I have a question for anyone experienced with GPT or GPT2 models, specifically regarding Byte-Pair Encoding for text encoding. My main issue is figuring out how to generate my own vocab.bpe file.
I possess a Spanish text corpus that I intend to use for training a BPE encoder. I’ve successfully created the encoder.json file with the python-bpe library, but I’m struggling to create the vocab.bpe file. I’ve examined the encoder.py file from the GPT-2 repository but couldn’t find any guidance. Any assistance or suggestions would be greatly appreciated.
hey! i had the same issue a while back. you should try using learn_bpe from subword-nmt instead, it works like a charm. just run it on ur spanish corpus with the vocab size you need and itll generate the .bpe file for you. was perfect for my catalan stuff.
just use the tokenizers library from hugging face and train bpe from scratch on your spanish corpus. way simpler than recreating gpt-2’s exact format - you’ll get both the vocab and merges files automatically. i had the same headaches before switching to this approach.
Had this exact issue with Portuguese texts last year. The vocab.bpe file has the merge rules BPE learned during training - it’s separate from the token-to-ID mapping in encoder.json. I used the original BPE implementation from GPT-2’s codebase but changed the output format. You’ve got to extract the merge operations during training and save them how GPT wants - each line needs the byte pair being merged. The tricky bit is making sure the merge order matches what your encoder expects. Training with tiktoken gave me way better compatibility with modern GPT implementations, though you’ll need extra setup for Spanish preprocessing.
The vocab.bpe file is just the merge operations file from BPE training. You’ve got encoder.json from python-bpe, but you’re probably missing merges.txt (or something similar) with the byte pair merge rules. I ran into this same issue - Hugging Face tokenizers library makes this way easier than the original GPT-2 setup. Just train a BPE tokenizer directly on your Spanish corpus with their BPE trainer. It’ll spit out both vocab and merge files in the right format for GPT models. Main thing is making sure your merge operations file matches what GPT expects.
The mismatch between encoder.json and vocab.bpe tripped me up too. I ran into this working on a German corpus project. Here’s what I figured out: vocab.bpe contains the merge operations from BPE training, while encoder.json maps tokens to numerical IDs. The problem is python-bpe doesn’t export files in GPT-2’s format. I fixed this by capturing merge pairs during training and formatting them right - each line needs two subword units being merged. I wrote a quick script to convert python-bpe’s merge table into GPT-2’s vocab.bpe format. The tricky part is making sure your merge operations match up with encoding during inference.
There’s a cleaner way to handle BPE training without getting stuck on file formats.
I’ve hit similar tokenization issues with custom corpora. This problem’s super common - you solve one part then waste hours figuring out format requirements.
I’d build an automated workflow that handles the entire BPE process. Create a flow that takes your Spanish corpus, runs BPE training, and spits out all files in the right formats.
Once it’s set up, you can rerun it for new data or different vocab sizes. Easy to experiment with parameters without manually juggling intermediate files.
Used this approach on several NLP projects - saves tons of time vs wrestling with individual tools and conversions.
Latenode’s great for building these workflows. Chains processing steps together and handles file transformations automatically: https://latenode.com