haloop

Haloop is a speech agent toolkit. Haloop provides:

• hai program to initialize models;
• hac program for acoustic model training;
• har for RNN language model training and evaluation;
• hal for causal attention model training;
• hat for agent testing;
• hap to score log probabilities of sentences under the GPT language model;
• haw to compare labels in datasets using word error rate;
• hax to compute correlations between datasets;

The package can be installed from PyPI:

pip install haloop

Pretrained models

hat can be used with Ukrainian GPT-2 models from our paper GPT-2 Metadata Pretraining Towards Instruction Finetuning for Ukrainian.

You will need to install and download:

pip install bitsandbytes sentencepiece

wget https://a.wilab.org.ua/gpt/wiki.model  # sentencepiece tokenizer
wget https://a.wilab.org.ua/gpt/ckpt10m.pt  # model checkpoint for GPT-2 Large

Now, kick off the REPL:

hat --spm wiki.model ckpt10m.pt

Score a list of sentences by computing log probabilities under the language model. First the input file will be sorted by token count to improve GPU utilization:

cat ubertext.wikipedia.filter_rus_gcld+short.text_only.txt | spm_encode --model wiki.model | awk -v OFS="\t" '{ print length, $0 }' | sort -r -n -s | cut -f2-  | spm_decode --model wiki.model > wikipedia.toksorted.txt
cat wikipedia.toksorted.txt | hap --compile --spm wiki.model ckpt10m.pt | pv -l > wikipedia.toksorted.scores.txt

Citing

Please cite:

@inproceedings{kyrylov-chaplynskyi-2023-gpt,
    title = "{GPT}-2 Metadata Pretraining Towards Instruction Finetuning for {U}krainian",
    author = "Kyrylov, Volodymyr  and
      Chaplynskyi, Dmytro",
    booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.unlp-1.4",
    pages = "32--39",
    abstract = "We explore pretraining unidirectional language models on 4B tokens from the largest curated corpus of Ukrainian, UberText 2.0. We enrich document text by surrounding it 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 each on single GPU, reporting training times, BPC on BrUK and BERTScore on titles for 1000 News from the Future. Next, we venture to formatting POS and NER datasets as instructions, and train low-rank attention adapters, performing these tasks as constrained text generation. We release our models for the community at https://github.com/proger/uk4b.",
}

Reading

Speech Discrimination by Dynamic Programming, T. K. Vintsyuk (1968)