Datasets:
CPT on 161B tokens of Ukrainian court decisions — interested in updates?
Hi Joel,
I'm Vladimir Kovtun, PhD student at the Institute of Cybernetics (NAS of Ukraine), working on domain-adapted LLMs for Ukrainian law.
We've just completed a data preparation pipeline for Continued Pretraining (CPT) of Qwen2.5 on the full Ukrainian court decision registry (EDRSR):
- Source: 38.5M court decisions → 33.9M after dedup + quality filtering
- Tokens: 161.42B (Qwen2.5 tokenizer, fertility 0.515 tok/char)
- Pipeline: 8-stage (export → clean → dedup MD5 → filter → structure → tokenize → package → S3), completed in ~3.5h on 208-core node
- Training data: 19.7M sequences × 8192, 1,232 shards, 601 GB
- Hardware: 8×H100 SXM 80GB (NVIDIA Innovation Lab grant)
- Training: currently running CPT on Qwen2.5-14B (baseline) and Qwen2.5-32B (main), DeepSpeed ZeRO-3
We plan to evaluate on LEXTREME (Ukrainian subset) as one of our primary benchmarks. Our earlier experiments with generic models topped out at ~48.5 on LEXTREME — we're expecting a significant jump with domain CPT.
Would you be interested in hearing about the results? Happy to share the evaluation numbers, the training curves, and potentially the model weights once training completes.
We also have several related papers in progress:
- Statute retrieval benchmark (arXiv: 2605.17639)
- Citation graph analysis of Ukrainian case law
- Temporal drift in legal language
Best regards,
Vladimir Kovtun
https://legal.org.ua
https://github.com/overthelex
Closing — will reach out directly instead.