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README.md

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+---
+license: cc-by-4.0
+size_categories:
+- 10K<n<100K
+language:
+- ar
+task_categories:
+- multiple-choice
+pretty_name: AraSTEM (verified)
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: ids.csv
+---
+
+# AraSTEM (verified)
+
+A filtered version of **AraSTEM**, a native Arabic multiple-choice benchmark of STEM questions from school and college sources, with samples flagged by a multi-judge audit removed. The verified set has one config per level and subject (14 configs, `<level>_<subject>`).
+
+> **What "verified" means.** Samples were removed when all three LLM judges flagged a significant defect. This is not a human review, and only samples that baseline models answered incorrectly were audited, so some defects may remain in the kept data.
+
+**This repository does not contain the AraSTEM questions.** The source repository is public but states no license, so we publish the filter instead: which source rows to keep, plus a script that applies it to the source.
+
+## How to use
+
+```bash
+pip install pandas pyarrow huggingface_hub
+python apply_filter.py arastem_verified
+```
+
+[`apply_filter.py`](apply_filter.py) downloads the source file from GitHub at the commit below, checks every row against the fingerprints in `ids.csv`, splits it by `level` and `subject`, and writes the 10,494-row verified set as one parquet file per config (`arastem_verified/<level>_<subject>.parquet`, e.g. `primary_math.parquet`). It stops with an error if the source has changed.
+
+Or filter it yourself:
+
+```python
+import json, urllib.request
+import pandas as pd
+
+ids = pd.read_csv("hf://datasets/IFM/AraSTEM_verified/ids.csv")
+url = "https://raw.githubusercontent.com/AI-DSCHubAUB/AraSTEM-Dataset/d8f8fdd4e420df45737df1105a60a34b9b4aecd2/AraSTEMv5.json"
+source = pd.DataFrame(json.load(urllib.request.urlopen(url)))
+kept = ids["status"].eq("kept").to_numpy()
+verified = source[kept].reset_index(drop=True)  # 10,494 rows
+config = ids.loc[kept, "config"].reset_index(drop=True)
+primary_math = verified[config.eq("primary_math")].reset_index(drop=True)  # 2,774 rows
+```
+
+## Files
+- `ids.csv`: one row per source row (10,824 rows):
+  - `row`: the 0-based row index in the source file.
+  - `config`: `<level>_<subject>` from the source `level` and `subject` (spaces replaced by `_`, e.g. `college_information_technology`).
+  - `status`: `kept` (10,494) or `removed` (330).
+  - `fingerprint`: the first 16 hex characters of a SHA-256 over the row's `question`, `option_0`–`option_4`, `correct_option`, `level`, `subject` and `resource` (as text, joined by `\x1f`), used to detect changes in the source.
+- `apply_filter.py`: rebuilds the verified set from the source.
+
+## Source
+- Original dataset: GitHub [`AI-DSCHubAUB/AraSTEM-Dataset`](https://github.com/AI-DSCHubAUB/AraSTEM-Dataset), commit `d8f8fdd4e420df45737df1105a60a34b9b4aecd2`, file `AraSTEMv5.json` (10,824 questions). Splitting that file by `level` and `subject` reproduces the evaluation files exactly; each config is one such split after filtering.
+- Columns of the rebuilt set, as in the source: `question`, `option_0`–`option_4` (unused options are empty strings), `correct_option` (the 0-based index of the correct option, an integer), `level`, `subject` and `resource` (source reference).
+
+## Audit and filtering
+
+Samples were screened with the multi-judge audit described in [`IFM/discarded_samples`](https://huggingface.co/datasets/IFM/discarded_samples) (config `AraSTEM`). Three language-model judges (GPT-5.6 Sol, Gemini 3.8 Flash, Claude Opus 5) assessed candidate samples that strong baselines answered incorrectly. A sample is excluded only when **all three** judges report a *significant* issue (a problem with the question, the options or the gold label that prevents reliable answering or scoring). Each discarded record's `doc_id` is the 0-based position of the sample within its `<level>_<subject>` split of the source file; `ids.csv` gives the matching source row (`row`) for each excluded sample. All other rows are kept unchanged and in their original order.
+
+| Config | Original rows | Excluded | Kept |
+|---|---:|---:|---:|
+| `college_biology` | 588 | 21 | 567 |
+| `college_chemistry` | 326 | 34 | 292 |
+| `college_dentistry` | 632 | 29 | 603 |
+| `college_information_technology` | 369 | 38 | 331 |
+| `college_medicine` | 721 | 31 | 690 |
+| `college_pharmacy` | 423 | 15 | 408 |
+| `college_physics` | 137 | 4 | 133 |
+| `primary_math` | 2,822 | 48 | 2,774 |
+| `primary_science` | 3,868 | 66 | 3,802 |
+| `secondary_biology` | 322 | 9 | 313 |
+| `secondary_chemistry` | 237 | 9 | 228 |
+| `secondary_math` | 84 | 3 | 81 |
+| `secondary_physics` | 180 | 7 | 173 |
+| `secondary_science` | 115 | 16 | 99 |
+| **Total** | **10,824** | **330** | **10,494** |
+
+## License
+The source repository states no license, so its content remains all rights reserved by its authors, and this repository redistributes none of its text. The files here (`ids.csv`, `apply_filter.py` and this card) are released under CC BY 4.0. The verified set you build from the source remains subject to the source's terms.
+
+## Citation
+Please cite the original authors:
+
+```bibtex
+@article{mustapha2024arastem,
+  title   = {AraSTEM: A Native Arabic Multiple Choice Question Benchmark for Evaluating LLMs Knowledge In STEM Subjects},
+  author  = {Mustapha, Ahmad and Al-Khansa, Hadi and Al-Mubasher, Hadi and Mourad, Aya and Hamoud, Ranam and El-Husseini, Hasan and Al-Sakkaf, Marwah and Awad, Mariette},
+  journal = {arXiv preprint arXiv:2501.00559},
+  year    = {2024},
+  url     = {https://arxiv.org/abs/2501.00559}
+}
+
+@misc{jais3_2026,
+  title  = {Jais 3: Introducing Arabic-centric Agentic AI},
+  author = {{IFM Team}},
+  year   = {2026},
+  url    = {https://ifm.ai/blog/jais3/},
+}
+```

apply_filter.py

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+"""Rebuild the verified AraSTEM configs from the public source file on GitHub.
+
+Usage:
+    python apply_filter.py [output_dir]
+
+Writes one parquet file per config, <output_dir>/<level>_<subject>.parquet.
+"""
+import hashlib
+import json
+import os
+import sys
+import urllib.request
+
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+SOURCE = "AI-DSCHubAUB/AraSTEM-Dataset"
+SOURCE_REVISION = "d8f8fdd4e420df45737df1105a60a34b9b4aecd2"
+SOURCE_URL = f"https://raw.githubusercontent.com/{SOURCE}/{SOURCE_REVISION}/AraSTEMv5.json"
+THIS_REPO = "IFM/AraSTEM_verified"
+COLUMNS = ["question", "option_0", "option_1", "option_2", "option_3", "option_4",
+           "correct_option", "level", "subject", "resource"]
+EXPECTED_KEPT = {
+    "college_biology": 567, "college_chemistry": 292, "college_dentistry": 603,
+    "college_information_technology": 331, "college_medicine": 690, "college_pharmacy": 408,
+    "college_physics": 133, "primary_math": 2774, "primary_science": 3802,
+    "secondary_biology": 313, "secondary_chemistry": 228, "secondary_math": 81,
+    "secondary_physics": 173, "secondary_science": 99,
+}
+
+
+def fingerprint(row):
+    return hashlib.sha256("\x1f".join(str(row[c]) for c in COLUMNS).encode()).hexdigest()[:16]
+
+
+ids = pd.read_csv(hf_hub_download(THIS_REPO, "ids.csv", repo_type="dataset"))
+with urllib.request.urlopen(SOURCE_URL) as f:
+    source = pd.DataFrame(json.load(f), columns=COLUMNS)
+assert len(source) == len(ids), f"expected {len(ids)} source rows, got {len(source)}"
+mismatch = [i for i, row in source.iterrows() if fingerprint(row) != ids.at[i, "fingerprint"]]
+assert not mismatch, f"source rows differ from the audited version: {mismatch[:10]}"
+
+config = (source["level"] + "_" + source["subject"]).str.replace(" ", "_")
+assert config.equals(ids["config"]), "config labels differ from ids.csv"
+kept = ids["status"].eq("kept").to_numpy()
+
+out = sys.argv[1] if len(sys.argv) > 1 else "arastem_verified"
+os.makedirs(out, exist_ok=True)
+for name, expected in EXPECTED_KEPT.items():
+    verified = source[kept & config.eq(name).to_numpy()].reset_index(drop=True)
+    assert len(verified) == expected, f"{name}: expected {expected} rows, got {len(verified)}"
+    verified.to_parquet(os.path.join(out, f"{name}.parquet"))
+total = sum(EXPECTED_KEPT.values())
+assert total == int(kept.sum()) == 10494
+print(f"Wrote {total} rows in {len(EXPECTED_KEPT)} configs to {out}/")