datasetw_cad2-codeqa
Dataset Card
Dataset Description
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Dataset Structure
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Uses
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Limitations
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License
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Citation
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small_repos_multi_file_chatgpt_5_qas_part5_code_qa-datasetcodeqa_reduced
Dataset Card for "codeqa_final"
More Information needed
code_qa_updatedopc-annealing-corpus-synth-qa-code_python_js_tsrlm-codeqa-gpt-5-nano-20260226-001320
rlm-codeqa-gpt-5-nano-20260226-001320
RLM evaluation results for codeqa using gpt-5-nano.
Metrics
accuracy: 0.3333333333333333
correct: 1
total: 3
Configuration
Parameter
Value
Backend
openai
Max iterations
30
Seed
42
Num examples
3
Configs
Config
Description
results
Per-example evaluation results
rlm_call_traces
Per-iteration RLM call traces for the visualizer
codeqa-vagen-trajectoriescodeqa_v2
Dataset Card for "codeqa_v2"
More Information needed
codeqa-gt50-javasingle_file_code_qa_1k_repos-datasetcode-qa-6krlm-codeqa-gpt-5-nano-20260225-224658
rlm-codeqa-gpt-5-nano-20260225-224658
RLM evaluation results for codeqa using gpt-5-nano.
Metrics
accuracy: 0.3333333333333333
correct: 1
total: 3
Configuration
Parameter
Value
Backend
openai
Max iterations
30
Seed
42
Num examples
3
Traces
Per-iteration RLM call traces are available in a companion dataset:
reasoning-degeneration-dev/rlm-codeqa-gpt-5-nano-20260225-224658__rlm_call_traces
codeqa-agent-distill-260703-jasmall_repos_multi_file_chatgpt_5_qas_code_qa_1k-datasetcodeqa-agent-distill-260705-jasmall_repos_multi_file_chatgpt_5_qas_part4_code_qa-datasetrlm-codeqa-gpt-5-nano-20260225-224658__rlm_call_traces
rlm-codeqa-gpt-5-nano-20260225-224658__rlm_call_traces
Per-iteration RLM call traces for codeqa using gpt-5-nano.
These traces power the agg_visualizer and contain one row per RLM iteration.
Parent dataset
Results: reasoning-degeneration-dev/rlm-codeqa-gpt-5-nano-20260225-224658
Schema
Column
Description
example_idx
Index of the evaluation example
rlm_iter
RLM iteration number within this example
prompt
Serialised prompt for this iteration… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/rlm-codeqa-gpt-5-nano-20260225-224658__rlm_call_traces.all_1000_multifile_generated_qa_pairs_code_qa-datasetsmall_repos_multi_file_chatgpt_5_qas_part2_3_code_qa-datasetfiltered_repos2_clean_files_picked_gemini_flash_all_code_qa-datasetcodeqa-agent-distill-260709-jacodeqa-agent-distill-260704-jacode-switching-codesaviours-si26-qadeesanoorDataset Summary
This dataset contains Roman Urdu–English code-switched sentences, the way mixed-language text actually gets written in everyday Pakistani texting, tweeting, and casual conversation (e.g. "Aaj ka din bohot busy tha, had 3 meetings back to back"). Each sentence is broken down word-by-word, and every word is tagged with a language label. No existing Roman Urdu NLP resource handles this kind of within-sentence language mixing well — this dataset is a step toward building tools… See the full description on the dataset page: https://huggingface.co/datasets/qadeesanoor/code-switching-codesaviours-si26-qadeesanoor.codeqa-gt50-combinedMath-VR-QA-codecodeqa_v3
Dataset Card for "codeqa_v3"
More Information needed
Family-code-of-Ethiopia-QA-TEST