---
language:
- ar
pretty_name: dclm-pro-arabic
size_categories:
- 10M<n<100M
task_categories:
- text-generation
tags:
- arabic
- translation
- pretraining
dataset_info:
  features:
  - name: doc_id
    dtype: int64
  - name: text
    dtype: string
  splits:
  - name: train
    num_examples: 33245503
---

# dclm-pro-arabic

Arabic translation of [DCLM-Pro](https://huggingface.co/datasets/gair-prox/DCLM-pro) (global shards 01 and 05), translated with [Seed-X-PPO-7B](https://huggingface.co/ByteDance-Seed/Seed-X-PPO-7B) using greedy decoding. Documents were split into ~490-token chunks at sentence boundaries, translated, and reassembled. Each row is one complete document. A companion corpus translated with the same pipeline is available at [fineweb-edu-arabic](https://huggingface.co/datasets/SultanR/fineweb-edu-arabic).

## Details

- Documents: 33,245,503 (22.7% of the two source shards, uniformly sampled)
- Arabic tokens: ~93B (Seed-X tokenizer)
- Shards: 381, zstd compressed, 83 GiB

| Column | Type | Description |
|--------|------|-------------|
| `doc_id` | int64 | Document index (not globally unique, see limitations) |
| `text` | string | Full Arabic translation |

## Results

We ran continued pretraining ablations with a 1.46B LLaMA model: 10B tokens on AraMix-HQ, then 20B tokens on the listed mix (30B total). We follow the [FineTasks](https://huggingface.co/spaces/HuggingFaceFW/blogpost-fine-tasks) evaluation format: scores are rescaled against a random baseline, then macro averaged over general knowledge (GK), reading comprehension (RC), reasoning (RES), and NLU. DCLM is dclm-pro-arabic, FWE is [fineweb-edu-arabic](https://huggingface.co/datasets/SultanR/fineweb-edu-arabic).

| Mix | GK | RC | RES | NLU | Aggregate |
|---|---:|---:|---:|---:|---:|
| 25% AraMix + 75% FWE | 0.1453 | 0.2006 | 0.1283 | 0.2417 | 0.1790 |
| 25% AraMix + 75% **DCLM** | 0.1569 | 0.1899 | 0.1171 | 0.2479 | 0.1779 |
| 33% AraMix + 33% **DCLM** + 33% FWE | 0.1565 | 0.1892 | 0.1091 | 0.2362 | 0.1727 |
| 75% **DCLM** + 25% FWE | 0.1494 | 0.1719 | 0.1113 | 0.2580 | 0.1726 |
| 100% **DCLM** | 0.1412 | 0.1877 | 0.1090 | 0.2492 | 0.1718 |
| 50% AraMix + 50% **DCLM** | 0.1496 | 0.1868 | 0.1133 | 0.2371 | 0.1717 |
| 50% AraMix + 50% FWE | 0.1475 | 0.1727 | 0.1176 | 0.2316 | 0.1673 |
| 50% **DCLM** + 50% FWE | 0.1440 | 0.1752 | 0.1033 | 0.2450 | 0.1669 |
| 100% FWE | 0.1426 | 0.1768 | 0.0993 | 0.2467 | 0.1664 |
| 100% AraMix-HQ (baseline) | 0.1282 | 0.1890 | 0.1048 | 0.2375 | 0.1649 |
| 75% AraMix + 25% **DCLM** | 0.1415 | 0.1794 | 0.1092 | 0.2267 | 0.1642 |
| 25% **DCLM** + 75% FWE | 0.1397 | 0.1707 | 0.0962 | 0.2456 | 0.1631 |
| 75% AraMix + 25% FWE | 0.1371 | 0.1811 | 0.1080 | 0.2259 | 0.1630 |

25% AraMix + 75% dclm-pro-arabic was the second best result across all 24 runs in the ablation, within 0.0011 of the best. Pure dclm-pro-arabic had the strongest GK and NLU of any single corpus. Small fractions of translated data underperform the baseline; it pays off when it dominates the mix.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("SultanR/dclm-pro-arabic", split="train", streaming=True)
```

## Limitations

- `doc_id` restarts at 0 for each of the 20 split workers, so values repeat across unrelated documents. Do not use it as a join or dedup key.
- The decode cap truncated ~8% of chunks, so some documents are missing tail content.
- No automatic quality filtering was applied to the translations.

## License

Refer to the original DCLM-Pro dataset for license information.