README.md

metadata

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 (global shards 01 and 05), translated with 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.

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 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.

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

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.