Skip to content

Latest commit

 

History

History
38 lines (34 loc) · 1.07 KB

README.md

File metadata and controls

38 lines (34 loc) · 1.07 KB

Transfer Learning

We take the model trained on Wikipedia data and continue training on HN data.

Steps

  • Apply wiki BPE to HN data
    for i in {dev,test,train}; do
      cut -f3 ../data/data.$i.tsv \
        | ~/src/hncynic/data/mosesdecoder/scripts/tokenizer/lowercase.perl \
        > $i.pp.titles
    done
    for i in {dev,test,train}; do
      cut -f4 ../data/data.$i.tsv | ~/src/hncynic/data/normalize_links.sh > $i.pp.comments
    done
    for i in {dev,test,train}; do
      for j in {comments,titles}; do
        ../data-wiki/fastBPE/fast applybpe \
          $i.pp.bpe.$j \
          $i.pp.$j \
          ../data-wiki/bpecodes
      done
    done
    
  • Vocabulary
    onmt-build-vocab --save_vocab vocab.titles train.pp.bpe.titles
    onmt-build-vocab --save_vocab vocab.comments train.pp.bpe.comments
    
  • Shuffle
    paste train.pp.bpe.{titles,comments} | shuf > train.pp.bpe.shuf.titles-comments
    cut -f1 < train.pp.bpe.shuf.titles-comments > train.pp.bpe.shuf.titles
    cut -f2 < train.pp.bpe.shuf.titles-comments > train.pp.bpe.shuf.comments
    

dev perplexity