We pretrain unidirectional language models on 4B tokens from UberText 2.0. We enrich document text with weakly structured metadata, such as title, tags, and publication year, enabling metadata-conditioned text generation and text-conditioned metadata prediction at the same time. We pretrain GPT-2 Small, Medium, and Large models on a single GPU, reporting training times, BPC on BrUK, BERTScore, and BLEURT on titles for 1000 News from the Future.

Install haloop to access the model: https://pypi.org/project/haloop/

See video on metadata pretraining (2m33s):

Model checkpoints are available at https://a.wilab.org.ua/gpt/. BLEURT/BERTscore evaluation on News from the Future is available on lang-uk/bleurt_eval

Next, we venture to formatting POS and NER datasets as instructions, and train low-rank attention adapters, performing these tasks as constrained text generation. See video (2m50s): https://www.youtube.com/watch?v=NDXJ9hXtf-o

See video on instruction finetuning (2m50s):

See POS and NER adapters can be trained using examples/Makefile.

This repository fuses karpathy/NanoGPT and asivokon/unlp-2023-shared-task

Authors:

• Volodymyr Kyrylov @proger
• Dmytro Chaplynskyi @dchaplinsky

When reporting BPC results in the UNLP paper, we make a mistake switching to the log-2 base. True measurements are larger by a factor of ~2.08. The correct measurements are reported in commit 1c5dc381. The updated table is available in the preprint https://wilab.org.ua/uk4b.pdf.