---
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.