| --- |
| license: apache-2.0 |
| language: |
| - en |
| task_categories: |
| - text-generation |
| configs: |
| - config_name: questions |
| default: true |
| data_files: |
| - split: train |
| path: data/questions/train.parquet |
| - config_name: with_answers |
| data_files: |
| - split: train |
| path: data/with_answers/train.parquet |
| --- |
| |
| # OpenThoughts3 processed questions |
|
|
| This dataset extracts the first `conversations[0].value` from each row of [OpenThoughts3-1.2M](https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M), deduplicates questions by their original text, and filters lexical overlaps with the benchmarks below. The original `difficulty`, `source`, and `domain` fields are retained from the first occurrence. Each `conversations` array contains only the original question dictionary; generated responses have been removed. |
|
|
| | Configuration | Columns | Rows | |
| | --- | --- | ---: | |
| | `questions` | `difficulty`, `source`, `domain`, `conversations` | 65,064 | |
| | `with_answers` | the four columns above plus `answer`, `answer_method`, `answer_confidence`, `response_count`, `answer_support` | 65,064 | |
|
|
| Domain counts in both configurations: **math 53,100**, **code 5,714**, **science 6,250**. These are measured counts after filtering; the code count is substantially below 16,000. |
|
|
| ## Processing |
|
|
| - Source: all 120 train shards (1,200,000 rows) at revision `61bcf9d4eb38b30295efc2021227a63cc5bb34c8`. |
| - Exact duplicate questions removed: 1,134,911 rows. Benchmark overlapping unique questions removed: 25. Final unique questions: 65,064. |
| - Benchmarks: [MATH-500](https://huggingface.co/datasets/HuggingFaceH4/MATH-500) (500), [AIME 2025](https://huggingface.co/datasets/MathArena/aime_2025) (30), [AIME 2026](https://huggingface.co/datasets/MathArena/aime_2026) (30), [HMMT February 2025](https://huggingface.co/datasets/MathArena/hmmt_feb_2025) (30), [HMMT February 2026](https://huggingface.co/datasets/MathArena/hmmt_feb_2026) (33), [IMO-AnswerBench v2](https://github.com/google-deepmind/superhuman/blob/main/imobench/answerbench_v2.csv) (400), [LiveCodeBench `release_v6`](https://huggingface.co/datasets/livecodebench/code_generation_lite) (1,055 cumulative problems from `test.jsonl` through `test6.jsonl`), and [OJBench prompts](https://huggingface.co/datasets/He-Ren/OJBench_testdata) (464 English/Chinese prompts). |
| - Contamination matching uses Unicode NFKC, case folding, alphanumeric normalization, exact normalized text, and word-anchor candidate retrieval followed by containment or fuzzy similarity. All 25 removed questions matched MATH-500. This is a lexical screen and cannot certify the absence of semantic paraphrases. |
|
|
| ## Extracted answers |
|
|
| The `with_answers` configuration mines only completed final sections after `</think>` from the original generated responses. For math, it uses final `\boxed{...}` or an explicit final-answer label and selects the most frequent extracted answer across responses; ties become `null`. For code, it takes a final fenced code block. For science, it retains a completed final response as the answer text. Questions with no explicit completed answer retain `null`. |
|
|
| | Domain | Extracted answer | `null` | |
| | --- | ---: | ---: | |
| | Math | 29,840 | 23,260 | |
| | Code | 4,464 | 1,250 | |
| | Science | 6,066 | 184 | |
|
|
| `answer_confidence` describes **extraction evidence**, not correctness: `high` means at least two math responses agree on the selected text, `medium` means a single explicit math answer or a completed code/science answer was extracted, and `none` means `answer` is null. `response_count` counts source assistant responses for the question; `answer_support` counts responses supporting a selected math answer (and is 1 for extracted code/science answers). Generated answers have **not** been independently checked against ground truth or executed as code. Treat them as answer candidates. |
|
|
| ## Load |
|
|
| ```python |
| from datasets import load_dataset |
| |
| questions = load_dataset("ChuGyouk/OpenThoughts3-processed", "questions", split="train") |
| with_answers = load_dataset("ChuGyouk/OpenThoughts3-processed", "with_answers", split="train") |
| ``` |
|
|
| See `processing_report.json` for counts and `process.py` in this repository for the extraction rules. |
|
|
